The past few weeks have “felt very strange” for Juan Andres Guerrero-Saade.

Like many, he is trying to sort through the spate of frontier-model AI agents from OpenAI, Anthropic, Meta and others hacking their way onto the open internet over the past few months, particularly amid the already-heated national debate around the emerging technology and its impact on society.

Guerrero-Saade, a fellow for AI and security research at SentinelOne and an adjunct professor at Johns Hopkins University, said the hacks are worth taking seriously, but at a time when businesses and open-source maintainers should be focused on further hardening their systems and policymakers should be discussing new solutions,  “what we see is cybersecurity being used essentially as an excuse for these AI doomer arguments.”

The incidents have spawned those “doomer arguments” amid an intense public debate about the technology, the pace of industry development, and whether government and the private sector are doing enough to protect against “doomsday”-type scenarios, where AI systems take over or attack large parts of the internet or society.

Guerrero-Saade is among a growing chorus of cybersecurity professionals who say that while AI systems pose real, unique threats to our systems, the apocalypse is far from inevitable. Most of the public concerns around the incidents, let alone worries about killer AIs attacking critical infrastructure, assuming control of the internet and wiping out humanity, are either technically impossible or can largely be controlled through established cybersecurity principles.

There is this “narrative or magical thinking of ‘Well, AI is going to be able to hack everything, and therefore it can control everything, and therefore it’s going to kill us all,’” he told CyberScoop. “And you [think] these just don’t add up. They’re not very well-reasoned arguments.”

This fatalistic narrative tied to AI’s eventual dominance doesn’t hold up under scrutiny, according to experts CyberScoop spoke with. In recent conversations, cybersecurity and national security professionals raised questions about both the technical solutions OpenAI and Anthropic use to contain their models, as well as the glaring absence of federal oversight from federal regulators or truly independent third-party review.

For example, Jacob Coxon, an Anthropic employee who resigned over AI safety concerns, told CBS News that frontier models could not be “unplugged” by humans once deployed because the model would copy itself to thousands of other computers connected to the internet.

By contrast, Matt Tait, a former information security specialist at UK signals intelligence agency Government Communications Headquarters (GCHQ), pointed out that the models run by Anthropic and other frontier companies require extremely expensive, “ultraspecialist” machines that “are functionally supercomputers.”

“There is a zero chance that Anthropic’s most capable models will be able to extract their own model and run in the wild, because those supercomputers essentially only exist in datacenters,” Tait said.

“Not a credible warning”

Other former cybersecurity government leaders say the agentic hacks represent a failure by regulators and industry to deploy known technical and policy options that make it harder for these types of incidents to occur.

Matt Hartman, former deputy executive assistant director for cybersecurity at the Cybersecurity and Infrastructure Security Agency, said “we should not accept harmful AI behavior as inevitable or unmanageable.”

“There are meaningful steps companies can take to monitor agent activity, constrain permissions, detect anomalous behavior, and build stronger safeguards into how these systems operate,” said Hartman, now a chief strategy officer at Merlin Group. “Those controls will inevitably involve trade-offs in capability and speed, but that’s a familiar cybersecurity challenge. Our goal should be to manage the risk without unnecessarily limiting the enormous benefits AI can provide.”

Ciaran Martin, former head of the UK’s National Cyber Security Centre, took issue with the way the CEOs of frontier AI companies have framed the threat of “rogue” AI behavior as inevitable, while issuing dire warnings about future threats and capabilities with little transparency.

Martin’s comments came after an essay published by Anthropic CEO Dario Amodei that cited the threat of a HuggingFace-style swarm of agents that could create a botnet capable of “taking over the entire internet” within 6-12 months.

This, Martin said, “is not a credible warning,” because it doesn’t explain how the exploitation would function, how such a botnet would persist on the internet, or how it would escape law enforcement. 

 “It assumes no monitoring of systems, no anti-virus, no DDoS protection, no network segmentation, no incident management, no nothing of any kind of the cybersecurity on the global Internet of the type that has developed over the last 30 years,” wrote Martin. “For a claim of this magnitude, there is neither evidence for the contention nor a credible account of a path to this outcome.”

Meanwhile, some federal government cybersecurity leaders have touted the technology’s disruptive potential and called for more widespread adoption of AI tools by defenders.

Joseph Alm, assistant secretary of cyber, infrastructure and risk resilience at the Department of Homeland Security, said classified systems may retain stronger protections. But for most other data, AI models are “just going to know things and be able to infer things about the world, and we’re going to have to adapt to that as almost inevitable.”

Asked by CyberScoop whether the government or frontier AI companies could be doing more to prevent or deter their models from carrying out unauthorized hacks via agents, Alm cited recent efforts by the Trump administration this year to establish pre-release testing of commercial models as a step in the right direction. But he called unauthorized AI agent hacks “a new threat class” that is different from previous threats and can be easily distributed to users through open-source software today.

“I think what we can do is…encourage the building of good sandboxes, so that the best models aren’t used for this and the stuff you see out in the wild is the kind of detritus that you can actually respond to effectively and control your networks,” said Alm.

Other experts have shared similar concerns. Earlier this month, CrowdStrike CEO George Kurtz recently warned of a new threat class emerging alongside nation-states, cybercriminals, and hacktivists: “the agent state.” By pairing AI systems with small human teams, these operators can now match the speed, scale, and sophistication of government-backed hackers.

“It took a nation to fund the talent, the tooling, the infrastructure, the patience,” said Kurtz. “That scarcity is over.” 

To be sure, frontier AI companies tout their commitment to both approaches. OpenAI and Anthropic have rolled out an array of cybersecurity partnerships, external red-teaming programs, vulnerability disclosure programs and cybersecurity technical advisory bodies filled with cybersecurity experts.

Mohammed Husain, strategic delivery lead for government at OpenAI, told CyberScoop that the company deploys both internal safety guardrails for their models and relies on outside cybersecurity vendors for additional expertise.

Internally, OpenAI focuses on vulnerabilities at the training level: filtering data poisoning attacks, blocking harmful datasets, and using network controls to prevent prompt injections. For other security layers like sandboxing, identity management, networking controls, they outsource to external vendors. 

“I don’t think OpenAI has all the answers here but what we do as a research lab is we’re going to focus on levels of protection we have expertise in and we partner to self-complement,” said Husain.

AI safety vs. AI cybersecurity

In response to the HuggingFace hack, OpenAI and Anthropic have allowed third-party organizations, such as nonprofit AI research firms METR and Redwood Research, to investigate. But multiple cybersecurity professionals told CyberScoop that both firms lack incident response experience and focus primarily on AI alignment and safety. Their reporting on the hack also lacked critical details: network monitoring logs, telemetry, and other data standard in cybersecurity threat intelligence reports.  

METR president Chris Painter addressed those general concerns in a post on X, saying since 2022 the organization has worked with Google, Anthropic, OpenAI, Meta, Amazon and others on investigations and third-party evaluations. Painter said none of the AI companies fund METR and that his employees are not uniformly “doomer” or “accelerationist” around AI.

Painter also said METR’s work ensures that if AI systems become autonomous or “rogue” within a company, there are ways to share that information with governments and people “outside the company’s walls.”

“We don’t accept money from frontier AI companies,” wrote Painter. “They haven’t paid us for our work, and we don’t accept donations from them or their employees. As we’ve shared previously, multiple frontier AI companies currently provide us with free access to their models in order to perform our evaluations, research, and engineering.”

AI safety and AI cybersecurity advocates take different approaches to securing “rogue” AI behavior. Safety advocates focus on aligning models around ethical training and behavior. Cybersecurity advocates argue that technical and regulatory controls must go further—actively preventing models from accessing what they need to carry out malicious behavior.

Guerrero-Saade said sandboxes in particular can easily be programmed with aggressive cybersecurity monitoring in order to spot when something odd may be happening and react in real time.

“I can’t think of an easier situation in which to set up trip wires, set up configurations like DNS servers, just different parts where you can say ‘Hey, anomalous behavior is happening,’” he said. “We should have been able to tell this immediately, not weeks and months later. So watching [the AI hacking incidents] go down is a little ‘crazy-making’ because we’re seeing things that, frankly, look like neglect, negligence, people just mishandling things, and then being told that these are categorically new incidents that mean that AI systems need to be treated completely different from anything that’s come before.”

While cybersecurity experts say AI systems are, at their core, still software, they do operate differently from more traditional code in ways that can make them harder to predict and control.

John Hultquist, chief analyst at Google’s Threat Intelligence Group, said most software has been deterministic. It may have bugs or vulnerabilities, but an expert could generally understand how it would react to certain stimuli, making it easier to design straightforward controls.

AI models are non-deterministic, with far more variability than traditional software. That can break security controls that rely too much on predicting behavior in advance. Using AI to enforce security controls on other AI models faces the same problem: the systems being deployed to control AI are just as unpredictable. 

But people are also non-deterministic, and people have developed systems in other industries and practices to account for that.

Hultquist drew on his Army experience, noting that “they give incredibly dangerous, expensive things to 18-year-olds” and expect responsible use. The military manages this through two types of controls: deterministic ones like strict weapons and ammunition protocols, and non-deterministic ones like human officers who monitor and correct violations.

Similarly, established cybersecurity controls have been used by incident responders to detect and prevent or mitigate ongoing cybersecurity breaches.

“I don’t think we should throw out all the other tools that we have learned to use as well. I think that would be utterly foolish,” he said, later adding “I will say that if we use only non-deterministic tools to figure out when things are happening, we shouldn’t be surprised when we get the wrong answer.”

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Researchers say they have discovered thousands of malicious software packages uploaded to an online public software repository that were left by a “swarm” of OpenAI agents.

According to an incident timeline published Friday by researchers Spencer Kitts, Thomas Larsen and Sydney Von Arx, the campaign began May 5 when they observed a handful of suspicious packages being uploaded to RubyGems, a public library for the Ruby programming language. By May 11 and 12, the site saw more than 2,000 malicious uploads from the same actors before RubyGems maintainers halted new user sign-ups for four days to stop the flow.

In one instance, the agents attempted to exploit a very recent vulnerability that had only been discovered this past July that would have given them access to RubyGem user API keys. According to Colby Swandale, the technical lead at RubyGems, the flaw involved an improper cache configuration. While initial access logs showed no evidence of malicious key use, Swandale acknowledged the review was limited in scope and inconclusive. 

According to the report published Friday, the agents also used “disposable” email addresses and exploited another bug in RubyGems platform (since patched) that allowed them to register new accounts and gain API keys without verifying their email address.

The researchers said their understanding, based on discussions with “people in the RubyGems community,” is that OpenAI had yet to disclose the involvement of their agents in the May campaign.

An OpenAI spokesperson told CyberScoop that the company is aware of the incident and said they were in contact with both the researchers and RubyGems to conduct a broader review. The company characterized the episode as “benign,” describing it as routine training runs where agents attempt to access publicly available data.

“Based on our review, our agents used the RubyGems platform to access the internet to carry out benign tasks and retrieve public information,” the spokesperson said. “We’ll continue to investigate as part of our broader review of agent activity during training and evaluation.”

In many ways, the agents were not subtle about their identities or goals.

Days into the campaign, researchers noticed that some of the packages had “oai” in their filenames, while fifteen of them had “oai” set as their author and another listed the email “openaixyz65947@gmail.com” as their point of contact.

They also “clearly regarded what they were doing as hacking,” naming some of their files “hack.rb,” “evil.rb,” “inject.rb” and “exploit.rb.” Other packages were given names like “pwnp999,” “exfiltestwand3,” and “hacksvn,” and comments referring to things like a “malicious probe” or “#hack” are present through the files.

They also said the actors’ behavior was extremely similar to another incident revealed earlier this month where OpenAI agents flooded a German wiki  with thousands of hacking-related posts. OpenAI has confirmed their agents were involved in that incident.

The RubyGems campaign used some of the same retrieval methods as the German Wiki agents, while thousands of malicious packages uploaded included a similar snippet, r.jini.ai, that was contained in the German posts.

Cybersecurity company Socket first flagged the campaign in a threat intelligence report posted May 13, but it does not mention or attribute any of the activity to OpenAI or AI agents.

However, the researchers said they had only limited visibility over the model’s actions and how successful some of them were, noting only OpenAI had the full details.

“This analysis is entirely based on the publicly available RubyGems packages uploaded by these agents,” the researchers wrote. “However, we do not have access to the rest of the AI behavior, in particular the chain-of-thought produced by the model during the incident, which is internal to OpenAI. Therefore, we do not know why the AI agents chose this strategy or whether it was successful.”

OpenAI’s spokesperson told CyberScoop that to date, they have not been able to verify the specific claims about malicious packages or exploitation detailed in the report and are continuing to investigate.

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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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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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Okta announced Thursday it has signed a deal to buy Permiso Security, a cloud-based firm that tracks threats tied to human, machine, and AI-driven digital identities. 

Permiso specializes in spotting risks after a user or system has already logged in, an area the industry refers to as identity threat detection and response. The company draws on more than 2,500 signals gathered from over 70 identity-related partners to flag issues such as excessive access permissions, unused credentials, unusual behavior from AI agents, and violations of internal security policies.

Ely Kahn, Okta’s chief product officer, told CyberScoop that Permiso will allow Okta to merge two functions that have operated separately: real-time threat detection and identity security posture management. “Today those are two separate products that don’t really talk to each other,” he said. 

Combining them, Kahn said, produces sharper alerts for security teams. As an example, he described a hypothetical scenario where a dormant administrator account is flagged by posture-management tools that later shows a login from an unfamiliar IP address. “By combining those things, you now have a very high-confidence, high-fidelity alert that’s more actionable by a security operations team,” Kahn said. “A security operations team on its own might not care about the dormant account, but when you combine that with some threat signals, then it becomes a higher critical-level alert.”

A crucial part of the deal, according to Kahn, is that Permiso will bring visibility beyond Okta’s current threat detection products, telling CyberScoop that customers also rely on other identity systems, such as Microsoft Entra ID or Active Directory, that fall outside that view.

“For us to be a real player in the identity security space, we have to look beyond the Okta perspective and give folks a full view into their identity threats,” he said.

The acquisition also fits into a security landscape reshaped by artificial intelligence. According to figures cited by Okta, 58% of executives say their organizations experienced an AI-related security incident or a near miss within the past year. That trend has pushed identity companies like Okta to expand beyond authentication and into continuous monitoring of what accounts, including AI agents, actually do once inside a system.

“Agents will be breached,” Kahn said. “The most important thing you can do is ensure that if an agent is compromised, the blast radius is small,” through a narrowly defined, revocable identity tied to each agent. 

Among the capabilities Okta says it will gain is a tool called SandyClaw, which tests AI agent skills and prompts in an isolated environment before they are allowed into a customer’s systems, aiming to catch supply-chain attacks embedded in AI tools. Other planned additions include expanded tracking of AI agent behavior across cloud platforms and software-as-a-service tools, and automated systems to investigate and isolate AI agents that appear compromised or misconfigured.

The transaction is expected to close in the third quarter of Okta’s 2027 fiscal year, pending standard regulatory and closing conditions. Terms of the acquisition, including its purchase price, were not disclosed.

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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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Artificial intelligence is claiming many firsts as it permeates every layer of technology, including the tools cybercriminals use to break into networks, steal sensitive data, hop into connected systems and deploy malware. 

This includes, for the first time, according to Sysdig researchers, a case of agentic ransomware managing an extortion operation spanning reconnaissance, credential theft, lateral movement, persistence, encryption, destruction and the delivery of the ransom note itself.

The AI agent didn’t accomplish every step in the late June 2026 attack, but it allowed the threat actor, which Sysdig tracks as JadePuffer, to significantly reduce complexity, speed up the tempo and gain operational advantages. 

“We have seen attackers script attacks for years, and we have seen AI speed up individual steps of attack chains,” Michael Clark, senior director of threat research at Sysdig, told CyberScoop. However, this recent attack was “driven end-to-end by the model’s own decision-making, rather than a human at the keyboard,” he added.

The AI-aided attack achieved initial access by exploiting a Langflow vulnerability — CVE-2025-3248 — before moving on to its intended target: a production server running MySQL and Alibaba Nacos. 

Sysdig observed multiple factors that bolstered what it described as the first documented use of agentic ransomware.

The payloads involved in the attack narrated their objectives in plain language and identified high-value databases, details that large-language models annotate by default, according to Clark. The AI agent also quickly diagnosed problems and worked around obstacles — in one case redeploying a corrected payload 31 seconds after it originally encountered an error.

Before it was all over, the AI agent ran more than 600 distinct, purposeful payloads in rapid succession.

“The model closed loops that used to require a skilled human,” Clark said. “The 31-second failure-to-fix cycle on the Nacos backdoor is the clearest example of where agentic AI gave the attacker an advantage. The agent read the error, switched its approach from subprocess calls to direct library imports, and redeployed at a speed no human matches.”

Sysdig researchers found evidence that multiple models were used in the attack. The agent accessed keys for OpenAI, Anthropic, DeepSeek and Gemini as it gathered information on the victim’s systems. The cybersecurity vendor did not name the victim.

The AI agent played a crucial role in the attack, but a person was still heavily involved, Clark said. “A human still set up and pointed the operation and provisioned the infrastructure behind it, the command-and-control server, the staging server used for the stolen data and chose a victim,” he added.

The agent also connected to the victim’s MySQL server with root credentials that were not lifted from the victim’s environment, indicating a person gained access to the credential through a prior compromise. 

The origins of JadePuffer, a financially motivated threat actor, are unknown and it doesn’t overlap with any established ransomware group or nation state, researchers said.

For Clark, there is a clear uncomfortable takeaway from this attack: “The skill floor for running a full ransomware operation just dropped to whatever it costs to run an agent,” he said. 

“We have not yet seen operations against other victims, and given how cheap this agentic ransomware operation is to run, I would expect this will not be the last.”

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