NATO’s cyber defense arm and a startup that uses artificial intelligence to find software flaws can now issue the ID numbers the industry uses to track those flaws, the European Union Agency for Cybersecurity announced last week. 

The NATO Cyber Security Centre, part of the NATO Communications and Information Agency, and AISLE, a cybersecurity company with offices in San Francisco and Prague, joined as CVE numbering authorities under the ENISA Root. The CVE program assigns a unique record to each publicly disclosed security flaw so that governments, vendors and researchers have a common marker when referring to particular vulnerabilities. 

Twenty numbering authorities now sit under the ENISA Root, with 12 brought in by ENISA itself and eight moving over from the MITRE Root, run by the U.S. nonprofit that has handled the program’s daily work for more than 20 years.

Hans de Vries, ENISA’s chief cybersecurity and operations officer, linked the growth to changes in how people find flaws. 

“Recent developments in the global cybersecurity landscape, coupled with the emergence of Frontier AI models and their impact on vulnerability discovery and exploitation, have underscored the need to build strong vulnerability management infrastructure and capabilities,” he said in a statement. He said ENISA’s role helps build a “more globally representative, resilient, and scalable vulnerability identification ecosystem.”

The two new members show how bespoke each member is within its authority. The NATO Cyber Security Centre can now assign CVE IDs to eligible flaws across the NATO enterprise. The agency said that will make tracking more consistent and let the alliance share information with trusted partners sooner. The center guards NATO’s networks, watches for threats and coordinates the response when incidents hit.

Meanwhile, AISLE’s authorization is narrower. The company said in a July press release that the designation covers vulnerabilities discovered in its own products, allowing it to publish identifiers without waiting for a third-party authority to process a request. 

Jaya Baloo, the company’s co-founder, described the step as “foundational” and said coordinated disclosure “starts with holding your own products to the same standard you expect of everyone else.” Separately from the designation, the company said its researchers have disclosed hundreds of vulnerabilities in widely used open-source software, including OpenSSL, Linux, Apache and OpenEMR, each coordinated through the relevant authority for that project.

The changes come as the CVE process continues to involve amid program upheaval and the torrent of vulnerabilities discovered by AI systems. 

The CVE program, run by CISA, narrowly escaped a sudden demise when a last-minute, 11-month contract extension averted a shutdown in April 2025. Since then, several competing databases from European nonprofits and other private entities have been stood up in order to better coordinate how vulnerabilities are tracked, disclosed, and ultimately patched.

Earlier this year, The Computer Incident Response Center Luxembourg (CIRCL) launched the Global CVE Allocation System, or GCVE, as an alternative to the CVE program.

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TeamPCP, the threat actor behind an unrelenting flurry of attacks on open-source software this year, has been active much longer than previously thought, according to research Oligo Security shared exclusively with CyberScoop. 

The threat actor, which gained notoriety and has captivated threat hunters as it compromised and injected malicious code into more than 1,000 software packages in less than four months earlier this year, was also responsible for attacks dating back to 2020, Oligo Security found. 

The security vendor’s research team found multiple attacks that bear the markings of TeamPCP, including a late 2025 campaign involving the exploitation of a ShadowRay vulnerability that resulted in the first self-propogating botnet running on hijacked AI infrastructure.

Evidence uncovered during that investigation into the ShadowRay 2.0 campaign was linked to more historical attacks originating from the same IPs, domains and other infrastructure TeamPCP used in attacks that captured widespread attention earlier this year. 

“The scariest thing in this campaign is the speed at which the payloads evolved and changed and adapted to the environment they run in. We saw changes in the speed that we’re not used to seeing in these kinds of attacks. They’re usually slow, careful,” said Uri Katz, director of research at Oligo Security. “This was clearly with the help of AI — the payloads changed rapidly to adjust and change to the environment that they were trying to attack.”

One of the domains that Oligo Security identified in July 2025 was in the profile of TeamPCP’s official GitHub account, said Avi Lumelsky, AI security researcher at Oligo Security. “It’s public, they’re not even trying to hide their identity,” he said. 

From there, Oligo linked TeamPCP to activity tracked under multiple names, including TA-NATALSTATUS and IronErn, spanning from 2020 to late 2025. Much of that activity was traced to the same IPs, domain names, a file server and command-and-control server, researchers said. 

TeamPCP emerged publicly as a brand in late 2025. Soon after, “TeamPCP started to go really broad and do campaigns, which are much more noisy,” said Gal Elbaz, co-founder and CTO at Oligo Security. 

Widespread adoption of AI and TeamPCP’s use of the technology supported this growth as the threat actor built a brand, got more active on social media and boasted publicly about its activities and claimed victims.

“The ability to control the infrastructure and orchestrate the attack with AI was also super new, and I’m sure it helps them,” Elbaz said. 

“All of the companies in the world are in this race to adopt AI because they are afraid their business will die, and they understand, of course, the opportunity. But it’s also what gives the attacker this power to go into it,” he added. “If you don’t really have visibility in what’s going on there or how it behaves, that’s exactly what attackers are after.”

TeamPCP’s more recent attacks have capitalized on new security gaps created by developers’ increasing reliance on AI and the automated systems companies use to deploy code. The threat actor is also consistently wrecking the open-source frameworks and software packages these systems rely on. 

“Most AI infrastructure is open source by design because nobody has the manpower and money to develop everything from scratch,” Lumelsky said. 

“We love open source. We use many of these products ourselves, but it’s all about reading the documentation, and I think many of these tools place the responsibility of using it right and security on the user, and developers are not used to these new kinds of animals,” he added. “That’s why the trust can be exploited at scale.”

As it uncovered a long operational history spanning multiple campaigns, Oligo Security has gained more confidence in understanding how TeamPCP operates. It also means TeamPCP was likely involved in other attacks that haven’t been attributed to it yet or attacks that haven’t been detected. 

“There’s a lot more out there that we haven’t caught or been able to prove up until now,” Elbaz said.

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Researchers said INC ransomware, one of the most active ransomware groups globally, has been the main attacker exploiting a pair of SonicWall zero-days soon after they were disclosed last month.

The prolific ransomware-as-a-service operation wasn’t the first group to exploit the flaws, which were actively exploited for three weeks before the vendor disclosed and patched the defects July 14, but it has been the most assertive and concerning group to target and chain both vulnerabilities together for full access.

“Since public disclosure, INC ransomware has emerged as the most commonly named threat actor actively weaponizing this vulnerability chain,” Brett Deroche, director of incident response at Rapid7, told CyberScoop. “While Inc is the name driving the post-disclosure wave, we can’t attribute the full body of exploitation to INC specifically.”

SonicWall did not respond to a request for comment.

The SonicWall vulnerabilities — CVE-2026-15409 and CVE-2026-15410 — are the latest in a series of security issues confronting the vendor’s customers, including actively exploited zero-days, previously disclosed defects, and an attack last year that allowed a state-sponsored threat group to steal the firewall configurations of every SonicWall customer. 

Just last week, Huntress researchers spotted an attack spree that compromised 30 SonicWall customers in less than two days. 

Ransomware groups have taken a special interest in SonicWall. Ten of the 17 SonicWall defects added to the Cybersecurity and Infrastructure Security Agency’s known exploited vulnerabilities (KEV) catalog since late 2021 are known to be used in ransomware campaigns.

INC ransomware, which has claimed nearly 900 victims across 71 countries since it was first discovered three years ago, is just the latest financially-motivated group to target SonicWall customers. 

Researchers haven’t determined how many organizations have been impacted by the latest SonicWall zero-days, including attacks linked to INC ransomware. 

“Attribution here isn’t a single clean answer. The earliest exploitation we observed, beginning June 22, traced back to common hosted infrastructure, though those attacks were largely unsuccessful,” Deroche said. 

“INC’s confirmed activity that we’ve observed came after public disclosure, using different infrastructure and moving from initial access to ransomware deployment in short order. That’s a meaningfully different operational tempo and skill level than what we saw pre-disclosure,” he added. 

Deroche said Rapid7 has successfully prevented data theft and encryption in the majority of recent cases, yet noted ransomware was deployed in at least one case the security vendor observed.

Yet, there could be other attacks outside the purview of Rapid7’s telemetry. INC ransomware has listed multiple new alleged victims on its data leak site, including organizations and government agencies in Australia, the United States, the United Arab Emirates, Colombia and Switzerland, Resecurity said in a blog post Saturday.

The company said it has aided several victims with incident response, and learned multiple victims received emails and phone calls from alleged hackers who pressured them to engage in negotiations.

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Zero-knowledge proofs could let infrastructure operators answer key security questions without handing over the sensitive data behind their answers.

Imagine a major software flaw is discovered in equipment used across pipelines, power plants and telecom networks. The government needs to know as fast as possible which companies are exposed. But answering that question may require firms to share software inventories, network diagrams and vulnerability scans, which could become attack roadmaps for attackers if compromised. A lesser-known cryptographic concept could help solve this problem. The method, known as zero-knowledge proofs, allows companies prove a vulnerability exists without disclosing how their systems work or other proprietary information.

For more than a decade, Washington has tried to address companies’ concerns about sharing cybersecurity data. Congress has provided legal protections, and agencies have created information-sharing programs. Those efforts have helped companies exchange signs of an attack, incident reports, and defensive advice. But they’ve done much less to get companies to share data on vulnerabilities and security controls before an incident occurs.

The data that would help the most is what companies are least willing to share. A vulnerability scan can show which devices are connected, which software is running, how systems are configured and where defenses are weak. If unintentionally exposed, it would be a terrific guide for adversaries.

Another problem: Once sensitive data leaves a company, it can be stolen, subpoenaed, passed to another agency or used in a regulatory proceeding the company never expected. Industry is constantly asked to reduce security risk by creating more elsewhere.

Zero-knowledge proofs could reduce the need to disclose the underlying sensitive data. The idea is simple, even if the math is not: a company can prove that an agreed evaluation of its authorized scan data indicates that a specific software flaw is present, without disclosing its full asset inventory, network architecture or configuration data.

A computer does not read a vulnerability scan the way a person does. A security analyst might open a report, look through the devices and software versions, and decide whether a vulnerable product is present. A zero-knowledge proof turns that same evaluation into a local mathematical calculation.

For example, the government and a company could agree on a precise question: Does a specific vulnerability exist anywhere inside a defined group of systems? The company keeps its scan data inside its own network. A cryptographic tool checks that data against the agreed question, compares the software and version information against the vulnerability, and produces a proof tied to the final answer. If the scan data satisfies the agreed conditions for a “yes” result, the company cannot generate a valid proof supporting a false “no” answer under those same rules.

The government never sees the raw scan report, the device list, the software inventory, or the network map. It only receives and verifies the mathematical proof, confirming that the answer follows from the agreed rules and underlying data without exposing that data.

This isn’t theoretical. FDD’s Center on Cyber and Technology Innovation recently tested the approach with anonymized vulnerability data from three operational environments. The test asked yes-or-no questions about 38 known vulnerabilities while keeping the raw scans inside the participating environments. Results were promising: only the proofs and answers were shared, yet they revealed how widespread each vulnerability was across the environments.

The test proved the approach works, but that does not mean the government should rush to build a national system around it. A lot of work still needs to be done before agencies can rely on these for compliance, vulnerability reporting, or procurement decisions.

The next step should be structured pilot programs, not mandates. Federal cyber officials and standards bodies should test this with narrow, practical questions: whether a known vulnerability is present or whether a specific security control is in place.

Only after those pilots should agencies decide what underlying data can be trusted and what counts as sufficient proof in a regulatory setting. The Cybersecurity and Infrastructure Security Agency (CISA), the National Institute of Standards and Technology (NIST), and regulatory agencies are natural candidates to run these pilots. CISA already works with critical infrastructure operators on cyber risk, while NIST can help define what a trustworthy proof should look like before agencies try to rely on one. Regulatory agencies, meanwhile, could reduce private sector headaches by developing more secure mechanisms for companies to share compliance information.

Zero-knowledge proofs won’t solve every problem when it comes to cyber information-sharing. But they could solve one of the hardest: how to give the government a trustworthy answer without forcing companies to expose the very systems everyone is trying to protect. The government should test this concept now, while there is still time to learn, and avoid blindly entering the next cyber crisis.

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While AI is supposed to help defenders, it’s now creating more than twice as much noise as human-triggered incidents CrowdStrike detects as potentially malicious. The company’s threat hunting team and systems triaged an average of 14 million detection leads daily, resulting in about 36,000 customer alerts during the one-year period ending in June.

“AI agent-driven behaviors have surged past human triggers,” said Adam Meyers, senior vice president of counter adversary operations at CrowdStrike. “AI has driven the detections significantly above what humans are causing, and this gives you a sense of how frequently AI is being used, and really just that it’s being used everywhere.”

The threat posed by AI showed up incessantly during the past year, sparking alarming shifts and heightened targeting across software defects, open-source supply chains and AI tools themselves — all of which create greater difficulties for defenders, CrowdStrike said in its annual threat hunting report. 

“The AI tools that are being implemented by every enterprise across the globe right now are also creating an extended attack surface,” Meyers said during a press briefing. 

“AI is now a tool, a target, and a force multiplier for adversaries,” researchers wrote in the report, adding that AI-enabled malicious activity surged 89% during the past year as attackers used the technology to scale operations, hasten tradecraft and target AI infrastructure.

Attackers are using frontier AI models to uncover vulnerabilities and develop resources, including AI-generated scripts, payloads and commands that increase their effectiveness and efficiency. The technology also allows threat groups to design more creative ways to run automated attacks and boost impact by manipulating, interrupting or sabotaging AI systems and data.

“AI is both the weapon and the target,” Meyers said. 

Most organizations don’t view it as such, and thus far haven’t secured or put proper guardrails around the AI tools they use or address the ways attackers can use AI against them, he added. 

AI’s mark on vulnerabilities is particularly concerning, as reflected by what Meyers described as “one of the scarier stats” in this year’s report: 88% of vulnerabilities were weaponized through AI within 48 hours. 

“This is creating a rich ecosystem of vulnerabilities for attackers to use against various systems,” he said. It also renders the 30-day patch window obsolete, forcing organizations to struggle under a new baseline patch cycle of 24 to 48 hours, according to Meyers.

The AI ecosystem also became the next software supply chain battleground during the past year, as evidenced by TeamPCP’s rampage through open-source software in the first half of this year. 

The threat cluster compromised more than 300 software dependencies in one day, Meyers said. 

AI tools are already in the crosshairs and the attack surface will continue to grow as agentic systems, AI application integrations and dependency managers for AI agents proliferate, the report concluded.

“The same AI tools driving modern businesses are creating under-defended attack surfaces that adversaries are exploiting,” Meyers said. “We have to secure AI. This is absolutely critical.”

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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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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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SonicWall customers are attempting to dodge another security challenge as attackers are exploiting a pair of zero-day vulnerabilities that have been confirmed by the vendor. 

The company publicly disclosed the vulnerabilities — CVE-2026-15409 and CVE-2026-15410 — in a security advisory Tuesday. SonicWall credited an employee with discovering the defects, but it hasn’t said when the discovery occurred or the earliest known instance of exploitation. 

Rapid7 researchers told CyberScoop both vulnerabilities were first exploited June 22. “From the cases that our team has observed, the goal is likely ransomware, though we have prevented the actors from achieving exfiltration and encryption,” said Seth Lazarus, senior manager of detection and response services at Rapid7.

Overlapping tactics, techniques and procedures from the attacks observed by Rapid7 indicate the same threat group or attacker discovered and exploited the zero-days, Lazarus added.

SonicWall did not answer questions about the impacts of these attacks thus far, and the company hasn’t attributed the attacks to a known group or described the attacker’s origins and motivations.

The vendor did, however, confirm to CyberScoop that both vulnerabilities have been chained together for exploitation. The vulnerabilities affecting SonicWall SMA1000 appliances, including a max-severity defect that allows attackers to make authenticated requests and a 7.2-rated vulnerability that allows authenticated command injection.

“When these two are chained, an attacker can go from zero access to a complete system compromise for the affected appliance,” said Landon Rice, senior exploit developer at VulnCheck.

Ben Harris, founder and CEO at watchTowr, said two characteristics of the vulnerabilities fuel a sense of dread. “Both were exploited as zero-days before fixes were available, and together they offer a plausible path to remote-code execution from the internet,” he said.

The Cybersecurity and Infrastructure Security Agency added both zero-days to its known exploited vulnerabilities catalog Tuesday. 

SonicWall encouraged customers to patch the vulnerabilities by upgrading to the latest software version, which it released upon disclosure, and shared some indicators of compromise to help customers hunt for potential malicious activity on their systems.

“Speed of response was a priority for us,” said Bret Fitzgerald, senior director of global communications at SonicWall. “Within days of becoming aware of the issue, our team had developed a script that we can run on behalf of affected customers to assist with resolution, and mitigation efforts are already underway.”

SonicWall and third-party researchers haven’t said how many SonicWall customers are impacted by the exploited vulnerabilities, but the vendor did say it already investigated multiple cases of active exploitation. 

Fitzgerald said the company monitors about one million sensors globally and “SMA1000 appliances represent a very small subset of that footprint, less than 5,000 units.”

SonicWall said support staff are also helping customers work through instances of suspicious activity, warning that patching alone is not sufficient. 

The vendor and its customers have been hit by a barrage of actively exploited zero-days and previously disclosed defects in SonicWall devices for years. In 2025, an undisclosed state-sponsored threat actor intruded the company’s cloud environment and stole firewall configurations of every SonicWall customer. 

Seventeen defects affecting the vendor’s products have been added to CISA’s known exploited vulnerabilities catalog since late 2021. Ten of those defects are known to be used in ransomware campaigns, according to CISA, including a wave of about 40 Akira ransomware attacks between mid-July and early August.

“As always,” Harris said, “when something is confirmed as already exploited in the wild, patching is the bare minimum, and breach should be assumed.”

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Microsoft’s monthly Patch Tuesday security program reached an unrivaled pinnacle this month, as the vendor addressed 622 vulnerabilities across its suite of business products and systems. 

“The bug apocalypse has finally descended upon us,” Dustin Childs, head of threat awareness at Trend Micro’s Zero Day Initiative, wrote in a blog post Tuesday.

“The mother of all releases. To call this record-breaking is an understatement,” he added. “The CVE count year-to-date exceeds all other years’ totals.”

The startling increase in vulnerabilities reflects a compounding effect taking root across software as artificial intelligence plays a growing role in discovering and developing patches for defects lurking in error-riddled applications. 

Microsoft’s June Patch Tuesday update broke the previous all-time record with 206 vulnerabilities.

The company last week warned forewarned customers and defenders that a flood of defects would be uncovered as it applies its multi-model agentic scanning harness (MDASH) to discover and address vulnerabilities at greater speed and scale.

The monthly exponential rise in Microsoft vulnerabilities already puts the vendor on pace to break a full-year record, ending 2026 with the largest annual collection of defects, beating the previous record of 1,245 CVEs in 2020, Satnam Narang, senior staff research engineer at Tenable, said in an email. 

“It’s probable that we will not only exceed 2,000 CVEs in a calendar year, but potentially over 3,000 CVEs this year or more,” he added.

“The volume is striking, but it reflects how good these tools have become at finding bugs, not how many of those bugs actually pose a risk to organizations,” Narang said.

Microsoft disclosed two actively exploited zero-day vulnerabilities — CVE-2026-56155 and CVE-2026-56164, privilege escalation defects in Active Directory Federation Services and Microsoft SharePoint Server, respectively. 

The monthly batch of patches included 416 defects in Windows, 82 in Office and 46 in Microsoft Edge. More than 1 in 10 vulnerabilities the vendor disclosed — 63 total — were rated as critical.

“The products covered this month are also astonishing,” Childs said. “Just about everything you’ve ever heard of is getting patched.”

The full list of vulnerabilities addressed this month is available in Microsoft’s Security Response Center.

SAP also addressed a fresh assortment of vulnerabilities Tuesday, including critical defects CVE-2026-44747 in SAP NetWeaver Application Server and CVE-2026-27690 in SAP Approuter.

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AI-generated code is part of everyday software development. Developers use it to prototype, refactor, troubleshoot, and move from idea to implementation with less friction than ever before. The productivity gains are undeniable, which means that security leaders now face a hard question: whether their organizations can govern the risk that AI creates at that same speed.

That challenge is rooted in scale. AI changes how quickly software can be created, while many application security programs still depend on controls designed for a slower development model. When code generation accelerates beyond the capacity to review, test, and remediate issues, security debt accumulates faster.

That is the hidden cost of AI-assisted development. Risk now enters the enterprise at machine speed, while many organizations still manage it with human-scale processes. CISOs should govern AI-generated code as a high-risk input: tested automatically, checked for unsafe dependencies, remediated quickly, and blocked from production if it fails policy.

The metric that matters is risk velocity

Application security has long been measured through discovery. Teams count vulnerabilities, categorize severity, report trends, and show whether the numbers are improving. Those questions still matter, but AI adds a more urgent metric: risk velocity. Security leaders need to know how quickly the organization creates new software risks and how quickly it can reduce or eliminate them.

AI changes the economics of security debt. A development team that produces significantly more code without a matching increase in security capacity will create more issues than it can reasonably review or fix. Even when AI-generated code is comparable to human-written code on a per-line basis, the total risk can rise because the volume of change is higher. The backlog grows, vulnerabilities persist, and security debt eventually constrains the business.

AI expands familiar failure modes

The failure modes are familiar. AI coding tools can reproduce insecure patterns found in training data, including weak input validation, unsafe authentication flows, insecure direct object references, hard-coded secrets, and vulnerable dependency choices. They can also miss the context that determines whether code is secure in a specific environment: authorization models, tenant boundaries, data sensitivity, production configurations, and how services interact in a real application.

There is also a human factor. Under the pressure of deadlines, developers may accept code that works without fully understanding how it does so. The result is misplaced confidence. Code compiles, tests pass, features ship, and hidden risk enters the system. Over time, the organization may lose sight of the security concerns that naturally arose during manual development.

The supply-chain risk is bigger than the code itself

The software supply chain adds another layer of risk. Modern applications are assembled from open-source components, frameworks, plugins, containers, APIs, and cloud services. AI coding tools can recommend outdated packages, vulnerable libraries, or nonexistent dependencies. Veracode’s 2025 GenAI Code Security report found that AI coding tools produce insecure code nearly half (45%) of the time. It may sound like an amusing hallucination until attackers register malicious packages with similar names and wait for developers or automated tools to pull them in. At that point, a coding shortcut becomes a supply chain exposure.

AI is already part of the development lifecycle, and its use will continue to expand. Security teams need a control model built for that reality.

“Shift Left” needs an enforcement layer

The industry has spent more than a decade moving security earlier in the development lifecycle, improving visibility and helping teams catch issues sooner. Many organizations, however, moved findings closer to developers without also moving enough ownership, automation, and remediation capacity with them. Developers received more alerts, while security teams gained more visibility into risks they still struggled to reduce.

AI makes that operating gap more urgent. As software output increases, security cannot remain a checkpoint near the end of the process. It must become a continuous control system built into the way software is created, tested, approved, and deployed.

Secure-by-design has to become infrastructure

Secure-by-design in the AI era requires an engineering environment where unsafe choices are harder to make and easier to catch. Approved frameworks, secure defaults, reference architectures, dependency controls, automated testing, and policy enforcement should be embedded directly into developer workflows and CI/CD pipelines.

Remediation also must move closer to the point of creation. When a coding assistant introduces a vulnerable pattern, the ideal response is an inline fix that is proposed, validated, and governed as part of the normal development process. AI can help defenders here when it is connected to reliable security signals, policy context, and evidence from real testing. Counterintuitively, developers using AI to write code often don’t trust AI to automatically remediate code without human review. This takes one of the best ways to keep up with machine-speed created vulnerabilities and slows it down to human speed. An acceptable balance between risk and speed must be found.

Approval is not governance

CISOs should focus on governance, not just approving AI coding tools. Governance means tracking where AI-generated code enters your environment, documenting the policies and tests applied, recording what issues were found and fixed, and keeping proof of these decisions. This documentation becomes critical as AI-assisted development becomes standard. If vulnerable code reaches production, you’ll need to show that adequate controls were in place and risks were managed according to policy.

What leaders should do now

CISOs and engineering leaders should treat AI-generated code as untrusted until proven otherwise. They must require automated testing before release, enforce dependency controls, prioritize remediation based on exploitability and business impact, and measure success by the rate at which critical risk is reduced.

Additionally, boards and organizational policymakers should ask whether organizations can demonstrate that AI-assisted software is governed before it is deployed. The key evidence should include the policies applied, the tests performed, the vulnerabilities remediated, the risks accepted, and the approvals recorded. Today, many organizations can confidently track what their AI tools produce, but they cannot demonstrate how that output was secured, reviewed, and governed before reaching production. The industry is still working to close this gap.

AI is changing how quickly software risk moves through the enterprise. The organizations that succeed will make security move just as quickly by embedding governance, remediation, and proof directly into the software delivery pipeline.

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China-aligned attackers broke into the networks of U.S. and Canadian universities to steal sensitive data and establish persistent access via webshells and backdoors, Proofpoint threat researchers said Tuesday. 

The espionage-motivated attacks targeted physics and engineering departments, focusing on administrators and professors with national security links or organizations researching astrophysics and particle physics.

Proofpoint identified less than 10 university victims and estimates a few dozen universities may be impacted, Greg Lesnewich, principal threat researcher at Proofpoint, told CyberScoop. The company first observed the campaign in May and believes the campaign is ongoing. 

“There is a high likelihood that many victims have not been made aware of this activity yet,” Lesnewich added.

Researchers traced the attacks to a pair of critical vulnerabilities in Roundcube, an open-source email client, that were exploited and chained together to steal credentials and gain long-term access.

The threat cluster, which Proofpoint tracks as UNK_MassTraction, exploited CVE-2024-42009 to execute JavaScript inside the victim’s browser, then exploited CVE-2025-49113 to gain a foothold in the mailserver. 

The initial exploit in the chain only requires a victim to open an email, and the attackers sent victims a series of generic lures to trigger the initial access.

Proofpoint attributes the campaign to a China-aligned cluster because the attackers used a known covert network used by multiple China-aligned threat groups, an infection chain leading to VShell and left Chinese language artifacts in the phishing emails. 

Researchers haven’t drawn any conclusions about why attackers targeted the universities and what they are seeking. 

“We do not have data to suggest what got stolen, as we only observe the initial inbound email attempt,” Lesnewich said. 

The engineering aspects do align with China’s strategic initiatives, he added. Google threat hunters recently spotted a Chinese state-sponsored espionage group that burrowed into systems for years, stealing data across academia, medicine, military, cybersecurity and foreign policy. 

“China-aligned adversaries have been targeting other types of edge devices such as routers and VPN concentrators for years with various exploits to create a foothold into a target network, not using email for delivery,” Lesnewich said. “This campaign flips that on its head, using email to deliver an exploit chain to compromise a mail server, instead of using email to deliver a credential harvesting URL or malware to target an end user, not a server.”

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I keep hearing the same frustration when I talk with security leaders. The real problem sitting on their desk isn’t finding vulnerabilities. It’s deciding which ones actually matter.

The industry has spent billions on better visibility. We’ve convinced ourselves that if we could just discover more vulnerabilities, collect more data, and ingest more threat intelligence, we’d become more secure. But look around. Organizations still aren’t more secure. They’re just overwhelmed.

Then AI arrived and changed the game entirely. In a matter of months, vulnerability discovery accelerated dramatically. AI systems review code faster than human researchers ever could. They identify weaknesses at unprecedented scale. They scan continuously without the limitations of time, staffing, or attention. The headlines have been everywhere. Government leaders are reevaluating AI laws. CEOs are tossing and turning at night.

But we are focusing on the wrong issue. None of this is actually solving what matters most.

For years, security teams have been drowning in findings. Every new threat feed promised greater visibility. What arrived instead was noise—more data, more alerts, more dashboards, more vulnerabilities. Rarely clarity. Now AI is pouring gasoline on that fire.

The conversations around AI in cybersecurity often get stuck in the wrong place. People debate whether it will help defenders move faster or enable attackers more easily. Both matter, but they are not the core issue.

The real consequence of AI is that it’s exposing something organizations have avoided facing. A vulnerability is not risk, it’s just a clue. Risk emerges when information connects to context: how critical the affected asset is, what controls surround it, how likely exploitation is, the business processes it supports, and what happens operationally if it fails.

Without that context, prioritization becomes impossible. Resources get spent on low-risk issues while mission-critical, vulnerabilities sit unfixed.

Now AI is now making the data volume problem almost impossible to comprehend. An enterprise working with hundreds of software vendors, cloud providers, contractors, and technology partners must investigate every relationship. It’s like a cybersecurity nesting doll where AI continuously identifies vulnerabilities across that entire ecosystem, every minute of every day.

The real challenge today isn’t discovering weaknesses. It’s determining which of tens of thousands of newly discovered weaknesses could actually disrupt operations, impact customers, halt revenue, or create regulatory exposure. Most organizations can’t answer that question quickly.

Some still rank risk using severity scores built for technical teams rather than business leaders. Others rely on manual triage that was already struggling before AI. Many still measure security maturity by how many findings they identify rather than the speed and accuracy of their decisions.

These approaches don’t work anymore. They probably didn’t work yesterday either.

What’s uncomfortable to acknowledge is that AI isn’t creating a cybersecurity crisis. It is revealing one that’s existed for years. The organizations that succeed in this an AI world will transform discovery into judgment faster than their competitors. When AI can find nearly every weakness, security belongs to those who know what to act on. It belongs to those who can connect data to business reality.

That’s the real edge. That’s what separates secure organizations from those that are just collecting more findings.

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Citrix published a security bulletin Tuesday disclosing six vulnerabilities in NetScaler ADC and NetScaler Gateway appliances, including a high-severity memory disclosure flaw that researchers say belongs to a vulnerability class first identified in the 2023 incident known as CitrixBleed.

The company rated the overall bulletin severity as high and assigned CVSS scores ranging from 6.9 to 8.8 across the six CVEs. Citrix said customers should install the updated builds and, in one case, manually adjust a configuration parameter even after patching.

The most closely scrutinized of the vulnerabilities, CVE-2026-8451, was discovered by researchers at watchTowr, a cybersecurity firm that has published several prior analyses of issues in NetScaler products. According to a technical writeup the firm released alongside Tuesday’s disclosure, the vulnerability stems from how NetScaler parses SAML authentication requests when an appliance is configured as a SAML identity provider, a deployment mode commonly used for single sign-on.

WatchTowr researcher Aliz Hammond wrote that the firm found the flaw in late March while reproducing a separate vulnerability, CVE-2026-3055, that Citrix disclosed earlier this year. That March flaw was added to CISA’s Known Exploited Vulnerabilities catalog after researchers and the agency confirmed active exploitation within days of disclosure. The new flaw shares a root cause with the March bug: both involve out-of-bounds memory reads triggered by malformed SAML requests sent to NetScaler’s authentication endpoints.

“Referencing what we wrote previously, because it is demonstrably evergreen: ‘However, what should be of concern is the bigger picture – the trend, which is very clearly suggesting that memory management continues to appear fragile within Citrix NetScaler appliances, to the extent that even accidentally misconfiguring an appliance can lead to the disclosure of leaked memory,’” Hammond wrote in the report. 

The bulletin also discloses five additional vulnerabilities affecting different NetScaler subsystems. Two involve memory overflow conditions that could cause denial-of-service outcomes. A separate flaw could allow unauthenticated arbitrary file reads on appliances where management access is exposed on certain network interfaces. Another concerns memory overread triggered through TCP timestamp handling. The sixth involves a denial-of-service condition tied to malformed HTTP/2 requests, which requires an additional manual configuration change to fully fix, since the relevant timeout parameter defaults to a value that leaves the underlying condition unaddressed unless administrators set it explicitly.

Along with Hammond, the bulletin credits Michael Tucker of the XOR team at JPMorgan Chase and Maxim Suhanov for finding the vulnerabilities. 

The NetScaler product line has accumulated more than 20 entries in CISA’s KEV catalog over the past three years, including multiple flaws that have been weaponized in ransomware campaigns. As of Tuesday, the latest vulnerability had not joined that list — neither the vendor bulletin nor watchTowr’s writeup cited confirmed exploitation at the time of disclosure.

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Microsoft addressed a whopping 206 vulnerabilities lurking in its vast portfolio of business products and foundational systems in this month’s Patch Tuesday update, marking the vendor’s largest monthly batch of security patches on record, according to researchers.

The massive assortment of vulnerabilities in Microsoft’s latest defect dump accentuates an alarming trend across technology — fears and warnings about a roaring flood of error-riddled software have materialized. And the disease is spreading. 

“It is extraordinary that Microsoft can produce so many patches in a single month, but it does raise concerns,” Dustin Childs, head of threat awareness at Trend Micro’s Zero Day Initiative, wrote in a blog post Tuesday.

Researchers consistently highlight the role artificial intelligence is playing in discovering more vulnerabilities and aiding in the development of patches and testing. Childs isn’t alone in wondering if this is the new normal and how that will impact defenders’ strategies for patch prioritization and deployment. 

“Pandora’s proverbial box has been opened, and as more advanced AI models become available, we expect the norm to continue upward across the board, not just for Patch Tuesday,” Satnam Narang, senior staff research engineer at Tenable, said in an email.

This vulnerability flood isn’t a one-off or rare event. Half of Microsoft’s Patch Tuesday updates through the first half of this year contained a volume of defects well into the triple digits. 

“The current number of CVEs shipped by Microsoft this year exceeds the total number of CVEs shipped in all of 2018,” Childs wrote. 

Microsoft disclosed three vulnerabilities — CVE-2026-45586, CVE-2026-50507 and CVE-2026-49160 — that were publicly known at the time of release, but not yet exploited in the wild, according to the company. 

Yet, in an out-of-band update May 19, the vendor did disclose and release a patch for CVE-2026-41091, an actively exploited zero-day vulnerability affecting Microsoft Defender.

Microsoft disclosed one max-severity vulnerability — CVE-2026-48567, affecting Azure HorizonDB — and nine defects with critical CVSS ratings. The company designated 15 of the vulnerabilities it addressed this month as more likely to be exploited.

The full list of vulnerabilities addressed this month is available in Microsoft’s Security Response Center.

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Cisco customers are confronting yet another actively exploited zero-day vulnerability affecting the vendor’s SD-WAN management software, reinforcing pressure on organizations that have experienced rare breaks from active threats this year.

The vulnerability — CVE-2026-20245 — marks the seventh actively exploited zero-day in Cisco SD-WANs this year.

Cisco said it first became aware of active exploitation of the latest defect in the network management software earlier this month. The company disclosed the vulnerability, which was first spotted by Mandiant, on Thursday and warned that a security patch is not yet available and there are no workarounds to mitigate the defect in the meantime.

“A patch for this vulnerability will be provided on a future date,” a company spokesperson said in a statement. 

Cisco did not attribute the attacks to any specific group, describe the objectives of those attacks or share how many organizations have already been impacted.

The validation error defect affecting the Cisco Catalyst SD-WAN Manager allows authenticated or local attackers to execute commands as root, resulting in command-injection attacks on an affected system, the company said.

Yet, the scope of potential impact may be limited because exploitation requires valid credentials or privileged access through other means. Cisco said exploitation of a pair of zero-days it disclosed earlier this year —  CVE-2026-20182 or CVE-2026-20127 — could allow attackers the access required to exploit the new vulnerability. 

The company said it is “not aware of successful exploitation by other means,” adding that it “observed limited cases where the exploitation of this bug resulted in a configuration change pushed to edge devices.”

Landon Rice, senior exploit developer at VulnCheck, said the need for existing privileges “makes an attacker heavily reliant on previous vulnerabilities, or a net-new initial access vector, in order to be able to reach the privilege escalation path.”

Cisco advised customers to upgrade to fixed software released in May as part of its response to CVE-2026-20182 as a protective measure. 

Absent a patch that would provide organizations more protection against the new vulnerability, Cisco provided some indicators of compromise but noted that those same log entries may occur during standard operations. The company encouraged customers that need help distinguishing between legitimate and malicious activity to contact Cisco Technical Assistance Centers.

Cisco isn’t the only security vendor facing an onslaught of attacks on its customers, but it is among the most heavily targeted. The Cybersecurity and Infrastructure Security Agency has added seven vulnerabilities affecting Cisco SD-WANs and firewalls to its known exploited vulnerabilities catalog this year, not including CVE-2026-20245, which has yet to be added to the catalog.

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Troy West was in Warsaw when his dinner was interrupted by his phone. But he was happy about it.

West, associate director of cybersecurity for autonomous offensive security company XBOW, had just learned that a trial version of the company’s platform had found a vulnerability that led to a full takedown of a development environment used by Moderna, the pharmaceutical company primarily known for its work related to mRNA vaccines.

It was, by most measures, exactly the kind of outcome a security team dreads. But for West and Farzan Karimi, Moderna’s deputy CISO, it was something closer to a proof of concept. XBOW’s product had done in hours what a human penetration tester could not — and it had done so with a level of persistence and creativity that neither of them had fully anticipated.

The episode is one data point in a much larger shift now rippling through the cybersecurity industry: The artificial intelligence models discovering vulnerabilities are moving faster than the teams that have to patch them.

Across recent conversations and presentations, industry experts said the tools are getting sharper, the attack surface is getting larger, and the gap between finding a problem and fixing it is not closing fast enough. For now, most organizations are caught between the speed of discovery and the slowness of remediation, with vendors across the industry rushing to position their products as the way through.

A shift in scale 

The inflection point came with Claude Mythos. When Anthropic announced the highly guarded model, security executives at major enterprise technology companies took notice in a way they had not with prior frontier releases. 

Zscaler was among the early organizations given access to the model, and CEO Jay Chaudhry told CyberScoop that he directed his team to use it to probe the company’s own applications.

“Are we finding some serious stuff? Yes, indeed,” Chaudhry told CyberScoop at Gartner’s Security & Risk Management Summit. He was careful to note that the findings were not necessarily more severe than those produced by other models. The issue, he said, was volume. 

“There aren’t enough resources and cycles to fix all those,” he said. 

The reason Mythos changed the calculus, according to Tom Gillis, general manager for infrastructure and security products at Cisco, comes down to code complexity. Legacy network infrastructure was built on tens of millions of lines of code developed over decades, and earlier AI models lacked the context window and reasoning capacity to comprehend it in full.

“The models couldn’t understand the entirety of it before,” he told CyberScoop. “Now they can. That’s why they’re finding all these vulnerabilities.”

The problem runs deeper than application code. Firewalls and network switches often run for decades without updates or reboots, and many have never been patched in any meaningful way. The combination of aging infrastructure and newly capable AI models has created what Gillis described as a meaningful and accelerating shift in attacker capability that the industry’s existing operational rhythms were not built to absorb.

An opportunity in existing technology 

Cisco’s answer to the oncoming vulnerability deluge is a technology it calls Live Protect, a compensated control built on eBPF, a Linux feature that lets security software operate at the kernel level to block threats without rewriting system code.

“It’s a pinpoint, laser-fine control that can shield a vulnerability on a production system,” Gillis said. “We’re not touching or modifying the binaries of that production system.”

The intent is to shrink the window between discovering a vulnerability and the next scheduled patch, allowing IT teams to fix issues without taking systems offline.

“This is a finger in the dike that plugs a hole until you get to new change control windows,” he said, acknowledging that some customers may be tempted to treat the shields as a permanent solution. 

The product has been shipping since October, but customer urgency shifted noticeably after Mythos. “Customers are like, ‘Oh, good story, Tom. I’ll think about it.’ Now it’s like, ‘Oh my God, turn this thing on right now.’”

He also noted that eBPF is open source, and said he expects the broader industry to follow. 

“While I’m very proud of Cisco leading the market with these compensated controls, I know my competitors have to do this.”

The bot that broke everything 

But shielding vulnerabilities only works if you know they exist. Karimi, the Moderna deputy CISO, faced a different problem: His vulnerability management system was surfacing hundreds of high-severity findings with no reliable way to know which ones an attacker could actually exploit. His team had skilled red-teamers, but they were finite resources. What he needed was something that could test continuously, everywhere.

“We have some very senior red-teamers and pen-testers in our organization that are pointed in a specific direction,” Karimi said during a presentation at the Gartner summit. “XBOW is covering different attack stories for us.”

West, who leads offensive security for XBOW, describes the platform as a response to a structural problem in how offensive security has traditionally worked. Human testers scope an engagement, run it, write a report, and move on. The window between tests is where risk accumulates.

“Historically you have exploit developers spending time finding the right vulnerabilities, writing the exploits, finding if those exploits are reachable, and then finding a way to chain them all together,” West said. “That takes a long time.”

Given the realities, Karimi decided to put XBOW through a trial, which produced two notable findings.

In the first, XBOW identified a web application firewall bypass on a company application built on the Spring Boot framework. The bypass involved encoding a single character (a capital “A”) as its percent-encoded URL equivalent (A), which the WAF interpreted as a legitimate request, allowing the bot unfettered access. 

The second finding, which was the cause for West’s dinner interruption, was more consequential. West had provided XBOW with access to the source code of an internal application called Orders, used by Moderna’s research partners to procure drug substances, but no login credentials. The platform identified a valid API key embedded in the source code, used it to authenticate, and then began probing the application’s APIs for SQL injection vulnerabilities.

What happened next was not entirely planned. One of those APIs handled a malformed SQL injection attempt in an unexpected way, dumping garbage data into a shared routing application that other services depended on.

“Not only was it able to kick that Orders app I showed you, but it somehow kicked over the entire ecosystem of apps,” West said.

Human pen-testers who reviewed the findings afterward confirmed they were valid, and said they would not have found them on their own. Karimi said despite the outage, his team recognized the value immediately.

“If we’re able to demonstrate where you could have an outage in a safe testing environment, that’s a great signal,” he said.

The broader value, Karimi argued, is in forcing prioritization when bugs are discovered. “If you have exploit proofs, you can provide that plus-one modifier and really point your developers to remediate the top tier of real risk that’s been validated.”

But he does worry about the volume of bugs that will be surfaced by these tools. 

“How do we now handle the volume of bugs that have gone up due to AI-driven scale?” he said. “That’s a whole other problem space.”

A broader reckoning

Across these conversations, a consistent theme was that even as defenders are trying to get arms around the forthcoming wave of bugs, it’s going to be a tremendously uphill battle. That mirrors what some of the industry’s top leaders have been saying for months. 

It also mirrors what the model developers themselves have consistently been warning about. In its announcement about expanding access to Mythos, Anthropic admitted the timeline for a publicly available tool similar to its cybersecurity-focused model is shortening, and there are no guarantees it will be released with safeguards. 

“In that world, cyberattacks could occur much more often, and in much more unpredictable forms,” the blog post reads.

Gillis was blunter about what happens to organizations that don’t move. 

“Some people will be slow to change,” he said. “But the consequence of not making that change is gonna be front-page news. It’s a massive, massive compromise. You know, like, ‘you gave up every credit card number.’ Bummer.”

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A Department of Commerce inspector general report released Thursday found that the National Institute of Standards and Technology has mismanaged a critical cybersecurity vulnerability database through poor planning, inefficient operations, duplicate federal programs, and failure to communicate with users.

The National Vulnerability Database, maintained by NIST since 2005, collects information about computer security flaws and adds details like severity ratings and affected products. This information helps cybersecurity professionals across government and the private sector decide which security problems to fix first. In February 2024, the database’s enrichment contract lapsed, creating a backlog of unprocessed security flaws that has only grown worse.

The report identified the lack of strategic planning as a core problem. NIST leaders admitted they had no long-term plan for clearing the backlog, even as it grew from about 13,000 unprocessed security flaws in June 2024 to over 27,000 by the end of 2025.

NIST publicly promised in May 2024 that it would clear the backlog by September 2024, setting a goal of processing 6,200 security flaws per month, but the agency had never processed more than 5,000 per month in the past.

The report found major inefficiencies in how NIST enriches the information that is attached to the vulnerabilities. 

Analysts spend about 80% of their time on two tasks: calculating severity scores and identifying which products are affected. The inspector general’s office tested NIST’s severity scores and found they matched independent evaluators only 12% of the time. Also, nearly 80% of vulnerability submissions already include these scores from the companies that are responsible for the software. This means NIST is doing work that is often unnecessary and inconsistent. The inspector general proposed cutting back on severity score calculation work over the next two years, estimating that NIST would save $800,000 that it could redirect to other program areas.

Another efficiency problem highlighted is the program’s manual process for identifying affected products. Creating these standardized product identifiers takes a lot of time and keeps analysts from clearing the backlog. NIST is developing tools to make this faster, but it remains a major slowdown.

The report also found major duplication between two federal security programs. When the Cybersecurity and Infrastructure Security Agency launched its own Vulnrichment program in May 2024, there was no coordination between the agencies, leading to NIST analysts sometimes repeating work that CISA analysts had already completed. Additionally, the two agencies even hired the same contractor for portions of the same work. The inspector general found at least 21,000 cases of duplicated work between May 2024 and December 2025, wasting approximately $200,000 in the process.  

Communication failures have made the problems worse. In April 2024, over 50 cybersecurity professionals sent an open letter to Congress complaining that NIST was not being transparent about the database’s problems. Neither NIST nor the Department of Commerce answered the letter.

Vulnerability database programs managed by the federal government have been a point of contention for the cybersecurity community over the past two years. Earlier this year, NIST announced that it has narrowed its priorities for the NVD, focusing only on vulnerabilities in CISA’s KEV catalog, software used by the federal government, and critical software identified under Executive Order 14028.

A similar program that serves as a catalog of known security flaws, the Common Vulnerabilities and Exposures (CVE) list, has had similar issues over the past few years. That program, run by CISA, narrowly escaped a sudden demise when a last-minute, 11-month contract extension averted a shutdown in April 2025. Since then, several competing databases from European nonprofits and other private entities have been stood up in order to better coordinate how vulnerabilities are tracked, disclosed, and ultimately patched.

The inspector general recommended that NIST create a long-term plan for the database, set up a plan to clear the backlog with specific goals, cut back on unnecessary severity score work, make it easier for outside companies to help identify affected products, immediately start working with CISA to stop duplicating work, and develop a plan to communicate better with users.

NIST agreed with all six recommendations and said it is working on them. The agency must submit a plan showing how it will address these problems by late July.

You can read the full report here. 

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Anthropic said its month-old Project Glasswing initiative has uncovered more than 10,000 high- or critical-severity software vulnerabilities across systemically important code, a finding the company says has shifted the central problem in cybersecurity from discovering flaws to verifying and patching them.

The findings, drawn from partner reports and independent evaluations, mark one of the first large-scale accountings of what a frontier AI model can do when pointed at widely used code, and of the bottlenecks that emerge once it does.

Several partners reported that their rates of bug discovery had increased more than tenfold. Cloudflare identified 2,000 bugs across its critical-path systems, including 400 rated high or critical, with a false-positive rate the company said it considered better than that of human testers. At one unnamed partner bank, the model was credited with helping detect and prevent a fraudulent $1.5 million wire transfer initiated after a customer’s email account was compromised and followed up with spoofed phone calls.

External evaluations cited in the update tracked with the results Anthropic released. The United Kingdom’s AI Security Institute found that Mythos Preview was the first model to solve both of its cyber ranges — simulations of multistep cyberattacks — from end to end. Mozilla said it found and fixed 271 vulnerabilities in Firefox 150 while testing the model, more than 10 times the number found in Firefox 148 using an earlier Anthropic model. AI-powered security platform XBOW called the model a significant step up over existing systems on its web exploit benchmark.

Anthropic also used Mythos to scan more than 1,000 open-source projects. The model has flagged 23,019 potential vulnerabilities, 6,202 of them estimated as high or critical. Of 1,752 high- or critical-rated findings reviewed by six independent security research firms or by Anthropic itself, over 90% were confirmed as valid, and over 62% were confirmed to be high or critical.

The company did note that while it’s good at finding vulnerabilities, there is still a gap in having people fix every issue. 

“The bottleneck in fixing bugs like these is the human capacity to triage, report, and design and deploy patches for them,” the report states. 

Open-source maintainers have also been contending with a wave of low-quality, AI-generated bug reports, and Anthropic said it tries to reproduce and assess each issue before reporting it. At maintainers’ request, it has sometimes disclosed bugs without further vetting, reporting 1,129 such cases, of which the model estimated 175 to be high or critical.

Anthropic said it has not released Mythos-class models publicly because no company, including itself, has developed safeguards to prevent serious misuse. In the interim, it has released Claude Security in public beta for enterprise customers, which it said has been used to patch more than 2,100 vulnerabilities in three weeks using the publicly available Claude Opus 4.7, and has begun a Cyber Verification Program for security professionals.

The company said it plans to expand Project Glasswing with additional partners, including U.S. and allied governments, before any broader release of the underlying model.

“Glasswing helps the most systemically important cyber defenders gain an asymmetric advantage. However, there is an urgent need for as many organizations as possible to shore up their cyber defenses,” the report states. “We hope that our generally available models, and the new tools, resources, and research we’re providing to accompany them, will support those organizations to improve their cybersecurity posture.”

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Attackers couldn’t get enough of the vulnerabilities at their disposal last year, making exploits the top initial access vector across more than 22,000 breaches Verizon analyzed in its latest Data Breach Investigations Report released Tuesday.

The massive annual study uncovered a surge of exploited vulnerabilities during a one-year period ending in October 2025. Exploited defects accounted for 31% of all known initial access vectors, jumping from 20% the previous year. 

The uptick in exploited vulnerabilities is a reflection of the “sisyphean cause” of vulnerability management, researchers wrote in the report. “Put quite simply, there are often too many vulnerabilities and not enough time for patching all of them.”

Organizations are struggling to keep up with the torrent of vulnerabilities affecting technology across their systems. This slide is especially worrisome, and declining, among defects in the Cybersecurity and Infrastructure Security Agency’s known exploited vulnerabilities catalog.

Only 26% of the critical vulnerabilities in CISA’s catalog were fully remediated by more than 13,000 organizations Verizon studied in 2025, marking a drop from 38% the year prior. 

“There is also a worse result for the median time elapsed for a vulnerability to be fully patched by detection,” researchers wrote in the report. “Our new median time is 43 days, almost two weeks longer than last year’s 32 days.”

Verizon also noted that the median number of KEV vulnerabilities that organizations had to patch jumped from 11 in 2024 to 16 in 2025.

CISA’s KEV catalog contained more than 1,500 CVEs as of February, and 65% of those were exploited during the previous year, according to the report.

Verizon identified the five most common weaknesses of CISA KEV CVEs in its report as out-of-bounds read, heap-based buffer overflow, use after free, external control of file name or path and access of resource using incompatible type.

Attacker motivations remained relatively consistent last year, with financially-motivated cybercriminals accounting for 88% of all breaches. Espionage-driven attacks from state-affiliated groups made up the remainder.

“Ransomware continues to be among the most disruptive and impactful types of breaches we see. Not unlike the price of everything from fast food to adult beverages in ballparks, it continues to trend upward,” researchers wrote in the report.

Ransomware accounted for 48% of all breaches last year, up from 44% in 2024. Yet, Verizon observed some positive trends in ransomware as well.

Ransom payments continued to decline, with 69% of victims reporting they didn’t pay, and the median payment slid from $150,000 in 2024 to almost $140,000 last year.

Tracking ransomware remains a challenge for researchers and authorities. 

“There is a growing disconnect between what is being reported and the reality of what has occurred, in no small part due to threat actors reusing old breaches, reposting breaches from other criminal partners and making up breaches out of whole cloth to help increase their notoriety in the criminal world,” Verizon wrote in the report. “We’re beginning to think that these cybercriminals might not be entirely trustworthy.”

Yet, despite the lack of indisputable data on ransomware activity, researchers concluded: “Ransomware is still the yoga pants of cybersecurity — ubiquitous, stubbornly popular and appearing in unexpected places near you.”

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