As security operations teams now use large language models (LLMs) and autonomous AI agents into their daily work, a new frontier is emerging: attackers deliberately manipulating AI agents. Prompt injection attacks—where an attacker hides malicious instructions that cause an AI agent to ignore its safety rules—pose a serious risk to enterprises. These attacks continue to grow in size and scale.  

Snyk’s security audit of the Agent Skills ecosystem, which includes Anthropic’s Claude, Vercel, and others, that 36% of all skills contained at least one critical-level security issue, including malware distribution, prompt injection attacks, and exposed secrets.

In June, researchers at Mozilla tested a prompt injection attack on Claude using indirect prompt injection—a technique that embeds malicious instructions in external content the AI agent processes. In this proof-of-concept, attackers took over developers’ systems by hiding indirect prompts in normal-looking repositories. When Claude Code executed them, the agent spawned a reverse shell.

AI agents often connect to more sensitive data than human employees do., A successful prompt injection can lead to catastrophic data loss or unauthorized system actions. Defending against prompt injection attacks requires multiple layers of protection. Security teams must monitor agent behavior for anomalies and prepare for agent containment, forensic preservation, and system remediation. Because AI agents execute tasks at machine speed, human responses must be able to match that pace.

The architecture of trust: Protocols and no “black box”

AI-native workflows need governed access rather than “black-box” autonomy. Modern governance frameworks use standardized protocols like the Model Context Protocol (MCP) to provide secure communication between AI clients and data sources. Visibility and transparency in agentic AI workflows matter, especially in cybersecurity. Autonomous agents perform complex tool executions and use independent logic, so they must show how they reached their decisions to meet regulatory requirements. Agents without transparency post serious risks: obscured reasoning can trigger unpredictable tool interactions, bypass governance controls, and create uncontrolled defensive gaps.

Implementing these protocols matters:

• Bounded Tenant Awareness: In a stable agentic AI architecture, multi-tenancy scales well. But if an AI tenant misbehaves, the entire system can fail. Bounded tenant awareness isolates any misbehaving AI agent to prevent cross-tenant contamination or data leakage.
• Strict Access Controls: By controlling connections to the platform, organizations can stop “ignore previous instructions” style bypasses. Maintain tight control over what the AI can see and do within a workflow.
• Standardized Telemetry: All telemetry must remain consistent and audit-ready. Even if an AI interaction is attempts to break rules, the underlying data movement gets tracked against established frameworks like MITRE ATT&CK and NIST.

Detecting the aftermath: UEBA and NDR as safeguards

A robust, unified SecOps platform can detect anomalous behavior even after prompt injection tricks an AI agent. Prompt injections often serve to steal credentials theft or extract data. When detected it’s important to act quickly. In agentic AI systems, misbehavior can escalate privileges, manipulate memory layers, create unauthorized identities, or alter shared reasoning components. Containment must be automatic and enforced at identity, authentication, and authorization layers.

These safeguards include:

• User and Entity Behavioral Analytics (UEBA): Identity-focused correlation and behavioral baselines to identify anomalous user activity or privilege escalation. If a compromised AI agent acts outside of its normal operational parameters, UEBA flags it in real-time and alerts a human security analyst.
• Network Detection and Response (NDR): Combining network traffic analytics with endpoint and cloud telemetry, NDR can identify data exfiltration or policy violations from a successful prompt injection.
• Multi-Layer AI Filtering: AI filters reduce raw alerts into high-fidelity incidents, cutting noise by up to 90%. This keeps the signals of an AI-driven attack from disappearing in a busy SOC.

Humans remain the strongest defense against AI agent social engineering. The human security analyst is still the one who makes the final decision. While AI handles triage and correlation, humans retain final control over response actions.

Moving beyond reactive guardrails

The traditional SOC model was never designed to handle machine-speed, AI-driven attacks. A human-augmented autonomous SOC approach moves from reactive alert handling to a proactive, verdict-first model. By combining a transparent, governed AI access with robust UEBA and NDR, organizations keep the SOC secure, transparent, and resilient as social engineering methods target machines.

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As AI-generated code continues to be injected into all corners of the internet, concerns have risen about an expanding attack surface for malicious hackers to exploit.

Some have argued that the enhanced cybersecurity capabilities of large language models could serve as a check, finding and fixing vulnerabilities nearly as fast as they’re created.

But new research that tested the patching capabilities of two popular commercial models, OpenAI’s ChatGPT 5.5 and Anthropic’s Claude Opus 4.8, found that generative AI is more likely to create an exploitable patch or introduce entirely new bugs than close off a vulnerability.

Researchers at 1Password tested the models ability to patch six “high-impact, high-complexity” CVEs, including the “Copy Fail” vulnerability, a kernel flaw that can give an attacker root access to Linux cloud environments. The overall success rate (or fully patching the vulnerability without introducing new problems), was less than a coin flip at 47%.

“Our research findings show that, in aggregate across a variety of scenarios, both Claude and ChatGPT had a low rate of successful patch generation, which we define as full remediation of all known exploit paths with no erroneous changes to application behavior,” wrote Keith Hoodlet, Axel Mierczuk and Spencer Michaels.

“The models often addressed only a subset of vulnerable code paths, added fragile guard code that satisfied tests while failing to address the vulnerability’s root cause, and sometimes introduced subtle changes in the application’s behavior while patching the immediate vulnerability,” the authors continued.

The research suggests that largely autonomous vulnerability-discovery and patching may not yet be effective in fixing the explosion of vulnerable code that is being created in the AI era.

Other private sector research has pointed to a similar problem. A report this year from Veracode found that while LLMs have made “enormous strides” in crafting workable code, “security is a different story.” Testing across a range of frontier models found the average security “pass rate” for AI generated code is around 56%. Newer models like GPT 5.5 push closer to 70%, while more than half sit between 50-53%.

Veracode tested 100 different models and while there was variability, in general a small number of models were showing progress on security patching while the rest have experienced “stagnation.” Similar to the 1Password research, in 44% of Veracode tests the models introduced a detectable OWASP Top 10 vulnerability into the codebase.

An important caveat: neither report tested newer models, like Anthropic’s Mythos or OpenAI’s GPT-5.6-Sol, that frontier companies tout as having significantly higher cybersecurity capabilities.

Those advanced models can identify and fix vulnerable code. Anthropic and OpenAI are distributing them to key industries through Project Glasswing and Daybreak before foreign or open-source alternatives can compete.

Tim Jarret, vice president of product at Veracode, told CyberScoop that AI tools are still subject to a range of limitations that can make them unreliable for cybersecurity patching without knowledgeable humans in the loop.

While some vulnerabilities – like SQL injections – can be easily patched through automation, other bugs like cross-site scripting, can be exploitable in several different ways and require either a human touch, additional context or both to fully close off. Additionally, models can slowly lose context from prior sessions over time, affecting their ability to complete tasks correctly and raising the possibility they’ll hallucinate to fill in the missing gaps.

“I think we would say, at this point, that Iits premature to treat those as anything other than another code change to the code base that needs to be reviewed and accepted by the team, as opposed to letting the agent merge the code freely,” said Jarrett.

However, he acknowledged that may not be possible in a world where AI agents are generating exponentially more code for human defenders to review. Some kind of automated code review will be necessary – preferably not by the same automation tool that produced the code. The ultimate goal is the same as it has always been in security: “trust but verify.”

“Ninety percent of the time, the human check might just be ‘did the cross check look good?’ Do we have a thumbs up?’” Jarrett said. “In those cases where there’s still something wrong, that’s where you focus your attention a little bit more.”

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The UK’s AI Security Institute said that their AI research system took “unsanctioned” actions over the internet to engage in “sustained, potentially harmful activity directed at real people.”

According to a blog post and technical report released Tuesday, the organization noticed “unusual data transfers” leaving their research systems through the pseudo-anonymous Tor network on July 28. The models, which included Anthropic’s Mythos 5 and OpenAI’s GPT-5.6-Sol models, were being tested for their cybersecurity capabilities.

Like the OpenAI and Anthropic incidents before it, the offending agents were attempting to solve a challenge. On 10 of 122 runs taken by two models, they took a combined 19 malicious actions, including attempting to insert malicious code into a real open-source software project and creating fake online identities to contact human maintainers and press them to approve it.

It also inserted malicious prompt injection instructions in places “where it reasoned that other automated AI systems might pick them up and execute them.” Sometimes multiple agents would collaborate on a task, with one model leaving public messages or notes for the other on GitHub.

However, AISI said their incident differs in one critical way.

“Importantly, this was not a case of a model escaping its secure test environment, or ‘sandbox,’” the institute wrote in an unsigned blog. “As was standard in our cyber testing, we had intentionally permitted internet access, and model-provider cyber classifiers were deliberately disabled – conditions that do not reflect how frontier models are made available to the public.”

The institute said that while their own design and evaluation choices “to some degree” enabled the malicious behavior, the models still displayed “novel, potentially deceptive behaviors” and were executed “to an extent and severity that we did not anticipate.”

The same day, OpenAI acknowledged AISI and another third-party cybersecurity tester,  private firm Irregular, had recently reported incidents where their models exceeded “beyond their intended testing boundaries.”

That included GPT-5.6-Sol reusing GitHub tokens left by another agent to attempt to recover accounts and to access a DNS server that contained malicious payloads meant to exploit a software vulnerability.

The setup did not work, there is no evidence any real resolver queried it, and the infrastructure was removed when the evaluation ended,” the company wrote in an unsigned blog post Tuesday.

OpenAI said it would review its own third-party testing procedures to focus on higher risk evaluations, assess requests by third-parties to enable internet access, stop conditions and other features for their models.

The incident with Irregular occurred on July 29 during a Capture-the-Flag cybersecurity evaluation of OpenAI’s models. Due to a “misconfiguration” the models were allowed to access the public internet, where they encountered and exploited a real domain, mistakenly believing it was still in a test environment.

OpenAI said an investigation by Irregular is ongoing, but also found that the models had found and used credentials for the site at one point. The blog also references other additional potential cybersecurity incidents.

“Irregular has informed us that all of the issues identified pertaining to the incident are no longer active and relevant safeguards were added to the testing environment,” the blog said. “Irregular has also communicated about related incidents involving other labs from the same testing environment.”

CyberScoop has reached out to Irregular for comment.

The incidents were made public the same day that the White House met with Anthropic, Open AI and other frontier AI companies to preview a new framework for evaluating models before they’re released publicly. Some media outlets have reported that after an executive order, export controls and other actions, the administration does not plan to make the new framework public.

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The Trump administration’s haphazard and opaque interventions into artificial intelligence security matters could catapult Chinese alternatives into broader acceptance, posing new security risks altogether, a group of Democratic senators wrote to top administration officials Monday.

The five senators said that the administration’s handling has alternated between too passive, such as when OpenAI models escaped testing in the Hugging Face hack last month, and overstepping, such as when the Commerce Department suspended access for any foreign national to Anthropic’s Fable 5 and Mythos 5 in June.

“The Administration’s ad hoc and unpredictable approach undermines U.S. competitiveness, heightening market incentives to adopt open weight models from vendors based in the People’s Republic of China (PRC),” wrote Sens. Kristen Gillibrand of New York, Adam Schiff of California, Mark Warner of Virginia, Chris Coons of Delaware and Mark Kelly of Arizona.

In the Hugging Face hack, the senators wrote that “the Federal Government cannot be passive as these capabilities emerge.”

In the case of the Fable 5 and Mythos 5 suspensions, the senators said that the administration “utilized an infrequently used authority to direct Anthropic to suspend all access to its Fable 5 and Mythos 5 models for foreign nationals (including foreign national employees inside the United States) citing an undisclosed national security concern later described as a narrow jailbreak finding.”

Because Anthropic couldn’t immediately assess users’ nationality, the firm had to disable both models for everyone. The administration and Anthropic negotiated for 18 days behind closed doors before reaching an agreement, the lawmakers complained.

“While the Administration may have been responding to real security concerns to protect the United States, even justifiable interventions can create broader harm if the standards and decision-making processes are opaque, ad hoc, or unpredictable,” they said in their letter to leaders in the White House, Office of the National Cyber Director and departments of State, Treasury and Commerce. “Moreover, when the Executive Branch exercises authority delegated from Congress, such as in the conduct of export control administration, it is essential that it keep Congress fully apprised of its actions and procedures.”

During the time Anthropic was under export controls, the stock price of “an entity-listed Chinese lab” nearly doubled, the senators said. And while Hugging Face was breached, the company “had to” rely on a Chinese open-weight model due to guardrails on U.S. frontier models.

“If American models are perceived as subject to sudden access disruptions based on a black-box U.S. Government process, or as unreliable because U.S. AI labs are overcorrecting in the face of this black-box process, companies and governments in the United States and abroad may hedge by adopting Chinese or other foreign models instead,” the senators contended. “That outcome would undermine U.S. technological leadership while increasing exposure to systems that may carry risks of PRC or otherwise directed censorship, espionage, IP theft, and other supply chain security risks.”

Their letter asked for answers to questions about the standards the administration uses to determine the national security risks a frontier model presents, what legal authorities it will use to invoke restrictions, which agencies are responsible for which decisions and more.

None of the offices or departments the letter was addressed to immediately responded to a request for comment.

The letter follows inquiries at the state level, where 15 attorneys general asked OpenAI for more information regarding the security incident at Hugging Face.

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President Donald Trump blamed Minnesota Friday for the cyberattacks its water systems have suffered in recent days, saying the state was “behind it.”

Trump said the state being “incompetent” was the issue, but it wasn’t clear whom he thought actually conducted cyberattacks that U.S. investigators have attributed to Iran — if, perhaps, somehow Minnesota incompetently cyberattacked itself. The White House referred a request for clarification back to Trump’s remarks.

“I think that Minnesota is behind it,” Trump told reporters Friday. “Because they’re grossly incompetent. I don’t think there was an Iranian cyberattack. I think Minnesota ought to get its act together.”

The White House also didn’t clarify whom the president believed was behind similar attacks in other states, when asked for comment. Trump has repeatedly used federal power aggressively in Minnesota, a state led by Gov. Tim Walz, a Democrat who was on the ticket that ran against him in 2024 as the vice presidential nominee. Trump also has downplayed Iranian attacks amid the war he launched against the nation with Israel in February.

A number of cyber experts quickly pushed back on Trump’s comments after he made them.

“Victim blaming in cyber is so 2000 and late,” cybersecurity pioneer Chris Wysopal, Veracode co-founder and chief security evangelist, said on the Bluesky social media platform. Said Jake Williams, a member of the IANS faculty: “His own intelligence services are attributing this to Iran.”

Andy Jabbour — founder and CEO of Gate 15, a cybersecurity firm which provides support to the water sector — told CyberScoop that, “speaking candidly, I’m not even sure what he was actually saying or suggesting Minnesota’s government did or didn’t do.”

“Attribution is tricky business,” he continued, referencing recent alerts from the Cybersecurity and Infrastructure Security Agency and others. “But logically, given an ongoing war with Iran, recent statements made by Iran-aligned threat groups, with assessments that the recent activity is aligned with recent CISA warnings, given yesterday’s statements from CISA and the FBI, random unsubstantiated allegations aimed at political opponents seem reckless and are a disservice to the American people.”

Walz struck back at Trump in a Facebook post, noting steps from his Department of Government Efficiency to slash federal funding. CISA has shrunken considerably under Trump, and his administration has pushed states to defend against cyberattacks that feds once countered.

“Trump knows exactly who is responsible for this attack, and knows that other states were hit too,” Walz said. “This is what modern warfare looks like, and it further illustrates there’s no plan to win a war with Iran.”

“DOGE took an axe to CISA and left the U.S. exposed to cyber attacks,” he continued. “Thankfully, our experts in Minnesota were able to identify the vulnerability quickly and work with local communities to stop it.”

A spokesperson for Minnesota IT Services, a state agency that has been responding to the water cyberattacks, declined to address Trump’s remarks.

“We remain focused on supporting affected communities, securing critical infrastructure and coordinating with local partners and federal officials as the investigation continues,” the spokesperson, Emily Zimmer, told CyberScoop. “We will not comment on political statements or speculate about attribution.”

Other cyber professionals declined to comment directly on Trump’s remarks, but offered thoughts on who was behind the attacks and their motives.

Bryson Bort, CEO and founder of Scythe said the evidence supports the attribution with Iran, and that it looks like hackers there found something they could exploit on the internet and seized the chance.

“This was a target of opportunity,” said Bort, co-founder of the ICS Village, a non-profit advancing awareness of industrial control system security; such systems are common in the water sector. “It wasn’t that Minnesota did something as a state to raise Iran’s ire.”

Cynthia Kaiser, a former top FBI cyber official, said that when the bureau conducts attributions, it looks at technical indicators but also who has the capability, who has conducted similar attacks in the past and what the purpose of the attacks is.

“Iran ticks all these kinds of things,” Kaiser, now senior vice president at cybersecurity firm Halcyon, told CyberScoop. “My view is, if it walks like a duck, if it talks like a duck, I strongly suspect it’s a duck. I’d be shocked if we found out it wasn’t Iran.”

Just last week, CISA updated an advisory about how Iranian hackers were targeting programmable logic controllers in the water sector and other sectors, a warning that the water industry’s information sharing and analysis center said it believed.

“WaterISAC is confident in our government partners’ assessment that the confirmed activity is aligned with the joint Cybersecurity Advisory (CSA) AA26-097A ‘Iranian-Affiliated Cyber Actors Exploit Programmable Logic Controllers Across U.S. Critical Infrastructure’ published and recently updated by CISA,” Tom Dobbins, executive director, WaterISAC, told CyberScoop. “We have evidence of earlier attacks from Iran even before this current conflict. Cyber attacks are the most viable way that Iran can directly attack our homeland, and it is logical that they would do so, especially given the challenges of absolute attribution.”

The water sector is often viewed as one of the most vulnerable critical infrastructure sectors, and Dobbins called on Congress to provide funding to provide funding for the ISAC.

Sen. Tina Smith, D-Minn., also took issue with Trump’s comments.

“The President provided an unserious response that is beneath the dignity of the office he holds. Iran’s purported cyberattack on Minnesota’s water infrastructure must be taken as a serious threat to our national security.  Smith said in a statement, adding that she’s been in touch with CISA and the FBI and was grateful to Minnesota’s IT experts. “The entire situation serves as a stark reminder of the danger this war puts us in the longer it drags on.”

Fellow Minnesota Democratic Sen. Amy Klobuchar had earlier been in touch with Sean Cairncross, the national cyber director and a Minnesota native, about the incident.

Trump has previously displayed a laissez-faire view toward other cyberattacks on the United States, such as when he’s been asked about Chinese and Russian cyberattacks and Trump shrugs them off as something America does, too.

He also has cast doubt before on his government officials’ assessments of who’s responsible for cyberattacks on the United States, such as when he asserted China rather than Russia was behind the landmark SolarWinds breach.

Updated 8/3/2026: with comments from Minnesota’s senators.

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Anthropic said it found three instances in which its models gained access to the live computer systems of outside organizations, according to a company blog post published Thursday.

The company said it began the review after OpenAI disclosed earlier this month that some of its models had exploited an unknown software flaw to escape an isolated test setup and reach production systems at Hugging Face, a platform for AI models and datasets. Anthropic checked over 141,000 evaluation runs for signs that Claude had reached the internet from environments meant to be closed off. It found six runs across three incidents, all tied to one outside testing partner, Irregular.

“Ultimately, many factors contributed to these incidents, but, consistent with a blameless postmortem culture, we’re approaching the fixes as if the responsibility were ours alone,” the blog post reads. “This begins with ensuring every part of our evaluation pipeline is secure, including the manner in which we integrate with external partners. Moving forward, it will include expanding our continuous monitoring of evaluation transcripts for unexpected behavior, improving our investigation tooling, and conducting more rigorous assurance work with the vendors we rely on.”

In each case, Claude was working on a “capture the flag” puzzle, a common way to test a model’s hacking skill. The model is told a piece of secret data sits on another machine and must be found. Anthropic‘s prompts told Claude it had no internet access. That was false. A setup error at the partner’s end left the machines connected to the open internet, and Claude found real systems while searching for its target.

Believing the real systems were part of the simulation, Claude broke into them using simple methods such as guessing weak passwords and finding unprotected access points. In the most serious case, the model extracted login credentials and reached a database holding several hundred rows of live data. 

In a second case, Claude built and uploaded a malicious software package to PyPI, a public directory for Python code, after noticing a fake company’s setup guide told new hires to install a package that did not exist. The package stayed online for about an hour and was installed on 15 systems, including one belonging to a security firm whose scanner ran the code and had its credentials stolen. 

In the third case, Claude scanned about 9,000 targets before breaking into one company’s application through exposed credentials and a database attack method known as SQL injection.

The three incidents involved different Claude models: Opus 4.7, Mythos 5, and an unreleased internal test model. Anthropic said the models behaved differently once they encountered signs their targets were real. Opus 4.7 recognized the systems were live but kept attacking anyway. Mythos 5 also noticed signs it was on the open internet but talked itself out of that conclusion, pointing to a system date reading 2026 as proof it remained in a simulation. The internal test model, the newest of the three, stopped once it concluded the target was real.

Anthropic said it began its transcript review last week, halted all cybersecurity evaluations, identified the three incidents, and notified the partner and affected organizations earlier this week. Two of the three organizations had not detected the intrusions before being contacted, the company said; outreach to the third was ongoing. 

Anthropic framed the incident as a failure of its testing setup and oversight rather than a case of a model acting on its own, noting that in each instance the model was operating as intended. 

The company said it is working with the outside evaluator METR on an independent review and plans to release a redacted transcript of the PyPI incident within a week. It also said it would tighten monitoring of test environments run by outside partners and expand review of evaluation logs, framing the changes as part of what it called a blameless review of its own processes.

“These facts give us cautious optimism that with tighter monitoring and controls around evaluation infrastructure, as well as continued investment in alignment, this type of risk can be overcome,” the blog post reads. 

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Anthropic researchers used Claude Mythos Preview to find new weaknesses in two cryptographic methods, the company said Tuesday, including one that is being considered by the National Institute of Standards and Technology for both traditional and quantum computing.  

In a blog post detailing the work, the frontier AI company called it a “substantial” research advancement, but also emphasized that neither flaw affects software now in use.

“The attacks described in these two papers are the strongest attacks we have found to date,” the company wrote in the post. 

One of the weaknesses found was in HAWK, a digital signature scheme under review by the NIST as part of a search for encryption methods that could survive attacks from quantum computers. Working with a human researcher, the AI system found a mathematical shortcut, known as a nontrivial automorphism, in the lattice structure (a complex mathematical grid underpinning its security) HAWK relies on.

The discovered weakness cuts HAWK’s effective key strength in half, meaning key sizes would need to double to keep the same level of security. Anthropic said that change would erase much of what made HAWK an appealing candidate in the first place.

Ellen Boehm, senior vice president of strategy and AI innovation at Keyfactor, a digital identity and cryptography management provider, told CyberScoop that research like Anthropic’s proves that the NIST PQC evaluation process is working. 

She also said the research “elevates the importance for organizations to have visibility of where cryptography sits inside their enterprise, what business systems and processes it’s connected to, and the need for PQC readiness, if they haven’t already built a plan.” 

The other flaw was found in a weakened version of the Advanced Encryption Standard, or AES, the cipher NIST adopted in 2001 and the most widely used method for scrambling data in transit. Working largely on its own, Mythos invented a mathematical shortcut dubbed the “Möbius Bridge.” While real-world encryption scrambles data through 10 sequential layers, or “rounds,” researchers regularly study a simplified seven-round test version to measure security margins. In previous theoretical attacks, codebreakers had to check 256 separate values against a memory table, but Mythos created a shortcut that eliminated that lookup process entirely.

Combined with other optimizations, this discovery made the strongest known theoretical attack against seven-round AES 200 to 800 times faster. The attack is purely theoretical: It requires an impossible amount of target data — over 400 octillion messages — and cannot touch the full 10-round encryption protecting everyday software. Additionally, Anthropic pointed out that real-world systems remain completely safe.

Anthropic said it followed standard disclosure practices, notifying HAWK’s designers in June and coordinating public release with a NIST mailing list, and briefing government and industry partners beforehand. It also worked with researchers at ETH Zurich, Tel Aviv University and the University of Haifa to build a shared testing tool, called CryptanalysisBench, meant to let other researchers measure how AI systems perform against a range of ciphers.

The findings come as frontier AI models are being deployed by cybersecurity researchers in order to find vulnerabilities in all kinds of software. In June, intelligence agencies in the Five Eyes alliance warned that advanced AI models capable of wreaking havoc in the cyber domain are “months away.” However, a recent report found that despite the avalanche of bugs being unearthed, the threat level across the internet has not materially changed. 

Anthropic said it expects the same AI capabilities eventually to be applied to systems already in wide use, raising a separate question it said it has not yet resolved: how researchers, companies and governments should respond if a language model uncovers a flaw in a cryptographic system that protects critical infrastructure.

“As we develop increasingly powerful cryptanalytic results, it would be prudent to consider how researchers should react if a language model were to discover vulnerabilities in cryptosystems where attacks do have an immediate real-world impact,” the company wrote. “We hope that our work here will help launch these conversations.” 

Boehm said work like Anthropic’s further shows that enterprises should not rest on their laurels with any facet of their security apparatus. 

“AI is becoming a powerful tool for many things, including software quality assurance, code development, and in this case cryptographic analysis,” she told CyberScoop. “As AI tools become more widely and continuously used, it just elevates the need for enterprises to treat their trust infrastructure in an ongoing, operational manner versus thinking of it as a static environment that only changes every few years as new cryptographic algorithms are released.”

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AI systems like Anthropic’s Project Glasswing and Microsoft’s MDASH are aiding in the discovery of vulnerabilities, filling the ever-growing pool of defects that defenders have to address before exploitation occurs. Yet, through the first half of 2026, these vulnerabilities were no more or less likely to be exploited than all vulnerabilities disclosed during that period, VulnCheck said in a report Tuesday. 

Concerns remain high about AI-discovered vulnerabilities fueling more attacks, but VulnCheck’s review of exploitation data shows that those fears are unfounded, at least so far. 

Patrick Garrity, security researcher at VulnCheck and report author, identified 1,061 vulnerabilities attributed to AI-assisted discovery during the first six months of the year. Of those vulnerabilities discovered by AI, 14 ( 1.3%) were exploited in the wild, a breakdown that aligns with the exploitation rate researchers observed across all vulnerabilities during the same period. 

“While AI-assisted vulnerability discovery clearly has value for both attackers and defenders, the data does not suggest that AI discovered vulnerabilities are inherently more likely to be exploited than those found through traditional methods,” Garrity wrote.

While AI’s contribution to actively exploited vulnerabilities was muted in the first half of the year, it’s too soon to assume that trend will continue. Moreover, none of these major vulnerability-hunting models were running for that full period. Project Glasswing rolled out in April, while Microsoft’s MDASH and OpenAI’s Daybreak were both unveiled in May.

The upward trend in Microsoft’s monthly Patch Tuesday indicates how much the floodgates might open through the remainder of the year as AI models discover more vulnerabilities. The company’s July security update contained an all-time-record of 622 vulnerabilities, besting the previous record-breaking June update with 206 vulnerabilities.

VulnCheck’s state of exploitation report also found that vulnerabilities were exploited much faster after CVE publication, speeding up from an average of 120 days in 2025 to 80 days during the first half of the year.

The intelligence firm also determined which technology categories were actively exploited most often. Content management systems accounted for nearly one-third of the 495 known exploited vulnerabilities VulnCheck identified during the first half of 2026. Network edge devices were responsible for almost 14%, followed by operating systems at nearly 9%, server software at 8%, and AI products — an emerging attack surface — at almost 6%.

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A Russian state-sponsored threat group has been stealing sensitive data from governments and commercial organizations since July 2025 via a novel exploit in popular Linux-based enterprise software, U.S. authorities and cyber officials from more than a dozen other countries warned in a joint cybersecurity advisory Thursday.

Laundry Bear’s most recent espionage campaign involves the exploitation of a zero-day vulnerability in Zimbra Collaboration Suite that wasn’t patched until November 2025, five months after attacks were well underway, officials said. 

The exploit just requires a view — no clicks — and allows attackers to steal the previous 90 days’ worth of email, the account’s password, search history, the victim organization’s email directory, two-factor authentication tokens and other newly created passwords.

“The covert and persistent nature of this activity, along with the absence of any known financial extortion, almost certainly indicates this group’s involvement in espionage activities with Russian government backing,” officials wrote in the advisory. 

“Additionally, extensive Ukrainian targeting, prior to use against U.S. and other NATO allies, outlines an increasing trend within Russian cyber threat groups to target Ukrainian users first—both as a priority target and as a testbench for malicious cyber techniques before broader global deployment.”

The state-sponsored espionage group, also known as Void Blizzard, has compromised governments and organizations in the defense, education, energy, law enforcement, media, finance, transportation and technology sectors. 

Laundry Bear’s year-long campaign involving the exploitation of CVE-2025-66376 showcases more technical capabilities, including a custom JavaScript payload it delivers to targeted victims via phishing emails. The threat group could also likely adapt the novel data exfiltration and aggregation capability, dubbed “beehive,” to exploit other vulnerabilities, officials warned.

The defect’s medium-severity rating of 6.1 underscores the challenge defenders regularly confront in prioritizing patching schedules based on measure of severity alone.

The Russian state-supported group, which has been active since at least 2024, is still actively exploiting Zimbra Collaboration Suite instances that remain unpatched, officials said.

Authorities shared Thursday indicators of compromise, mitigation steps and urged organizations to update their vulnerable software.

“This campaign’s targeted victimology and limited exploitation capabilities likely indicate this group manually identifies and targets the victim organizations” by identifying organizations with public-facing infrastructure, officials wrote in the advisory.

Once a target is identified, Laundry Bear also likely compiles email addresses for users to target with the exploit via phishing emails. Officials did not identify specific victims or describe the volume of organizations already compromised.

The joint cybersecurity advisory was issued by the United States, Australia, Canada, New Zealand, the United Kingdom, Czech Republic, Denmark, Estonia, Finland, France, Italy, Moldova, the Netherlands, Poland, Spain and Sweden.

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On July 1, the Cybersecurity and Infrastructure Security Agency (CISA) published a seven-page notice in the Federal Register that could fundamentally change how the U.S. government works with private companies to protect critical infrastructure from cyber threats and natural disasters.

The notice, “Establishment of the Alliance of National Councils for Homeland Operational Resilience – Critical Infrastructure (ANCHOR-CI),” details a new framework for CISA to build councils that let private partners advise the government on cybersecurity and critical infrastructure issues. This replaces the 20-year framework that the federal government used to work with critical infrastructure partners, formerly known as the Critical Infrastructure Partnership Advisory Council (CIPAC). For at least the next two years, ANCHOR-CI will dictate how the government and industry collaborate to protect critical infrastructure.

When former Homeland Security Secretary Kristi Noem terminated CIPAC in March of last year, Congress and private-sector partners objected immediately. The damage was real. The 16 sector-coordinating councils (SCCs), that brought together private partners from each critical infrastructure sector lost their legal mechanism to meet with the federal government. They could no longer advise and provide group consensus to their federal counterparts without triggering laws that CIPAC exempted. To be sure, CISA still had the Joint Cyber Defense Collaborative. Department of Energy had the Energy Threat Analysis Center. The National Security Agency had the Cybersecurity Collaboration Center. But none, however, replaced what the SCC ’s did: stand as steady forums where industry and government hashed out how to best assist critical infrastructure owners and operators.

Not that the SCCs were flawless. I worked with or alongside SCCs for more than a decade, most recently at CISA, and I am familiar with their shortcomings. SCC membership could be stagnant. The quality of recommendations to the government varied.  New members faced barriers based on each sector’s rules.

The core problem was how the model locked each sector in. DHS built SCCs in an era when critical infrastructure risks were looked at through a sector-specific lens: energy, transportation, water, communications, and so on. Today’s cyber threats jump across these sectors. A vulnerability in a cloud service provider or industrial software platform can harm hospitals, pipelines, manufacturers, water utilities, and financial institutions simultaneously.

To see why ANCHOR-CI works better than the CIPAC structure, we need to go back 20 years and understand what CIPAC was trying to do. Congress didn’t codify CIPAC into law. Instead, Congress authorized DHS to establish advisory committees exempt from the Federal Advisory Committee Act (FACA). This exemption let the federal government and SCCs hold private meetings without public notice, convene quickly without complying with FACA procedural requirements, pick members based on expertise rather than balanced public representation, and skip FACA’s public recordkeeping requirements. (But these FACA exemptions do not exempt records from the Freedom of Information Act, a common misconception.)

ANCHOR-CI carries this power forward. Crucially, ANCHOR-CI end the siloed, sector-by-sector approach by creating four types of councils: Critical Infrastructure Sector Councils; Cross-Sector Councils; Critical Infrastructure Industry Councils; and Regional Coordinating Councils.

Critical Infrastructure Sector Councils: These are basically the old SCCs, but with a change in power.  The CISA director now approves or removes any council member directly.

Cross-Sector Councils: These councils matter most. Specifically, they tackle “current and emerging threats, interdependencies, or other issues impacting multiple critical infrastructure sectors or industries.” Examples might include councils on countering unmanned aerial systems, AI threats, or reducing dependence on foreign supply chains. CISA could also revive and expand the Space Systems Critical Infrastructure Working Group.

Critical Infrastructure Industry Councils: Like cross-sector councils but designed for issues that span sectors in ways that don’t fit neatly into a given sector. After Volt Typhoon—a Chinese campaign that installed malicious malware in critical infrastructure—CISA could establish an Operational Technology council with original equipment manufacturers, software providers, and critical infrastructure owners to address and stop the threat.

Regional Coordinating Councils: CISA says these will help state and local governments tackle regional risks. What that means in practice is unclear. CISA could create 10 councils tied to 10 regional offices. Or it could create councils focused on real regional risks: preparing for the Cascadia subduction zone in the Pacific Northwest, droughts in the Southwest, or hurricane in the South and Mid-Atlantic.

In the end, like any policy, success hinges on how CISA carries it out. If CISA runs ANCHOR-CI thoughtfully and with transparency, it could become the biggest upgrade in of public-private cybersecurity collaboration work in two decades.

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A top White House technology official is accusing a Chinese company of distilling Anthropic’s models to create their own AI product.

Michael Kratsios, who leads the White House Office of Science and Technology Policy, claimed that Moonshot AI, a Beijing, China-based AI company, had distilled Anthropic’s recently-released Fable model to develop its own K3 model.

“To do this they developed a sophisticated internal platform to conduct large scale distillation against U.S. models, allowing them to quickly switch between multiple methods of access to avoid detection,” Kratsios wrote on X Wednesday.

Kratsios also said the company has used GB300 servers – either newly acquired or through Thailand – to train its AI models.

“The United States strongly supports the free and fair development of AI, including a thriving competitive ecosystem that spans frontier models, specialized systems, open-source frameworks, and open-weight models,” Kratsios continued. “Legitimate AI distillation used to create smaller, more efficient models plays a vital role in this open innovation ecosystem. However, large-scale, covert industrial distillation aimed at stealing proprietary U.S. technology and undermining American research is unacceptable.”

Kratsios did not provide details on how the U.S. government learned that K3 had been distilled from Anthropic’s model. 

Frontier AI companies in the U.S. have pressed policymakers to make it more difficult for third-parties to copy or duplicate advanced commercial models, calling it a form of intellectual property theft.

On their website, Moonshot AI describes its Kimi K3 model as the first open 2.8 trillion parameter model, and promotes its lower token costs while still delivering near-frontier performance. 

“While its overall performance still trails the most powerful proprietary models, Claude Fable 5 and GPT 5.6 Sol, Kimi K3 demonstrated frontier-level performance across our evaluation suite, consistently outperforming other tested models,” the company said on its website. 

A request for comment sent to Moonshot AI was not returned before this article’s publication. 

Piyush Sharma, CEO of Tuskira, an AI cybersecurity detection and response company, said distillation of AI models allows developers many of a model’s core capabilities. He pointed to another example when Anthropic earlier this year accused Chinese company Alibaba of distilling their Claude AI model.

According to Anthropic, the campaign used 25,000 fraudulent accounts to run 28.8 million interactions on Claude over six weeks. Given that kind of volume “the goal was clearly replication,” he said. 

“When a model has learned to reason through software weaknesses, security gaps, and attack paths, copying its behavior also copies that analytical capability,” said Sharma.

In April, Rep. Andrew Garbarino, R-N.Y., who chairs the House Homeland Security Committee and Rep. John Moolenaar, R-Mich., Chair of the Select Committee on China, announced they were conducting a joint investigation into the integration of Chinese AI models.

The committees said the inquiry will also focus on “examining a pattern of conduct by [Chinese]-based AI laboratories involving the large-scale theft of proprietary capabilities from American frontier AI systems through adversarial distillation” as well as “ the redistribution of those stolen capabilities as open-weight models available for global download, and the incorporation of PRC-origin models into products used daily by hundreds of thousands of American developers and engineers.”

Western governments and industry accuse Chinese companies of routinely stealing their technology, intellectual property and other trade secrets, often with the tacit support of Beijing. The copying of AI models would continue a long and established tradition of Chinese-sponsored intellectual property theft.

However, while distillation attacks by foreign governments or companies on U.S. frontier companies can have real national security implications, it’s still a fraught question of where policymakers should draw the line.

The AI industry, which includes not just frontier companies but large businesses with their own bespoke models, smaller proprietary startups and a vibrant open-source ecosystem, routinely share and use third-party data, including critical code and training sets for AI models.

Further, U.S. frontier AI companies have built and trained their world leading models in large part by crawling the open internet, ingesting content created and produced by others. Critics (and multiple ongoing lawsuits) argue that AI companies like OpenAI and Anthropic built their empires on data and content from others, taken almost entirely without consent or compensation.

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Frontier AI companies often refer to their models as “helpful assistants” or try to compare them to entry-level employees.  

But new research from the UK’s AI Security Institute reinforces how large language models suffer from a common flaw that would land many human employees in hot water with their employers: they cheat.

In other words, these models are so committed to completing their tasks that they will break the rules, cut corners and deceive  their own users to accomplish them.

“Every model we have tested for this behavior attempted to cheat,” the AISI report said.
“Models did not reliably report this behavior when asked, and often did not reason about it in their chain-of-thought, suggesting that detecting cheating will likely require robust monitoring methods.”

The research tested OpenAI’s ChatGPT 5.4, 5.5 and 5.6 models, along with Anthropic’s Claude Opus 4.7 and Mythos Preview.

The AISI ran models through a series of “Capture-the-Flag” cyber evaluations, where they obtained points by performing offensive cybersecurity related tasks, like exploiting a particular vulnerability or reverse engineering compiled code.

The research defines “cheating” as “taking an action that is out of scope for the task or explicitly disallowed by the rules, in order to achieve a goal through a shortcut, workaround, or unintended solution that the task was not meant to, or should not, permit.”

It captures a range of observed behaviors from models, like searching the internet for solutions, attacking or escalating privileges on unrelated systems, probing evaluation software to gain access to the task solution.

Models also failed to acknowledge when they were cheating to complete a task, and less than 50 percent said the rule-breaking was “wrong” when challenged on it by a user.

A model’s propensity for cheating was not related to its capability, meaning newer or advanced models aren’t more likely to cheat. Rather, researchers think it stems from the techniques used during a model’s training and alignment that are to blame.

But even if rates of cheating remain constant, the problem could still worsen over time. As newer models in the future could become more proficient and learn more effective cheating techniques.

This deception also makes it difficult for labs like AISI to verify their own work, which relies on evaluating trustworthy outputs from AI systems.

The research underscores how AI systems can go to drastic lengths to complete their task, including blowing through or circumventing a company’s IT and cybersecurity protections.

In one instance, AISI researchers said a model was inadvertently given a cyber capability evaluation that was misconfigured and impossible to solve.

“The model tested was so persistent in attempting to cheat that it wrote and ran code on an external service, hosted on the open internet outside of AISI’s systems, in an attempt to access our evaluation infrastructure, triggering a security alert in AISI’s systems,” the report said.

While AISI said there were no data leaks or damage from the incident, the model could have successfully accessed their evaluation system had they not had monitoring in place. The institute said it implemented further controls on internal systems in response to the test.

The researchers said there are “significant consequences” to a status quo where we can’t trust models not to cheat. The behaviors are especially problematic in areas like AI safety and security research, as well as cyber operations and military decision-making, where trust outputs from the AI systems are critical.

Today, AISI said it can detect LLM cheating through a mix of manual review and LLM monitoring, but that may not always be true, and future models may be better at hiding their actions from human overseers.

“A more fundamental fix would be to train the models not to cheat in the first place – but given this kind of behavior was reported in frontier models more than a year ago, robustly aligning it away may not be easy,” researchers wrote.

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After a year and a half spent downplaying calls for AI safety regulations, the Trump administration has sharply reversed course, embracing a level of government scrutiny of frontier AI systems before public release–a far stricter stance than the Biden administration took.

An executive order designed to be friendly to the AI industry was meant to let the federal government briefly review some new models on a voluntary basis.

When the Trump administration, suddenly and without much warning, slapped export controls on Anthropic’s Fable 5 and Mythos 5 in response to private sector threat intelligence reporting, the U.S. AI industry officially entered its regulatory era.

But key questions and gaps remain. It’s not clear why the administration drew the line where it did, or whether they will move it again in the future.

While newer models like Mythos and OpenAI’s Daybreak do have stronger cybersecurity capabilities, the private sector reports the administration relied on describe capabilities already available in older commercial, open-source and Chinese models that nearly anyone can access.

CyberScoop spoke with current users of the latest frontier models, including OpenAI’s ChatGPT 5.5 and Fable 5, to learn more about what these models are currently capable of in offensive and defensive cybersecurity.

Cybersecurity experts and former government officials say the administration may be playing catch up on threats that have been building for years as it has more fully realized the national security implications of the technology.

Are the models breaking new ground or just breaking things? 

Users of Chat GPT 5.5, introduced this past April, and Fable 5 tell CyberScoop those models have been largely helpful to their work, even as they complained about high token usage and safety guardrails that hinder,  but don’t meaningfully prevent, defensive cyber tasks.

Eyal Webber Zvik, chief strategy officer at Cato Networks, a cloud and cybersecurity network provider in OpenAI’s Trusted Access in Cyber program, said they use GPT 5.5 and later OpenAI models to scan and triage internal codebases for vulnerabilities, test new safeguards and provide “highly autonomized service” to their customers.

Zvik wouldn’t disclose how many bugs 5.5 has found but said the company’s view is that it helps both find bugs that humans missed and rank which ones to patch based on factors like each bug’s exploitability.

“It is now a native part of our development environment and cycles, and we use those models to scale our entire codebase and make sure what we release into the service that our customers use to run their networks and network security has the least likelihood of having any vulnerabilities that can be exploited,” said Zvik.

John Hopper, vice president of engineering at SpecterOps, an identity security company, said newer models like GPT 5.5 are sharper and more persistent in pursuing their tasks.

“That can be a good or bad thing,” he noted.

One metric that SpecterOps tracks is how long it can keep a particular agent working before it moves off task or fails. That metric “matters a lot” because the longer an agent works without human help , the more agents a single operator can run at once.

Hopper said this provides defenders with immense value, and pushed back on the idea that the offensive capabilities the models offer are automatically more beneficial to malicious hackers. There is “a modicum of grounding that the industry needs when we talk about these models.”

“Yes, AI frontier tools will lower the barrier of entry, but these problems have always existed,” he said. “I don’t actually believe that AI is going to remove the needle in the haystack problem, but by howdy, using my two hands to find that damn needle, compared to using a backhoe, I can tell you which one I’d rather be driving.”

Eran Kinsbruner, vice president of product marketing at software security firm Checkmarx, told CyberScoop that later models like OpenAI’s Codex Security and GPT 5.5 are noticeably easier to set up and run with local systems, even for less technical users. That alone gives them an edge over many cybersecurity tools where interoperability is a constant concern.

However, GPT 5.5 burns through tokens at a much faster rate. He recalled one instance of using it to scan a medium-sized repository in three different programming languages.

“After 26 minutes I almost ran out of tokens, and it didn’t provide anything, just created a threat model for me and told me you want to buy more tokens?” he said.

In other instances, some of the scan results he received were not comprehensive.

Further, he expressed frustration with some of the guardrails designed to prevent risk – like only allowing users to scan local files but not code repositories like GitHub – “makes not too much sense” given how often developers must work with remote code.

Those kinds of guardrails – which can prevent models or developers from injecting malicious code or prompting into their models – sit at the heart of the debate in Washington D.C. and around the world. Some users feel differently about their utility.

Kinsbruner said that doesn’t make sense for organizations like his, which work with thousands of different enterprise organizations with  thousands of different code repositories spread across the internet.

“I cannot imagine how large-scale developers could just jump into this solution and make it an enterprise-grade, enterprise-level, de facto cybersecurity solution” out of it, said Kinsbruner.

OpenAI did not respond to a request from CyberScoop for an interview on GPT 5.5. The company has since released another model, GPT 5.6, that they said is more efficient at token use.

The White House’s crash course in AI cyber risk 

 The White House keeps changing its line on whether and how the U.S. government should limit the release of commercial frontier models. The shift comes from lessons learned since coming into office in Jan. 2025. Trump threw out Biden-era regulations meant to steer the industry toward safer models. Top officials like Vice President JD Vance argued against restricting industry progress.

Less than two years later, administration officials worry about the impact of speed and scale – two things AI excels at – in cyberspace.

According to Will Loucks, senior director of intelligence at the Office of the National Cyber Director, over the past two years the number of exposed and known vulnerabilities has shot up. Threat actors exploit those flaws faster before defenders can fix them. Once inside, the time from initial access to full network control shrinks.

“So in other words, every stage of the cyber operations lifecycle that a threat actor has to move through to get to a victim network and achieve an outcome, they’re just moving through more quickly faster,” said Loucks at a July 16 event in Washington D.C.

Speaking about AI in particular, Loucks said one of the defining characteristics of the technology is its ability to lower barriers for threat actors.

“Sometimes speed and volume have a threatening aspect alone, even if sophistication isn’t quite increasing in the same way, and the reason for that is because it places pressure on defenders…to triage alerts more quickly,” he said.

Jordan Rae Kelly, former director for cyber and incident response on the White House’s National Security Council during Trump’s first term, told CyberScoop that the changes over the past two years reflect the lessons the White House has learned on the issue since returning to office.

In the early days of this administration, Kelly said, “there is a sense and a spirit that the Biden administration was limiting AI and there was a kind of a rip-it-all-off [attitude], everybody go and do whatever, we will be the biggest and boldest and brightest.”

“I love that talking point, but I think what you’ve seen is probably an education over the last 19 months, where people [in the White House] have said that’s a challenging premise to put into place, knowing about the potential downsides and capabilities,” she added.

Michael Daniel, former White House cyber coordinator under President Barack Obama, thinks the horse may already be out of the barn.

Daniel, now head of the Cyber Threat Alliance, a membership nonprofit group focused on cyber threat information sharing between industry and government, said his members report that AI is being used to do things “faster and at a slightly bigger scale” but aren’t yet seeing the flood of exploitation that analysts have warned about. Not yet.

“I think what we’re seeing right now [and] talking about is ‘okay, where are the step changes [in the cyber threat landscape] actually going to occur?” said Daniel. “Are we and when will we see the explosion in vulnerability reporting from these Mythos-like capabilities? That’s what’s really got their attention right now.”

But Mythos and OpenAI’s Daybreak models are restricted to select organizations, and neither has publicly released its most powerful cybersecurity models to the public. That dynamic won’t last.

The UK’s AI Security Institute estimates that open source and foreign LLM models are between 4-7 months behind frontier U.S. models. In that setting, it’s hard to stop the development of AI models worldwide through export controls or other limits.

“It’s not like we’re buying ourselves five to ten years on this,” he said. “We’re not, and so I’m not sure the impact on the defenders who are trying to obey the law is worth whatever small hiccup we cause for our adversaries.”

Kelly said there’s merit to the administration’s current position, even if it took time to get there. Many federal cybersecurity procedures that operated even a decade ago – such as a Vulnerabilities Equities Process that could take days or weeks to consider the pros and cons of keeping an exploit – are no longer practical.

“All of that work to some degree, is out the window, because you can’t meet with the regularity you would need to meet to adjudicate vulnerabilities that are being found in seconds and exploited in minutes,” said Kelly.

But Kelly and others say that’s also because AI capabilities in cybersecurity are developing faster than policymakers can react, even in the best of times.

Key questions remain and the administration’s balance between national security and backing domestic industry will likely shift  in response to new events.  The administration wants a framework that can predict and manage the risks of AI models today and tomorrow. That may be harder than it sounds.

“Do I think they’ve been clear? No,” said Kelly. “But I think it’s a place where clarity is really hard to achieve.”

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2026 has turned out to be the year when predictions about AI-powered cyberattacks, long hypothesized as a potential risk associated with AI improvement, seem to be coming true. New models have capabilities on par with the best human hackers, marking a pivotal window of opportunity in both AI and cybersecurity policy. This is a transitional period where new technologies are pushing existing American cybersecurity infrastructure to the brink. The real question isn’t whether cybersecurity still matters, but rather: How will the risks that AI introduces be managed before they outpace defenses, and who will step up to lead this challenge?

Attempts to control access to models with powerful cybersecurity capabilities, such as the federal government’s export controls (and their subsequent revocation) on Anthropic’s Mythos and Fable models, can only ever be a temporary solution. As with previous generations of AI models, other companies will soon catch up and develop models with Mythos-level capabilities. OpenAI was already hot on Anthropic’s heels with its GPT-5.5 model; more recently, Chinese lab Z.ai released its open-weight GLM-5.2 model, which early research suggests may be on par with Anthropic and OpenAI’s latest models when it comes to cybersecurity. Controlling AI is nearly impossible when foreign companies race to build more powerful models and release them publicly, so anyone with sufficient computing power can modify them for their own purposes.

The only long-term solution is to invest in defense. 

The problem is that defensive efforts haven’t kept up with the pace of AI progress. The federal government cut resources to key agencies like CISA and redistributed their authorities. This created a gap that AI companies have filled by taking on responsibilities that should be government-led. Some examples are Anthropic’s Project Glasswing and OpenAI’s Patch the Planet initiative, which aim to shore up critical infrastructure providers and open-source software libraries. AI companies have some incentives to invest in defense, both to improve public relations and strengthen software supply chains that they also rely on—but only to a certain extent. Unlike the public sector, they are incentivized to limit liability and blowback associated with irresponsible corporate behavior, not to secure the nation or its citizens. It’s a good thing that OpenAI and Anthropic have publicly committed to improving U.S. cyber defense. However, they are only positioned to help with one part of a very large problem. 

AI companies shouldn’t be expected to singlehandedly coordinate U.S. cyber defense, because many of the most urgent fixes have nothing to do with AI. Right now, AI companies can use their most powerful models to find software vulnerabilities and write patches. This is undoubtedly important, but the real challenge is making sure patches actually work and deploying them to key systems without causing problems. This is especially true for critical infrastructure, which relies on systems that are fragile, understaffed, and required to run continuously. 

AI companies bear responsibility for cyber defense, especially given the threats their own technologies create. But this responsibility is shared with other companies and the government.  Critical infrastructure owners and operators, government agencies, and corporations all need a trustworthy source of information to judge the evolving risk landscape and to outline the options to reduce that risk. Traditionally, the federal government has played the role of an information clearinghouse, receiving intelligence from both the public and private sectors and releasing guidance to benefit various stakeholders. Responding to and recovering from cyberattacks has traditionally been the government’s job. It should stay the government’s job, not become an AI company responsibility. 

There is no question that cyberattacks, whether powered by AI or not, will happen in the future. Leaders should strengthen our defenses by doing the following: measuring our exposure to attack, testing how systems perform under attack, and shortening recovery times. AI companies have introduced new threats and should help address them, but they can’t replace the government’s role. So far, the federal government has only reacted to AI and cyberthreats instead of planning ahead. What we need is real long-term cybersecurity strategy, not quick-fixes like blocking individual model releases. 

Everyone sees the threat coming—the question is whether or not we have the will to do anything about it before it’s too late.

Jessica Ji is a senior research analyst at Georgetown University’s Center for Security and Emerging Technology (CSET), where she works on the CyberAI Project.

Andrew Lohn is a senior fellow at Georgetown University’s Center for Security and Emerging Technology (CSET), where he works on the CyberAI Project.

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Russian state-sponsored hackers are breaking into critical infrastructure around the world by exploiting poorly configured and vulnerable networking devices, authorities from the United States and 12 additional countries said in a joint cybersecurity advisory Monday.

Officials once again urged defenders to take more preventative measures to thwart attacks from the Russian Federal Security Service Center 16, which has been actively targeting critical infrastructure for more than a decade. The hackers are also tracked as Berserk Bear, Energetic Bear, Crouching Yeti, Dragonfly, Ghost Blizzard and Static Tundra.

“This is an ongoing issue that has impacted various U.S. and foreign networks across multiple sectors, including the defense industrial base, communications, energy, financial services, government facilities and health care sectors,” the National Security Agency said in a statement.

The state-sponsored attackers scan the internet for vulnerable routers using default or weak passwords, and have also exploited vulnerabilities in Cisco devices, Cisco’s Smart Install feature and web portals to take over network devices. 

Two of the Cisco vulnerabilities exploited by the Russian FSB Center 16 hackers are quite old, including CVE-2008-4128 and CVE-2018-0171. 

Officials shared technical details of the threat group’s activities and advised network defenders to disable Cisco Smart Install on all devices, use stronger modes of authentication and passwords, monitor for unusual credentials and logins using local accounts. 

The joint advisory comes nearly a year after the FBI issued a similar alert about the same group targeting end-of-life networking devices running Cisco Smart Install. 

On Monday, the European Union blamed Russia’s FSB Center 16 for a December 2025 attack on Poland’s energy grid. The United Kingdom, also on Monday, sanctioned 24 individuals and entities allegedly involved in various attacks attributed to Russian intelligence services.

“From directing criminals to targeting businesses, and striking Poland’s energy grid in the depths of winter, the Russian state is sinking to new lows in its attempts to undermine European security,” Yvette Cooper, foreign security of the United Kingdom, said in a statement. 

Other countries behind the joint cybersecurity advisory include: Canada, Australia, New Zealand, Czech Republic, Denmark, Estonia, Finland, France, Italy, Poland and Sweden.

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Anthropic has announced its Fable 5 and Mythos 5 models will once again be available to the public as it has reached an agreement with the Commerce Department to deploy the AI models with new guardrails and classifiers meant to address jailbreaks.

In a blog posted Tuesday, Anthropic said that export controls that prevented their sale to foreign companies and individuals have been lifted after weeks of negotiation with the White House and Commerce Department. The company has also restored access to the model for U.S. users.

The export controls were put in place after the Trump administration became alarmed by a threat intelligence report from Amazon claiming to have jailbroken Fable’s cybersecurity capabilities.

On X, Secretary of Commerce Howard Lutnick appeared to confirm that the restrictions would be lifted.

“Over the past two weeks, we have worked closely with Anthropic to analyze and approve Fable 5 to ensure alignment across the US Government and strengthen America’s leadership in AI,” Lutnick wrote.

The administration levied the export controls after becoming concerned that the release of Fable 5 would lead to the model being jailbroken, giving users access to cybersecurity and other capabilities that Anthropic has said could wreak havoc on the open internet if  placed in the wrong hands. The Amazon report convinced administration officials that such jailbreaks were on the immediate horizon.

However, one oddity of the administration’s decision is that the capabilities described in the Amazon report, by all accounts, are not cutting-edge. Scanning code and breaking down how to exploit vulnerabilities for a user is already possible with existing models.

Anthropic confirmed that, saying that further testing found that equivalent and lesser models like ChatGPT 5.5, Claude Opus 4.8 and Kimi K2.7 could identify the same vulnerabilities as Fable did in the Amazon report, while a half dozen existing models were able to produce the same proof of concept code as Fable.

Crucially, Anthropic reiterated that they have yet to see a jailbreak that affects the model’s restrictions on cybersecurity and biology work, though they did call this instance “a borderline case.” Indeed, some cybersecurity professionals have publicly complained that existing safety guardrails on Fable 5 blocked many routine defensive cybersecurity work in addition to malicious use cases.

“Importantly, the reported technique did not expose any unique Mythos-level cyber capabilities,” the blog continued. “The behavior reflected a borderline case for Fable 5’s safeguards…there are some tasks that are unlikely to be dangerous but are nonetheless blocked by the safeguards out of an abundance of caution. The reported technique allowed access to one such behavior, but it only involved routine defensive cybersecurity work.”

Anthropic said it has trained new safety classifiers to target and block the behaviors described in the Amazon report and notify users when it happens, and that the new safeguards have been stress tested by the federal Center for AI Standards and Innovation. The new classifiers will block the techniques “99.9%” of the time, but Anthropic said they’re not expected to block all lower risk routine cyberdefense capabilities, just the most harmful ones.

The restrictions will likely make it even harder to use Fable 5 for defensive cybersecurity. One effect the company expects is that more “benign” requests for routine coding and debugging tasks will be flagged by the system.

Christopher Padilla, former Assistant Secretary for Commerce for export administration in the George W. Bush administration, said that while it’s “good news” the controls have ultimately been lifted, the Trump administration’s AI policy stumbles over the past two years illustrate “the risks of ad hoc, transactional policymaking.”

In a LinkedIn post, Padilla called the Trump administration’s approach chaotic and unpredictable — the opposite of the clear, consistent rules industry depends on. While Vice President J.D. Vance mocked AI safety regulations in a speech in Europe last year, the administration has quietly partnered with OpenAI and Anthropic on voluntary national security testing, especially as frontier models began showing advanced automation and cyberattack capabilities.

That national security arrangement was supposedly codified in a White House executive order last month, shaped heavily by industry boosters who feared regulatory delays would slow U.S. development. But days after Fable’s release, Commerce imposed new export controls on Anthropic’s models anyway.

Padilla called proposed AI safety regulations by the Biden administration “flawed and overly complex” but nevertheless predictable compared to the status quo. Instead of replacing those proposed regulations with their own vision, the Trump White House has been “to put it mildly, all over the place on AI policy.”

“The same BIS that stopped Fable and Mythos has a permissive policy for exporting high-end AI semiconductors to China — in exchange for a cut of the take,” said Padilla, referencing the Trump administration’s lifting of export controls on advanced AI chips. “This is not a smart way to make policy. Bad for industry competitiveness and for national security.”

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In a 5-4 decision, the Supreme Court upheld the right of states to accept mail-in ballots that are postmarked by Election Day, but can arrive up to five days later through the mail system.

The case stems from a lawsuit brought by the Republican National Committee against Mississippi and its Secretary of State, arguing they could not legally count mail ballots that arrive after Election Day, even if they are postmarked on or before that same day. The RNC argued that federal law defines “elections” and “Election Day” as the casting and receiving of ballots by that day.

Writing for the majority, Justice Amy Coney Barrett rejected that argument, stating that “nothing in the federal Election-Day statutes require ballots to be received by election day.”

“The federal Election-Day statutes do not preempt Mississippi’s law because the defining element of an ‘election’ has always been the electorate’s choice of candidate,” wrote Barrett.

Barrett, joined by Justices John Roberts, Elena Kagan, Sonia Sotomayor and Ketanji Jackson Brown, noted that other federal voting statutes like the Uniformed and Overseas Citizens Absentee Voting Act explicitly say that state law governs when ballots must be received, not the federal government. 

Further, while Congress inserted the phrase “Election Day” and specifies it as a Tuesday in its most recent update, it also allows states to modify that period of voting in response to certain force majeure events, like the COVID-19 pandemic.

While the Constitution requires voting to take place by a certain time, the review of those legally cast votes does not need to conclude at the same time.

“The Constitution requires the ‘Day on which [the electors] shall give their Votes’ to be ‘the same throughout the United States,’ but says nothing about the day for receipt,” wrote Barrett. “The Constitution thus envisions a system in which receipt of votes is necessarily divorced from voting. And it sets the crucial, uniform day as the day of voting while leaving receipt to happen later. The federal Election-Day statutes follow the same pattern.”

David Becker, executive director of the Center for Election Innovation and Research, said the ruling upholds the principle that “the election is completed for the voter at the moment they complete their ballot, not the moment that some administrative election official receives that ballot or reviews that ballot.”

It also validates more than a hundred years of state autonomy in setting their own rules regarding the receipt of election ballots.

“This case was about who gets to make that determination,” said Becker. “And as the founders intended, as is clearly laid out in the elections clause of the Constitution, the states get to make that determination about when those ballots should be delivered by the postal service and be counted.”

Still, Becker bemoaned the slow, steady politicization of the issue, and said in conversations many election officials were anxious about the case and relieved to see a victory, albeit a narrow one.

“I’ll be honest with you, in any other era this case should have been a 9-0 [decision],” said Becker. “This is a slam dunk, the states clearly have the authority to do this, they’ve been doing this for decades and decades.”

The ruling likely forecloses major changes to the way states receive or accept mail-in ballots before the midterm elections, but Becker does expect some states to seek legislative changes to align their state laws with the five-day post-election timeline blessed by the court.

14 states and Washington D.C. have state laws that allow any ballot to be received up to five days after Election Day or longer, while more than 30 allow military and overseas ballots to arrive after Election Day. Election experts have said ruling that such ballots were invalid could have upended decades of voting precedent and procedures for American voters at home and abroad.

Michael McNulty, director of Issue One Policy, a nonprofit focused on money in politics and elections, said had the court ruled the other way, it would have created chaos for election administration in more than a dozen states that accept such ballots, forcing them to move ballot receipt deadlines, redesign procedures and conduct large scale voter education campaigns without any additional funding.

The Supreme Court “rightly rejected an attempt to reinterpret federal law to force sweeping last-minute changes to election systems across the country and discard legally cast ballots.”

Pamela Smith, CEO of Verified Voting, a nonprofit focused on promoting secure election technologies, said the ruling should give relief to voters who rely on mail or absentee voting.

“This ruling ensures that a postal delay outside of any voter’s control does not erase a lawfully cast ballot and supports election officials’ ability to capture the will of voters,” said Smith.

Post-election audits and investigations have consistently shown voter fraud in the U.S. is exceedingly rare, and that mail-in ballots are not any more susceptible to fraud than other forms of voting.

Nevertheless, states accepting mail-in ballots past Election Day has been a politically charged subject since the 2020 election, when then-incumbent President Donald Trump was defeated by Joe Biden in part on the strength of late-arriving mail ballots that heavily swung in Biden’s favor.

In the years since, both Trump and the GOP more broadly have cast late arriving mail ballots as inherently suspicious, untrustworthy and opaque.

Those beliefs have persisted.

In their dissent, Justices Samuel Alito, Neil Gorsuch, Clarence Thomas and Brett Kavanaugh wrote that the decision “leaves open opportunities for voter fraud that may further undermine Americans’ faith in the integrity of this country’s elections.”

“Mail voting also presents a greater opportunity for voter manipulation, a more vulnerable chain of ballot custody, and a diminished ability to detect improprieties in real time,” Alito wrote on behalf of the minority. “Today’s decision compounds these vulnerabilities. Allowing absentee ballots to pour in over the days and weeks after election day, by which point preliminary election returns are being publicly reported, creates greater opportunity for fraud and risks further undermining the public’s confidence in election integrity.”

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The Federal Communications Commission approved new rules Thursday that boost cybersecurity regulations for the nation’s emergency alert systems and update security rules for the nation’s undersea cables.

The new rule would overhaul two national emergency systems, the Emergency Alert System and Wireless Emergency Alerts, to better protect against hijacking attacks from malicious actors.

The EAS is a national public warning system that state and local authorities use to disseminate information related to weather events, AMBER alerts and other emergencies via radio and television broadcasting stations. The WEA handles much of the same messaging via text.

A compromise of either system by a foreign government, cybercriminal group or other rogue actor could be used to sow chaos and disinformation in calmer times, or impede coordination efforts in the face of a genuine emergency. Any vulnerability in systems like the Emergency Alert System “can have serious consequences,” said FCC Commissioner Olivia Trusty in a statement after the vote.

“That is why it has been appropriate for the Commission to conduct a comprehensive review of the EAS framework by focusing on the security of the system itself,” Trusty continued. “As cybersecurity threats continue to evolve, EAS participants must take appropriate steps to safeguard the infrastructure that supports the delivery of life-saving alerts.”

The new rules amount to basic – but still critical – cyber hygiene practices for users accessing and updating the EAS and WEA systems. They must use strong passwords, quickly install security patches from vendors and use firewalls to limit access to their equipment.

The rule also creates a new authentication ID system to verify alerts before they’re submitted and avoid duplicate or unauthorized alerts from spreading.

Another rule passed by the Commission Thursday provided the first comprehensive update to the FCC’s submarine cable regulations in decades, and moves to tighten cybersecurity requirements in some areas while loosening them in others.

It exempts some undersea cable providers from submitting to stringent national security licensing reviews needed to land and operate cables that touch U.S. territory.

The review, called “Team Telecom,” is an interagency body led by the Department of Justice’s Foreign Investment Review Section and other federal agencies that advise the FCC on the national security implications of their telecom policies.

The new rules would presumptively exempt applications for undersea cable licensees when the provider can self-certify to “high security standards” that are “structured to increase certainty, predictability, and faster timelines for the licensing process.”

“Currently, all submarine cable applications get referred to Team Telecom…the changes adopted would exempt applications from applicants that have operated cables without incident, can certify to the highest national security standards, and agree to ongoing oversight and monitoring,” the FCC said in a release.

Other parts of the rule give the FCC greater oversight of critical functions within undersea cable operations. Owners and operators of submarine line terminal equipment, who connect submarine cables to land-based facilities in the U.S., will be subject to a new licensing requirement.

The rule also moves to update safeguards meant to address vulnerabilities related to principal equipment, third-party service providers, and other areas of concern in the undersea cable supply chain.

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In a novel maneuver for a disruption operation against cyber attackers, industry and law enforcement teamed up to conduct a court takedown of two widely-used criminal tools at once rather than individually, Microsoft said Tuesday.

The takedown simultaneously went after Amadey, a botnet that can serve as a malware delivery system, and StealC, an infostealer. Cybercriminals often use them in conjunction and they rely on the same infrastructure, Microsoft said.

“When multiple parts of an operation are disrupted together, attacks are harder to launch, scale, and recover from,” said Steven Masada, assistant general counsel for Microsoft’s Digital Crimes Unit. “The result: fewer disrupted services, fewer opportunities for cybercriminals to profit, and more friction when they try to rebuild. It’s no longer enough to go after threats one by one. We need to interrupt how the attacks are put together.”

Microsoft had been tracking Amadey with ESET, BitSight, Lumen and Mitsui Bussan Secure Directions. Meanwhile, Europol had been investigating StealC alongside law enforcement partners including Germany’s Federal Criminal Police Office and the Dutch and Danish National Police as well as IBM X-Force and Proofpoint.

They then joined forces and turned to the Racketeer Influenced and Corrupt Organizations (RICO) Act, used to help authorities go after organized crime, to disrupt more than 200 command-and-control servers. Microsoft said it gained insights from its artificial intelligence product Copilot that “allowed the legal team to treat both malware families as part of a single criminal conspiracy.”

Microsoft regularly leads court-authorized disruption operations, but the industry and law enforcement partnerships combined with AI to expand data collection and identify connections beyond what one company could normally do, it said.

Amadey and StealC were linked to more than 140,000 infected computers around the globe in the first week of May alone, the company said. StealC has ranked among the top infostealers for years since its emergence in 2023 and sells in underground forums as a malware-as-a-service. It’s typically used by Russia-linked groups.

Amadey dates back to 2018, and is also commonly employed by Russian groups, including in attacks on Ukraine.

Their interaction shows the assembly line-like structure of modern cybercrime, Microsoft said. Even if the cybercriminals behind both tools never coordinate, their tools are designed to work together, it said.

“StealC is an infostealer that collects sensitive data from browsers, cryptocurrency wallets, messaging applications, email clients, and gaming platforms,” the company wrote in a separate blog post. “It is a malware-as-a-service (MaaS) offering that threat actors use to generate customized payloads and manage stolen data through a centralized web panel. Meanwhile, Amadey is a MaaS loader that threat actors use to deliver StealC and other malware. Modular, pay-as-you-go models like StealC and Amadey allow threat actors to use a single initial infection to quickly escalate into multiple other threats.”

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Intelligence agencies for the United States, Canada, UK, Australia and New Zealand are warning that advanced AI models capable of wreaking havoc in the cyber domain are “months away” from being publicly available.

In a joint statement, the Five Eyes alliance say they expect the kind of advanced hacking capabilities provided by frontier models like Anthropic’s Fable 5 and OpenAI’s Daybreak to become broadly available the public within the year, despite efforts by AI companies to withhold them or restrict their access.

“Frontier Al models are anticipated to exceed current industry expectations, fundamentally transforming both offensive and defensive cyber capabilities,” the agencies said. “The timeline is not years, it is months.”

The statement, which included signatures from NSA’s Director of the Cybersecurity Directorate David Imbordino and acting CISA Director Nick Andersen, does not specifically cite secret or classified sources or methods to reach this conclusion.

But much of the underlying justification provided by the intelligence agencies also aligns with what public cybersecurity and AI experts have been warning about for months.

AI models capable of exploiting cybersecurity weaknesses are already available today through multiple channels: older commercial models, open-source versions, or foreign and black-market sources. And while newer models like Mythos are reportedly significantly more powerful for cybersecurity-related tasks, the breakneck pace of frontier model development often means that yesterday’s restricted frontier AI is tomorrow’s free, open-source AI.

Representative Andrew Garbarino, R-N.Y., Chair of the House Homeland Security Committee, said the warning from intelligence agencies “underscores what the Committee has repeatedly heard through roundtables, briefings, and hearings with industry leaders: China is just months, if not now weeks, away from achieving frontier AI capabilities comparable to those of the United States.”

“This threat reinforces the urgency of ensuring that federal agencies and critical infrastructure operators can responsibly leverage advanced U.S. models, and receive the guidance and support necessary to do so, to find vulnerabilities before adversaries can exploit them,” said Garbarino in a statement.”

The agencies flag legacy systems, sluggish patching loops, unnecessary internet connectivity, weak identity and access controls, and a lack of pre-incident planning by organizations as key weaknesses that AI will excel at exploiting.

“The rapid pace of frontier AI development means cyber risk assumptions can become outdated in months, not years,” the agencies wrote. “We must act before and be prepared to adapt and withstand evolving threats.”

Since large language models burst onto the scene, open-source models have run about 6-8 months behind the largest frontier AI companies.

To give an idea of how quickly the field develops: the capabilities described in the Amazon threat intelligence report that convinced the Trump administration to place export controls on Fable 5 could already be accomplished through older models like Claude Opus and Claude Sonnet, as well as open-source Chinese models.

Anthropic shut down access to their Fable 5 and Mythos 5 models as a result, and despite releasing a statement that they believe the White House decision was a “misunderstanding” the dispute remains resolved.

Programs like Anthropic’s Project Glasswing and OpenAI’s Trusted Access for Cyber Program provide AI systems to organizations for cyberdefense.  The goal is to give defenders a head start in finding and fixing vulnerabilities before AI systems can exploit them routinely in the coming years.

However, for all the fear surrounding the new technology, the recommended guidance is largely the same as it has been for decades. Governments, businesses and leaders must stop treating the digital security of their work as an afterthought or compliance issue.

“Success will come from getting the basics right, acting quickly, and integrating cyber security into core business strategy,” the agencies wrote. “Those that do not will face growing operational and strategic disadvantage.”

06/23/2026: This story was updated to include comment from Rep. Andrew Garbarino, R-N.Y.

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