The Federal Trade Commission wants to start regulating ideological bias in AI systems and assert federal control over state laws. They’re getting an earful from opponents on all sides of the political spectrum.

In a proposed policy statement released last month, the FTC said it was considering treating ideological bias in AI systems as an “unfair and deceptive practice” under Section 5 of the FTC Act.

The commission argued that consumers have an expectation that AI systems will provide them with information free from bias or ideological manipulation. Defining such bias as an unfair or deceptive practice would potentially allow the commission to regulate training or inputs that power AI algorithms. How precisely the FTC would determine when ideological bias exists in these systems is not fully explained in the document. 

Additionally, the statement suggests that the FTC believes this regulatory authority supersedes state AI laws. It specifically mentions the Colorado AI Act, which calls for models to be subject to risk assessments, transparency disclosures and “bias audits” before release. State lawmakers are now seeking to delay or eliminate the audits before the law takes effect in 2027.

CyberScoop reviewed dozens of public comments criticizing  the FTC’s proposal. Even ideological allies raised two main concerns: first, that the proposal distracts from real questions about the federal government’s role in regulating AI deception; and second, that it opens a Pandora’s Box by enabling political censorship of AI model outputs.

Leah Siskind, a former White House digital official and deputy director of the AI Corps at the Department of Homeland Security, told CyberScoop that AI companies face legitimate questions about their obligations to consumers, particularly whether they must ensure their models provide accurate information and protect against deliberate manipulation. 

Siskind’s past research has focused on how authoritarian propaganda tends to be overrepresented in answers provided by large language models, in part due to governments’ intentional efforts to poison data ingested by AI systems.

“There is a really interesting debate here about bias and about accuracy in models and whether that’s deceptive or not… about how we counter disinformation that has been absorbed and is now being reflected by LLMs…but this is not addressing that at all,” said Siskind, now a senior AI fellow at the Foundation for Defense of Democracies.

Instead, Siskind said the FTC statement appears primarily concerned about a power struggle with states over AI regulation and “petty squabbles about which AI model is more woke than the other.” She’s skeptical that the policy statement’s cited legal authorities are on sound footing.

“The way I see it is that the FTC’s role is to police consumer protection violations, not regulating AI systems, and it seems like they’re trying to solve a lack of congressional AI regulation by stretching section 5 [of the FTC Act] well beyond its traditional role,” she said.

Additionally, the policy statement’s language and sourcing suggests that the FTC is concerned with certain kinds of ideological bias more than others.

Anthropic, which has clashed with the Trump administration over AI guardrails and military applications of their technology, shows up more than half a dozen times in footnotes, many which are framed as examples of ideological bias the FTC is seeking to stamp out.

By contrast, the statement ignores a direct example of an American AI company owner influencing their model’s ideology: Elon Musk and his xAI-owned Grok model. Musk has publicly admitted, often on his own website, to intervening when Grok’s responses upset him. These interventions have shaped Grok’s outputs on specific topics, including South African race relations and the term “MechaHitler,” where the model now reflects Musk’s personal views.

But neither Musk and xAI are mentioned in the document, while Grok appears in a footnote which cites an advertisement for Grok as “your truth-seeking AI companion for unfiltered answers with advanced capabilities in reasoning, coding, and visual processing.”

Criticism across the spectrum

The FTC received more than 300 comments on its proposal from trade associations, think tanks, individual experts and members of Congress. Most criticized it as ill-defined and vulnerable to politically-motivated censorship, while some supported stronger rules against bias in AI systems. 

The International Center for Law and Economics noted the statement “offers little practical guidance about how the Commission will apply its deception authority to AI” and also does little to address hard questions, like where AI providers may be exercising their own First Amendment-protected activities.

The statement’s “focus on ‘ideologically motivated distortions’ suggests that the Commission’s concerns extend beyond factual misrepresentations in marketing to speech that may receive the highest degree of First Amendment protection,” the ICLE wrote.

The America First Legal Foundation, a conservative non-profit founded by top White House adviser Stephen Miller, pressed the FTC to adopt the policy “in full,” claiming that frontier models from OpenAI and Anthropic “have been programmed to prioritize ideologically liberal and progressive values as though they are objective, neutral positions rooted in truth.”

The group also argues that regulating these models’ ideological output falls under the FTC’s legal authority, because a “reasonable consumer” would expect that a model advertised for its usefulness and reliability would not prioritize liberal, ideological views.

“A reasonable consumer, based on AI companies’ advertising choices, would not expect that an AI system will adopt overwhelmingly liberal positions, thereby skewing results, or adopt a moral framework that would prefer to annihilate the earth rather than utter a slur,” wrote Emily Percival, senior counsel for America First Legal.

However, comments from other conservative groups questioned that rationale. The R Street Foundation’s Spence Purnell and Adam Thierer wrote that “the consumer expectations rationale is typically used in cases where there is an omission of information that should have existed.”

“Given that most LLMs already have disclosure statements [for their outputs], it seems unlikely that the FTC could explicitly prove that consumers were deceived about a product,” Purnell and Thierer wrote.

Reps. Josh Gottheimer, D-N.J., and Michael Lawler, R-N.Y., urged the FTC to carve out civil rights-related work from their scrutiny, such as preventing models from discriminating against users based on race, religion, gender, age and other federally protected characteristics.

“AI companies must not falsify facts in the name of fairness, but they also must prevent discrimination, stereotypes, and unequal treatment,” Gottheimer and Lawler wrote. “We would appreciate understanding how the FTC intends to ensure that these efforts remain permissible under the final policy framework.”

But the most common concern shared across the political spectrum was that the FTC could establish a precedent allowing the Trump White House and future administrations to reshape AI systems to reflect their political views.

David Inserra, Jennifer Huddleston and Juan Londoño of the Cato Institute point out that the FTC statement is conflating two different issues: ideological bias in AI systems and factual deception in marketing. 

“In other words, the FTC is trying to judge AI models’ accuracy and performance—two largely subjective variables—in the same way it evaluates dietary supplements’ medical-benefit claims or users being charged fees without proper notice or consent,” they write. “This is an absurd comparison.”

The post The FTC wants to regulate AI for ideological bias  appeared first on CyberScoop.

OpenAI announced Monday  it was expanding access to its frontier models for defensive cybersecurity, detailing different defensive and red-teaming workflows and a new partner program with major cybersecurity product providers.

In a pair of blogs posted Monday, OpenAI said it was updating its Daybreak program  – which provides unreleased frontier models to private organizations and governments for defensive cybersecurity work – and introducing a new model variant.

Daybreak Blue, powered by OpenAI’s ChatGPT-5.6-Sol, would operate with lower cybersecurity safeguards compared to other commercially available models and is described as “a recommended starting point for most defenders” that supports tasks like vulnerability discovery, secure code review, malware analysis, incident response and patch validation. 

Daybreak Red, meant for more advanced red-teaming, would provide access to a new model, dubbed GPT-5.6-Cyber, that the company said is more purpose-trained for finding vulnerabilities and testing (or exploiting) them. The model is also less likely to refuse requests around “dual-use cyber tasks.”

According to OpenAI, the organizations in Daybreak Red will have their use closely monitored and supervised, as GPT-5.6-Cyber is significantly more capable in carrying out malicious cyber tasks than Sol. A security evaluation the company devised tested both models on complex requests, including exploit chain development, authentication bypass, privilege escalation and other hacking tasks. Sol succeeded in 1.5% of the requests, while Cyber completed 95%.

OpenAI said it plans to publish a more detailed system card for GPT-5.6-Cyber at a later date.

“Models running with reduced safeguards carry risks beyond standard model usage, whether from misuse or misalignment,” the company said in a blog. “Despite these risks, we believe that democratizing access to frontier intelligence for defenders is crucial to accelerating and automating cyber defense.”

Additionally, OpenAI announced a partnership program with 16 major cybersecurity providers, saying organizations could access their models through their existing security services. The partners include IBM, CrowdStrike, Accenture, Ernst & Young, KPMG, Palo Alto Networks, Cisco, Cloudflare, Sophos and others. 

“These partners bring deep security expertise and established relationships with organizations around the world,” OpenAI said in its blog. “By bringing our frontier cyber models into their services, we can help more defenders find serious vulnerabilities, validate which ones matter, and fix them faster.”

Companies like OpenAI, Anthropic and others are trying to rebalance their priorities after a string of AI-agent sandbox escapes have rattled policymakers and caused some cybersecurity experts to question if AI companies are doing enough to properly isolate the models from the internet during testing. Last week, OpenAI said it was intentionally slowing down development of its newer “Astra” model in order to develop better guardrails to restrain its behavior.

Cybersecurity and AI experts have told CyberScoop that while AI systems have greatly improved at finding and exploiting vulnerabilities in software code, they still require substantial human guidance and supporting infrastructure to operate as intended.

Additionally, some research has shown that without such guidance, even near-frontier models can struggle to fully patch a discovered vulnerability or avoid introducing new bugs with their fixes.

The post OpenAI says Daybreak will expand to offer specialized cyber services  appeared first on CyberScoop.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The post More than half of AI-generated patches are broken appeared first on CyberScoop.

Like seemingly everything else these days, artificial intelligence will re-shape the way voters gather information on candidates running in the 2026 midterm elections.

In some ways, this is already the reality. Voters are increasingly turning to AI chatbots for information instead of Google.  Political campaigns are deploying deepfakes of their opponents. And AI systems have been developed to carry out increasingly complex  hacks.

Since the last major U.S. election in 2024, major tech companies have  embedded AI into their products while hundreds of millions of people have adopted the tools, either by purchasing subscriptions to commercial models or using open-source models. Yet both research and experts state that while AI systems have gotten better at handling basic facts, they’re nowhere near reliable enough to be a main source of  accurate or complete information. 

While chatbots are becoming a primary way that voters gather information on  local races, candidates, issues, and voting information, they are not substitutes for more authoritative sources, like a voter’s state or local election office. 

“I think this is one of the first elections we’re seeing…where AI is just everywhere,” said Thania Sanchez, senior vice president of research and analytics at the nonprofit States United Democracy Center. “Even if you just Google it, [now] the first thing that comes up is the AI overview.”

While AI companies have worked to cut down on errors in their model’s responses for questions around basic election information, they continue to fall short in important ways.

In new research shared exclusively with CyberScoop ahead of its release, States United Democracy Center tested two of the most popular tools — OpenAI’s ChatGPT’s free tier and the AI interface used alongside Google Search — for their performance on a series of basic questions around elections, such as how to register to vote, or a list of candidates in a race.

The models were chosen because they are free and easy to access. For Google AI, the nonprofit tested two types of accounts: ones running in Incognito Mode and ones that had a history of browsing election-skeptical websites.

The nonprofit ran two rounds of testing in 2025 and 2026, collecting nearly one thousand responses from the models submitted by users across six swing states (Arizona, Michigan, North Carolina, Nevada, Pennsylvania and Wisconsin).

In 2025 tests, 6.9% of responses from Google AI and 8.2% responses from ChatGPT“contained verifiable factual errors,” like not listing the correct candidates in a race or false guidance around polling site locations.

However, follow up tests in 2026 across Arizona, Pennsylvania and Michigan found that the error rates in both models had dropped to zero. The study notes that “this is real progress and should be acknowledged.”

But underneath those topline numbers, a more murky picture emerges around the tools’  reliability.

An AI response can sound accurate without actually being complete.  To wit: ChatGPT provided incomplete lists of current gubernatorial primary race candidates 88.9% of the time when queried.

Linking to a state election website – an output the study considers the single most important measure of voter utility  — happened less than 40% of the time. Whether due to formatting issues or the model ingesting outdated information, it’s a problem if voters use them as their primary information source for elections.

“It will be like ‘this person is the Republican candidate and this person is the Democratic candidate’ but it is not telling you there’s also these other third-party candidates,” said Sanchez. “It’s not giving you complete information, so the voter thinks these are the [only] two people running.”

A June survey from the Pew Research Center found that about half of U.S. adults reported having used chatbots at least once, up from a third in 2024, while a quarter reported using them daily. The top use case listed for engaging with the chatbot was searching for information.

Isabel Linzer, an elections policy analyst at the Center for Democracy and Technology, told CyberScoop that voters, campaigns and governments alike are using AI more freely and with fewer restrictions.

Bad actors in the information space have followed suit, and “we are in a phase now of generative engine optimization” where information operations are structured to rank higher in AI model responses.

“We’ve moved beyond [SEO] to [Generative Engine Optimization], and that’s where we’re seeing campaigns thinking about how to structure their materials to make sure that they are in a format that AI models want to use when they’re searching the web…to develop their responses to user queries,” she said.

There is also the underlying problem of frontier AI companies constantly tinkering with their models, their algorithms and the technologies they are intertwined with. . Election officials, by contrast, have decades of experience educating voters about their options.

A prime example of this churn occurred this past February, in between the first and second round of the study, when Google AI suddenly shifted to providing only links for election related queries in incognito mode, replacing the written summaries that showed up in the first round.

Like the study’s authors, Linzer said most people are still best served by going directly to local sources for accurate information on elections. With issues like ideological bias, the potential for bespoke or sycophantic answers for each user based on their prior chat histories and lack of predictability, voters should still be very careful about using AI chatbots as political truth machines.

The best thing that tech companies can do to educate voters is “making sure that for high-stakes situations like elections, that chats are connecting directly to the most important sources, like the website where you can actually register to vote,” said Linzer.

The post AI is getting better at election facts, but voters shouldn’t rely on it appeared first on CyberScoop.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

CyberScoop has reached out to Irregular for comment.

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

The post AISI, OpenAI report more ‘unsanctioned’ model hacks appeared first on CyberScoop.

The Senate is teeing up debate on a raft of new bills that would impact online privacy, kids safety and artificial intelligence.

The Senate Committee on Commerce, Science and Transportation will mark up five bills Wednesday. The most high-profile legislation, the Kids Online Safety Act, sponsored by Sens. Marsha Blackburn, R-Tenn., and Richard Blumenthal, D-Conn., would implement broad changes to how social media and other websites handle data and accounts for users under the age of 17.

KOSA would require online platforms — including social media, video games, messaging apps and streaming services – to exercise “reasonable care” when designing features that could lead to more addictive or harmful online behaviors for minors. It would provide parents with digital tools to control and monitor their children’s accounts, prohibit market or product research on children under the age of 13 and empower the Federal Trade Commission to investigate, fine and enforce the law.

Earlier bill versions earned the backing of large tech companies, including Apple, OpenAI, and others.

By contrast in June, nearly 100 smaller parent, youth and tech-focused organizations signaled their opposition to the bill in a letter to congressional leaders. Some of the signatories, like the nonprofit Issue One, were previous supporters of KOSA who turned on the legislation after the House passed a significantly watered down version that stripped out stronger language around tech companies “duty to care,” which would have set a higher legal standard for covered platforms to consider user harm when designing their products.

Legal and ethical design standards are critical for online services, the groups argue, given lawsuits alleging that major tech platforms contribute to teenage addiction, depression, suicide, and non-consensual deepfakes.

“Major social media companies, the companies this bill regulates, are currently on trial across the country,” the letter said. “The evidence in those cases – internal records prioritizing teen engagement over teen wellbeing, safety changes shelved because platforms would lose users, buried research on the benefits of disconnection shows the default poor choices of these companies when the law does not require otherwise. Stripping the duty of care does not lighten a regulatory burden; it removes the most important obligation requiring these products to be designed safely in the first place.”

However, Blumenthal and Blackburn publicly stated that the House version was “dead on arrival” without those provisions, and they remain in the Senate version of the bill being considered Wednesday.

The markup will also consider other major legislation that would regulate age on the internet, safety features for AI chatbots and more. While proponents claim the bills enhance privacy and safety protections, technology experts largely disagree.

The SCREEN Act, introduced last year by Sen. Mike Lee, R-Utah, would require social media companies to implement age verification technology.

Lee has partnered with parent-led groups to advocate for state-level age verification laws that expand  parental control over children’s social media accounts. Some public surveys have shown broad public support for age verification laws.

Louis Eichenbaum, a former chief information security officer at the Department of the Interior, told CyberScoop that one of the biggest challenges around online age verification is that it “increasingly requires collecting, storing or validating sensitive identity information about them.”

“The goal should not simply be verifying age, it should be doing so while minimizing the collection, retention, and exposure of personally identifiable information,” said Eichenbaum, now federal chief technology officer at ColorTokens. “Every additional piece of identity data collected expands the attack surface and increases the potential impact of a breach.”

Some privacy groups oppose the SCREEN Act and similar age verification laws, arguing the required data collection outweighs child protection benefits. 

The Electronic Frontier Foundation said the SCREEN Act is broader than state-level age verification laws, which only cover websites that are predominantly sexually explicit.

“The bill requires nearly any service hosting even a single piece of sexually explicit content to verify the ages of its users,” wrote EFF director of federal affairs India McKinney. “The result is that the bill would apply not only to adult content sites like PornHub or OnlyFans, but also streaming services like Netflix, and social media platforms like Reddit, Discord, or Bluesky, if they host any adult content.”

The Youth AI Privacy Act, from Sen. Ed Markey, D-Mass., would require new safety features for AI chatbots.

According to a fact sheet released by Markey’s office in March, the bill would ban push alerts, require chatbots to disclose they’re not human, limit data retention, and prohibit using minors’ data for AI training or any purpose beyond providing answers.

The Chatbot Act, by Sens. Ted Cruz, R-Texas, Brian Schatz, D-HawaiI, John Curtis, R-Utah and Adam Schiff, D-Calif. would require AI companies to implement “family accounts” for AI chatbots that give parents the ability to monitor and restrict their children’s interactions. Cruz has said the status quo “has left many parents in the dark” on their kids’ AI use.

The Children’s Artificial Intelligence Toy Safety Act, by Sen. Tammy Duckworth, D-Ill., would create a federal study around toys sold to children that include artificial intelligence or chatbot components.

The post Senate set to debate package of bills on privacy, AI and kids safety  appeared first on CyberScoop.

Microsoft, along with more than two dozen tech companies, are pressing policymakers to support open-source AI systems and code across society, arguing that it will be a safer approach than attempting to restrict access or relying on a handful of closed, proprietary models.

The open letter, posted Friday, draws parallels to the software industry of the 1980s, when large businesses worried that open-source software code would cut into their business. While industry lost that battle, the end result was a vibrant ecosystem that now underpins much of the modern internet, government IT and even commercial software products.

It also created a “shared foundation of knowledge” that has fed countless future software projects and innovations.

“The United States now faces a similar choice with artificial intelligence,” the companies wrote. “Our AI leadership will be judged not by one frontier AI model, but by whether the United States builds a strong, open ecosystem that diffuses into every sector.”

Expanding access and support to open-source AI comes with meaningful security risk. Cybersecurity experts warn that one of the biggest beneficiaries of broadly available AI tools are  low-level criminals who until now lacked the technical expertise or resources to launch serious attacks.

Once a model is open weight, anyone can download it, customize it, strip it of any guardrails and use it for their own purposes. As open-source models have gotten better at creating deepfakes and other AI generated imagery, the danger of locally-customized CSAM and sexualized deepfakes could also grow.

But the letter argues that open-weight AI models are most beneficial to startups, universities, research labs and other small, ambitious organizations that can innovate and iterate the technology and make it more broadly useful to society.

“Open weights let every organization match the right model to the right job at the right cost, reserving frontier-scale capability for genuine frontier problems and running efficient specialized specialized models everywhere else,” The companies wrote. “That discipline is what will make AI economically sustainable as its use scales into the billions of everyday tasks.”

 For cybersecurity specifically, the letter argues that defenders armed with open-source AI will outpace attackers better than any closed model approach.

“In a world where cybersecurity attackers use advanced AI, defenders need access to models with comparable capabilities so they can detect, simulate, and respond to emerging threats,” the companies wrote. “Open models broaden defensive capability, increase transparency, and allow vulnerabilities to be discovered and remediated across many teams.”

Other notable companies signing the letter include Meta, Palantir, Perplexity, Mistral, NVIDIA, Mozilla, The Linux Foundation, Hugging Face, Dell Technologies and IBM.

US policymakers continue to grapple with balancing unrestrained support for the domestic AI industry and providing oversight and regulation of harms that result from their use.

The Trump administration has cycled through several frameworks since coming into office, first a laissez-faire approach within no restrictions, then an executive order creating a voluntary testing regime for industry, then the imposition of export controls on Anthropic’s Fable model and reportedly pressuring OpenAI to delay the release of their models out of cybersecurity concerns.

The letter comes as the Trump administration has reportedly considered an executive order that would restrict American access and availability to Chinese-made open-source models.

But the White House and US companies are trying to thread a needle in recognizing the overall benefits of an open source approach while being wary of doing anything that could potentially benefit their Chinese rivals.

Earlier this month the White House announced the creation of its Gold Eagle AI cybersecurity clearinghouse that would help coordinate government, private sector and civil society work finding and closing AI-discovered vulnerabilities. A big part of that effort, a senior White House official said, is supporting providers and maintainers of open-source AI tools.

The post Microsoft, tech companies throw weight behind spread of open-source AI appeared first on CyberScoop.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The post White House accuses Chinese company of distilling Anthropic’s Fable appeared first on CyberScoop.

A cyberattack that poisoned the data pipeline of a major AI code platform was carried out using OpenAI’s ChatGPT, the company said Tuesday.

Last week, Hugging Face, a platform for sharing and working on AI code, disclosed that an external attacker had compromised its data processing pipeline. According to a July 21 blog post, the attacker poisoned a dataset to run code on a processing worker, eventually gaining node-level access and stealing cloud credentials.

The attack is notable, the blog said, because it appears to have been carried out by an autonomous AI system, which executed “many thousands of individual actions across a swarm of short-lived sandboxes, with self-migrating command-and-control staged on public services.”

At the time, Hugging Face said it wasn’t clear which LLM was used in the attack, but noted that their own attempts to recreate parts of the attack using an in-house commercial model failed due to guardrail restrictions.

The incident and its ultimate impact are still being investigated, and Hugging Face said it reported the incident  to law enforcement and is engaging with third-party cybersecurity forensic specialists to learn more.  

“This experience points to a gap worth planning for,” the company wrote. “We do not know which model powered the attacker’s agents, whether a jailbroken hosted model or an unrestricted open-weight one; either way, the attacker was bound by no usage policy, while our own forensic work was blocked by the guardrails of the hosted models we first tried.”

Now OpenAI is confirming one of their models was used in the attack.   

“After investigating, we now know that this particular incident was driven by a combination of OpenAI models — including GPT‑5.6 Sol and an even more capable pre-release model, all with reduced cyber refusals for evaluation purposes — while being internally tested on a benchmark of cyber capabilities,” the company wrote in a blog post Tuesday.

OpenAI called the incident “unprecedented” but also predicted that such attacks would  become increasingly more common as AI adoption continues to grow.

According to the company , the incident happened during an internal evaluation,  where the company deliberately prompts models to “pursue advanced exploitation using complex attack paths” in order for the company to study and measure the model’s cybersecurity capabilities.

To assess the model’s full potential, the team disabled the production classifiers that normally prevent OpenAI models from carrying out high-risk cyber activity. Without these safeguards,  the models were able to install software packages through registries like Hugging Face.

The model collected and chained vulnerabilities from OpenAI’s systems as well as Hugging Face’s production infrastructure.

OpenAI further explained that the activity stemmed from models attempting to solve a specific challenge posed by ExploitGym, a benchmarking system for AI agents.While such tests normally run in isolated environments, in this instance,  the model spent a “substantial” amount of compute resources figuring  out how to access the open internet. It did so by using a zero-day vulnerability in a third-party vendor’s system used by OpenAI (the company said it disclosed the flaw to the vendor).

“After gaining Internet access, the models inferred that Hugging Face potentially hosted models, datasets and solutions for ExploitGym,” OpenAI wrote. “Knowing this, the model searched for and successfully found ways to gain access to secret information that it could use to cheat the evaluation.”

In one instance, the model chained together multiple stolen credentials and zero-day vulnerabilities to find a remote code execution path on Hugging Face servers.

OpenAI said it was implementing new controls for infrastructure configuration, something that would come at “the cost of research velocity while the vulnerabilities are patched.” 

Hugging Face was also added to OpenAI’s Trusted Access for Cyber program, which will give them access to more advanced models like Daybreak to scour their systems for other AI-discovered vulnerabilities.

The post OpenAI says model test was behind Hugging Face hack appeared first on CyberScoop.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The post AI models keep getting caught cheating appeared first on CyberScoop.

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.”

The post Where’s the Trump administration line on AI regulation? appeared first on CyberScoop.

The head of a key federal government AI testing lab is leaving his post just months after taking over.

A Department of Commerce spokesperson confirmed to CyberScoop that Chris Fall is stepping down as director of the Center for AI Standards and Innovation, and his position is being backfilled.

“Following Chris’s departure, NIST Director Dr. Arvind Raman will continue to oversee CAISI and will serve as Acting CAISI Director,” the spokesperson said in a statement to CyberScoop.

Further details about the circumstances behind Fall’s departure were not provided. Axios, which first reported the departure, cited sources saying that Fall resigned.

The Center for AI Standards and Innovation has quietly become a key hub for the federal government to assess potential threats and harms that AI systems can pose to cybersecurity and national security. 

Early in the Trump administration, the center began informally working with frontier AI companies like OpenAI and Anthropic to test their models for threats, like their offensive hacking skills, assistance with building biological or nuclear weapons and other dangerous capabilities.

Fall was tapped to lead the center in April, and his departure just three months later comes as the White House has elevated the work of the center as one of the key means for determining which frontier AI models do — and do not — represent a step change in cyber or other capabilities compared to what’s available today.

Fall previously held other government posts, including as director of the Department of Energy’s Office of Science, assistant director for defense programs at the White House Office of Science and Technology Policy, and acting chief scientist at the Office of Naval Research.

The post Director of Commerce AI standards office out after three months appeared first on CyberScoop.

State and local officials and election security experts largely panned a Thursday night primetime speech by President Donald Trump, saying it was reflective of White House “desperation” to find any credible evidence to support their claims that U.S. elections have been rigged against the two-term president.

While the White House teased explosive new claims about the potential compromise of U.S. elections by China, Trump’s speech was a rehash of claims that both have no supporting evidence and have been repeatedly debunked when investigated. 

David Becker, executive director of the Center for Election Innovation and Research and a former voting and civil rights attorney at the Department of Justice, said none of Trump’s claims or allegations were new or substantively different from previous theories he’s been espousing over the past six years.

“The White House promised a bombshell and they delivered a dud,” Becker said on a call with reporters Friday. “There was nothing that even calls into question past elections — certainly not the 2020 election.”

The administration declassified a huge tranche of documents from the intelligence agencies, and news outlets continue to sift through them, but thus far nothing has been found that remotely validates the administration’s claims about foreign interference from China costing Trump the 2020 election.

In fact, some of the most relevant documents found at this point have supported the opposite conclusion, with agencies assessing that while China engaged in influence campaigns around the election, it was not attempting to outright interfere with U.S. election infrastructure, hack voting machines or manipulate ballots.

John Solomon, a former journalist and opinion writer at The Hill brought in by the White House to lead the investigation, also told reporters Thursday that his search hasn’t turned up evidence that the 2020, 2022 or 2024 elections were affected by fraud.

The one new major claim by Trump — that the Department of Homeland Security determined hundreds of thousands of noncitizens were registered to vote across four states — is almost certainly false or overinflated, given that it contradicts post-election state audits that have routinely found single or double-digit numbers of noncitizens registered to vote within a single state across multiple elections.

Over the past six years, similar claims by GOP secretaries of state and political activists purporting to find mass numbers of noncitizens registered to vote have turned out to be grossly inflated due to shoddy data analysis, and the vast majority of cases involving “suspected noncitizens” turn out to be U.S. citizens who are legally registered to vote.

The White House has provided little to no information on the methodology used to flag and identify supposed noncitizen voters, other than alluding to the use of “commercial data” and federal databases. A federal court recently ordered DHS to dismantle the SAVE database, its primary database for verifying the citizenship status of U.S. voters, because it was unreliable and violated longstanding privacy laws. 

 Apart from DHS admitting its own data on citizenship is incomplete, Becker said using a list that relies on matching voter files with commercial data is not a reliable way of determining citizenship.

“It is impossible to take a public voter file with very little information that is uniquely identified, like a driver’s license number, and compare it to a commercial database and say for sure the Maria Rodriguez or the John Lee or the Shawn O’Hara you have on that is the same person,” he said.

Election officials also responded forcefully. Nevada Democratic Secretary of State Francisco Aguilar said that Trump has spent a decade attempting to manufacture a crisis around voter fraud and the president’s speech Thursday night was an extension of that effort. 

“As Nevada’s chief elections officer, it’s my job to call balls and strikes — so when the President lies, I am obligated to call him out,” Aguilar said in a statement. “The facts have not changed: Nevada’s elections are among the safest, most secure and accessible in the nation.”

It’s not just Democrats that have objected to the administration’s efforts. GOP states have gone to court to block the Department of Justice from obtaining their voter data, and Idaho’s Republican secretary of state responded to a DOJ letter threatening prosecution of election officials as “not well met” and potentially illegal under state ethics laws. 

Trump’s speech potentially casts additional light on recent White House decisions, such as firing all three commissioners on the Election Assistance Commission. The agency helps certify voting machines for security, and all three commissioners have served across administrations and maintain close relationships with state and local election officials.  

Pamela Smith, CEO of the nonprofit Verified Voting, said that while the EAC can’t take certain actions that need commissioner approval, “critical functions like voting system testing and certification can continue under the existing framework and should not be affected.”

In 2020, Trump’s initial claims of widespread election fraud were undercut by leaders at the Cybersecurity and Infrastructure Security Agency, which said there was no evidence the election was compromised. The removal of EAC commissioners could represent an attempt to preempt any efforts to rebut or criticize White House claims that elections and voting machines have been compromised.

Some have worried that Trump could use the speech as a pretext to declare a national emergency or cancel elections.

Tom Lopach, CEO of the Voter Participation Center, said “you don’t dismantle election security infrastructure if you’re serious about protecting elections.”

“You dismantle it if you’re planning to claim, without evidence, that the system failed you,” he said. 

While Becker takes Trump’s broadsides against state election authority seriously, he also said it’s important not to lose sight of the fact that, in his view, the administration is losing the argument across the board.

More than a dozen federal courts have unanimously rejected the federal government’s attempts to forcibly obtain state voter data, while other courts have rejected core pieces of his election-related executive orders. State officials have publicly — and at times, angrily — pushed back on the administration’s demands as blatant federal overreach. 

Becker predicted that such an act would be quickly shot down by courts as well, noting that the U.S. has never canceled or postponed an election in its 250-year history, including when British troops were marauding on American soil during the War of 1812 or even at the height of the Civil War.

It’s important not to conflate the White House’s bluster and intentions with its actual authorities or capability to seize control of U.S. elections.

“This is what panic and desperation look like,” Becker said. “They’ve had 18 months in total control of the federal government and they have found nothing that would support President Trump’s lies about the 2020 election, and so they’re just trying to grab as much garbage as they can and throw it up against the wall, and it’s not sticking.”

The post State officials, election experts pan Trump speech: ‘This is what desperation looks like’ appeared first on CyberScoop.

As AI-enabled hacking becomes a bigger threat for cybersecurity and national security, public attention has focused on mainly a few leading frontier AI companies developing more powerful large language models.

These models, and the billions of dollars behind them matter, but they’re only part of a larger shift. Enterprises are now building their own technology platforms that take these general-purpose LLMs and turn them into bespoke cybersecurity tools.

Industry professionals refer to these tools as a “harness.” They control the model’s behavior, limit its risks, and connect it to internal IT systems and networks so it can work reliably at scale.

New research from Cato Networks shared exclusively with CyberScoop shows how much power can come from a harness. It paired OpenAI’s ChatGPT 5.5 and GPT 5.5-Cyber models with its own tool and tested the abilities of the agent to hack into a victim network with as little human direction as possible.

Across six different scenarios, the pairing achieved complete end-to-end attack chains, including domain administrator privileges and Active Directory access, sometimes in as little as 40 minutes.

“What was most surprising is that first we saw that it was capable of doing accelerated reasoning and attack, and interacting and doing all this by itself, like doing all of the stages of the attacks,” said Guy Waizel, a tech evangelist at Cato Networks and one of the authors behind the research.

Critically, the most successful scenarios happened when the model was given appropriate operational context from the technical harness developed by Cato Networks.

“It does support that it’s not just about the frontier model,” said Waizel. “We found that [our harness] really helps the reasoning” of the LLM.

The agent was given some – but not abundant – resources to complete its tasks, including an external Kali Linux attack host, the simulated target’s public IP address and a set of low-level domain credentials acquired through phishing.

It was not provided with any other details, and had to probe further for key information, such as further knowledge of the server type (Microsoft Exchange), the target’s operating system, version, build number, internal network topology, access to higher privilege accounts and other critical assets, nor was agent given any predetermined attack paths.

The Cato Networks research uses OpenAI models, but only as an example. Waizel said he believes other models would likely achieve similar results. In any event, if current trends hold, the kind of capabilities provided by LLMs like GPT 5.5 are likely to be open-source within a year.

Cato Networks is far from alone. Most enterprises have their own AI harnesses, and  executives tell CyberScoop they are playing an increasing role in more effectively steering the frontier model workflows.

While AI tools can struggle to duplicate human workflows in other areas, LLMs have long shown potential in cybersecurity and coding, improving greatly over the past few years. The Trump administration has set up a new federal clearinghouse for exchanging information between the public and private sectors on AI-discovered vulnerabilities, while European groups are setting up their own organizations to coordinate globally on AI cyber threats.

Eric Doerr, chief product officer at Tenable, told CyberScoop a harness used in the company called “Hexa”  offers a defensive advantage:  it can work with different commercial LLMs while delivering consistent  results.

“One of the first things we do when we get a [new] model is say ‘Well, let’s run it through Hexa and see what we learn,’” said Doerr. “We have a whole bunch of benchmarks. Is it the same, is it better? Where is it better? Where is it worse?”

Hexa is meant to ensure that whichever model or models become dominant, Tenable will be able to integrate it into their tech stack and protect their most sensitive assets from unintended behaviors. That frees up the LLM to do what it does best: find vulnerable code and establish attacker pathways for exploiting them.

“For years, it has been true that there are way more potential issues that a company has to deal with: code vulnerabilities, things that are unpatched, misconfigurations,” said Doerr. “There’s way more than you can actually remediate, and you really need to understand the difference between what’s a theoretical problem and a real problem.”

Dan Rapp, chief AI and data officer at Proofpoint, said their harness, “Satori,” has become a critical tool for keeping their agentic AI on track while giving humans the ability to step in when things go awry.

“I think what you’re seeing in the foundation of frontier models is you have raw intelligence, raw reasoning power, but to get these systems to perform the way you want to, both context engineering – the content provided ensuring that its accurate and relevant – and the harness engineering are essential to actually get the systems to perform well,” Rapp told CyberScoop.

That was a common theme in interviews with companies. While frontier models come and go, or are overtaken by international competitors, there will always be the need for the model to operate with data and context that often only the organization can provide.  

It suggests that while policymakers and cybersecurity experts have focused on the spread of newer and more powerful frontier models, industry – and likely soon the cybercriminal underground — has quickly developed the kind of technical infrastructure that is becoming far more important to AI cyber defensive and offensive tasks.

“We’ve had to bootstrap quite a few of these systems from first principles, and what it always boils down to is how effective you are with the tool calling… bringing in data, enriching the context,” said John Hopper, vice president of product engineering at SpecterOps.

The post Forget the model. When it comes to cybersecurity, it’s all about the harness appeared first on CyberScoop.

The Trump administration unveiled its new federal clearinghouse for sharing AI cyber threat information between the government and private sector, and said the project is already receiving threat intelligence on cybersecurity vulnerabilities and prioritizing patching.

Created last month through a White House executive order, “Gold Eagle” will be managed by the Department of the Treasury, with contributions from the Cybersecurity and Infrastructure Security Agency, Department of Homeland Security, and Department of Defense, as well as open-source software providers, critical infrastructure operators and industry.

“Under President Trump’s leadership, the Treasury Department is working hand in hand with the private sector to safeguard our financial institutions, close vulnerabilities, and protect the integrity of the U.S. financial system,” Secretary of the Treasury Scott Bessent said in a statement. “Treasury, along with our partner agencies, will continue to harness frontier AI capabilities to stay ahead of our adversaries and defend the American people from emerging threats.”

Gold Eagle is meant to help both public and private organizations find, fix and patch vulnerabilities found using AI tools before they’re discovered and exploited by bad actors. The work will involve using AI to find cybersecurity vulnerabilities in victim systems and software, and Secretary of Homeland Security Markwayne Mullin said it would also further explore ways for the technology to be leveraged for cyber defense.

A senior White House official told reporters on a background call that closed source models from frontier AI models, including Anthropic’s Mythos, will be used to discover vulnerabilities.

White House officials said they worked with the Software Engineering Institute, SEI at Carnegie Mellon University to develop a new platform, the Vulnerability Information and Coordination Environment – or VINTS – to receive third-party reports on AI-discovered vulnerabilities. According to the White House, the system has already begun collecting intelligence on vulnerabilities and prioritizing patches.

“I think on the early side of this, we have seen that the scale of vulnerability discovery, particularly with users of new technology to scan their system, is something that is a step function change [than] we’ve seen seen before,” the official said.

As AI models have improved at carrying out core cybersecurity-related tasks – like scanning code for vulnerabilities or developing proof-of-concept exploit code – cybersecurity experts and policymakers have become increasingly worried. The modern internet is rife with insecure code, misconfigurations and other mistakes that can be identified and exploited faster than ever before using AI tools.

Vulnerabilities in open-source software can be both widespread and hidden, as many commercial software products on the market rely on open-source code but few bother to document it. When hackers compromised a logging tool in the Log4J open-source Apache software library in 2021, it required a massive, multi-month coordination effort by CISA, the private sector and other stakeholders to find and fix affected pieces of software.

The White House official said the work of Gold Eagle is reflective of the administration’s “full support” of U.S. open-source software providers and maintainers.

Open source tools are “vital to systems that run throughout our country and daily life,” a senior administration official said, speaking to reporters on background. “It is being maintained by a talented group of people and entities and we will do everything we can to support the strength of that community.”

Michael Daniel, former White House cyber coordinator under President Barack Obama, told CyberScoop that AI is still so new that policymakers continue to observe its impact and adapt. While some existing communication channels for sharing cybersecurity threat information could probably be duplicated for tracking AI threats, there is still much for policymakers to learn more about the technology, the kind of threats it produces and its ecosystem of stakeholders.

“It may turn out at the end of the day that phishing is still phishing, and the fact that now you’ve got AI tools doing it, it’s still phishing. Or there may be something fundamentally different about it that we need to figure out how to combat and share information around,” he said.

The post White House details ‘Gold Eagle’ clearinghouse for AI cyber threats appeared first on CyberScoop.

The Trump administration’s abrupt firing of Election Assistance Commission commissioners last week and a Department of Justice warning threatening states with criminal prosecution have created new legal peril for officials who run, administer and secure elections.

The EAC is an obscure but important agency that oversees testing and standards for voting machines, including around security. While federal certification is voluntary, states have until now relied upon their stamp of approval when purchasing voting machines. 

On July 10, Democratic Commissioners Ben Hovland and Thomas Hicks were fired by the White House, while reports indicate that a third Commissioner, Republican Christy McCormick, resigned. While Congress mandated the commission be bipartisan, the Supreme Court has recently given the President broad authority to fire executive branch officials at will.

In an interview with NPR, Hovland said he worried the firings would further erode trust that the commission was working in a bipartisan manner.

“And as you eliminate things – or if you get rid of commissioners, for example – or as you eliminate some of these other sort of safeguards or norms, it certainly strains the system,” said Hovland. “And it certainly also likely causes people to lose faith in our democracy and in the process and their confidence in our elections. And that’s very concerning.”

A letter also sent last week to all 50 states by the DOJ said the department will investigate and prosecute any election official “who knowingly retains non-citizens on the state’s voter registration list or facilitates noncitizens in receiving and casting ballots.”

CyberScoop spoke with several Secretaries of State who said that the number one threat facing elections in their state is not from a foreign country or AI but their own federal government. 

Tobias Read, the Democratic Secretary of State for Oregon, told CyberScoop that his office is focused on providing the state’s 36 county clerks with the resources and support they need to carry out a smooth election. But he acknowledged that his office is “playing defense in a lot of ways [from] the intrusion from the federal government” that continues to assert its authority over local elections.

“If the president were actually serious about election security, he would be sending more resources to local election officials and bolstering the system rather than cutting it,” said Read.

This year, several counties in Oregon will offer voters access to a new ballot tracking system that provides text or email updates when a voter’s ballot is moving through mail and has been certified.  Reed estimated at “pennies per voter per election” and called it a good option for cash-strapped counties to assure voters their ballots are secure and properly tracked.

At the same time, Read said federal agencies like the Cybersecurity and Infrastructure Security Agency – which once regularly deployed cybersecurity and technical expertise to help states fix vulnerabilities and share threat intelligence – have largely gone quiet.

Oregon ranks in the top ten states for voter participation and relies heavily on mail-in voting.  However, state officials like Read lack confidence in the US Postal Service. Though a recent Supreme Court decision blocked an executive order giving the service control over mail-in ballot distribution, officials like Read are urging voters to take other measures to use drop boxes instead as a  safer alternative to ensure their vote is counted.

Adrian Fontes, Arizona’s Secretary of State and a Democrat running for reelection, said his office is focused on primary elections and processing the mail ballots that have been arriving “for a while.”

After Iranian hackers defaced Arizona’s candidate bio portal last year, Fontes moved to fill a widening gap: the Trump administration’s withdrawal of federal foreign interference training and support. His office is now directly supporting local jurisdictions on election security while coordinating more closely with state law enforcement, intelligence agencies, and other states.

But it’s being done with a fraction of the resources and coordination that the federal government brought to bear under both the Biden and first Trump administrations. While Fontes said he maintains positive personal relationships within the Department of Homeland Security, his office does not have a formal relationship with CISA.

“We’ve hobbled together a loose and often informal network of information sharing – that doesn’t violate any rules, it doesn’t break any laws – but it is certainly not anywhere near as robust as it would be if we had a responsible federal agency that was interested in the security of American elections,” said Fontes.

He said even if CISA offered such services today, he wouldn’t accept it, citing the lack of trust between states and the Trump administration.

“They have proven through their actions that they don’t want to be effective partners in protecting the American electorate and protecting American voters,” said Fontes. “Because of that, the clear answer, the only sensible answer for someone like me, would be to say ‘No, I don’t want the help of people I cannot trust.’ People who have demonstrably and explicitly threatened me and local election administrators of all political stripes with criminal prosecution.”

After this story’s initial publication, CISA acting director Nick Andersen said the agency remains committed working with “with critical infrastructure owners and operators to assist them in securing both the physical security and cybersecurity of the systems and assets that support the nation’s election process.”

“We provide state and local election officials, upon request, no-cost voluntary services such as the sharing of threat information, technical expertise, vulnerability scanning, and resilience-building support,” said Andersen in a statement sent to CyberScoop. “Our regional teams assist partners across the country by assessing risks, helping entities bolster defenses and improve resilience, and responding promptly to threats. We are committed to supporting state and local elections officials to protect election infrastructure and safeguard our democracy.”

Secretaries of State in Colorado, Nevada, Minnesota, Rhode Island, and others have also called the DOJ letters an attempt at federal intimidation of election officials. 

Others, like West Virginia Republican Secretary of State Kris Warner, have reiterated their refusal to hand over state voter data. On Monday, a federal judge upheld his right to do so. 

Warner wrote to the DOJ in response to say the state was “available to discuss our existing voter registration list maintenance” but “West Virginia law prohibits the disclosure of sensitive personally identifiable information contained in voter registration records.”

It’s leading some states to take new precautions. 

Read said he was working with Oregon county officials to make sure “county clerks have the number of their county counsel on speed dial” and know how to distinguish between a legitimate and illegitimate federal warrant or subpoena.

Additionally, FBI raids of election offices around the country to seize ballots records related to the 2020 and 2024 elections have been a cause for Read’s concern. By state law, Oregon and other states must keep copies of the ballot records and other election data they receive from counties for a certain time according to state law, after which they must eventually archive or destroy them according to ballot retention schedules.

Read emphasized that “it’s important to destroy those ballots at the appropriate time,”  The Trump administration has used the raids to further the impression of electoral fraud, despite the absence of credible evidence. 

“We can see when people are not on top of that, then you expose yourself to other vulnerabilities like the federal government seizing those ballots in Maricopa County [Arizona] and Fulton County [Georgia] as well,” said Read.

A former CISA official estimated that on Election Day in 2024, more than 1,000 representatives from federal, state and local governments, election technology vendors and other election stakeholders sat together in a room to communicate and coordinate.

Less than two years later, Read called his office’s interactions with CISA “minimal.” He recalled that upon taking office as Secretary of State in Jan 2025, one of his first conversations was with one of CISA’s regional advisors. A week later, those advisors were summarily fired by the Trump administration.

UPDATE: 7/14/2026, 11:15 a.m.: Updated with comments from CISA acting director Nick Andersen.

The post States are building their own election defense networks as federal support evaporates  appeared first on CyberScoop.

The Paris Peace Forum, a French non-profit that has convened world leaders on global security issues, is launching a new project to bring together international experts to assess AI-related threats to global internet infrastructure.

The Integrated Network for Trusted AI in Cyberspace (INTAiC) will tap researchers and civil society experts from government and the private sector, analyzing current AI cyber threats from the field and creating “forward-looking” reports on how the technology will impact society and what organizations can do to respond.

One of the project’s top goals is to create an international, quick-response coalition of government and business to address AI-related threats, similar to coordination mechanisms that exist in other areas of cybersecurity.

“Evidence fragmentation on AI-driven cyber threats isn’t incidental — it’s structural: those defending networks and those securing AI systems have long worked in separate spheres,” said Adrien Abecassis, policy initiatives director for the Paris Peace Forum. “That’s exactly why INTAiC is unique — it’s built to turn those fragments into one comparable reading of the threat, because this is a challenge no actor can meet alone.”

The network already lists a number of prominent businesses and organizations, including Microsoft, the Cyber Threat Alliance, the Cloud Security Alliance, Orange Cyberdefense and others.

According to the forum, INTAiC’s work will focus primarily on two, separate workstreams. One is a single and regularly updated resource for defenders to stay up to date on how AI is reshaping cyber threats. The resource is focused more on attacker capabilities, different forms of misuse and the impact on security operations rather than isolated incidents.

“The result is a common reference point, grounded in reality, that gives policymakers a clearer measure of the threat and identifies the risks most deserving of collective attention,” the Forum said in a release.

The second workstream will focus on evaluating and preventing cyber risks associated with AI, building up a base of independent third-party experts who can provide neutral or unbiased assessments of frontier model cyber capabilities. That work will pull in governments, research institutions and non-profits to develop new organizational and funding pathways to support that kind of research.

While the U.S. federal government has come a long way in recent years building up its own capacity to test and study AI cyber threats, much of the access and technical expertise around frontier model capabilities are concentrated within commercial AI companies. This has at times created concerns that federal agencies were being overly reliant on AI companies to explain how the technology worked and walk them through the possible threat scenarios.

As Anthropic and OpenAI have rolled out defensive cybersecurity programs like Project Glasswing and the Trusted Access for Cyber program, access to those models have become available to a wider group of researchers and organizations.

The Paris Peace Forum intends to brief the public further on INTAiC’s work and accomplishments in Paris later this year during the organization’s annual conference in November.

The post French nonprofit starts global intelligence and research hub for AI cyber threats  appeared first on CyberScoop.

Two new parties have been added to a class-action lawsuit against X.ai over its Grok tool including teenagers and children who say it was used by family members or other people they know to create nonconsensual deepfake child sexual assault material (CSAM).

The lawsuit, originally filed in March by three women, was amended this week to include two additional plaintiffs, Jane Does 4 and 5, who say that Grok was used to make the illegal content based on their real photos and videos.

All five of the women in the lawsuit are anonymous, and the complaint said the spread of the material had left them humiliated and ashamed.

Jane Doe 4, a female from Wyoming, said her stepfather uploaded a photo of her when she was 11 and lying on a couch to his phone. Using Grok, the stepfather created more than 7,000 CSAM-related images of her. He also shared and traded the images with others on social media platforms.

The lawsuit alleges that the stepfather opted for Grok “because the platform was less restrictive than other AI models and responded to his prompts to generate sexually explicit material using an image depicting a prepubescent minor.”

It also claims that in February, xAI did generate a tip to the National Center for Missing and Exploited Children regarding the images, but the company only submitted the original, authentic image as evidence. According to the suit, xAI did not respond when law enforcement requested the thousands of Grok-generated images based on the photo and IP address information that would have quickly helped identify her stepfather as the perpetrator.

The lawsuit states that the stepfather shot himself two days after he was arrested and charged with child exploitation crimes. His suicide added to the “extreme personal crisis” brought on by the images created through Grok. She regularly “struggles with self-loathing and disgust” as well as “extreme anxiety” at the thought that the images will be found by others online and suffer from depression, including excessive sleep and suicidal ideation when awake.

Jane Doe 5 claimed that an adult male related to one of her classmates used Grok to convert a photograph from her eighth-grade graduation into illicit material. The images were also traded and shared with others online. While the man was arrested and charged, much of the content is still available on the internet. As a result, she “feels a complete lack of control over the ongoing dissemination of the files.”

“It is impossible to know how many other child sex predators may now possess Jane Doe 5’s CSAM, nor how widely her CSAM has now been disseminated online through darknet channels and applications,” the complaint said.

The press office for xAI did not respond to an emailed request for comment from CyberScoop.

The lawsuit also adds Stability AI as a defendant, alleging the company released Stable Diffusion 1.0 as an open-weight model despite knowing it was trained on CSAM and has declined to alter or modify its guardrails in response.

According to a 2023 Stanford study, the underlying dataset used to train Stable Diffusion models was created through unguided webcrawling of internet content. That means it ingested “a significant amount of explicit material,” including CSAM. Stable Diffusion 1.0 had a classifier meant to block the generation of such images, but because of that training data, downstream developers could more easily exploit the model and create modified versions that bypass those protections.

While Stable Diffusion 2.0 introduced stronger guardrails, the lawsuit claims Stability AI rolled back those protections in response to “disgruntled” users that the new restrictions were “prude” and “unpopular.” That in turn has fed an ecosystem of jailbroken “nudify apps” based on Stability AI’s models.

“Stability AI knew that its models, once capable of generating sexually explicit images, would foreseeably be used to generate CSAM unless appropriate model-level safeguards were implemented,” the complaint said.

Stability AI did not respond to a request for comment from CyberScoop.

The post Deepfake CSAM lawsuit against xAI, Grok expands appeared first on CyberScoop.

Multiple U.S. Army internet subdomains were defaced in a 404 hijacking campaign, CyberScoop has confirmed.

As of Monday morning, error pages on two U.S. Army websites – oil.army.mil and ai2c.army.mil – displayed defacement messages visible to users. The messages denigrated President Donald Trump and United States Ambassador to Türkiye Tom Barrack, called to “FREE KURDISTAN,”  And included another line reading “Kurdish sr was here.”

One of the websites, oil.army.mil, belongs to the Army’s Open Innovation Lab, a test bed for software and cyber capabilities established in 2020. The other belongs to the Artificial Intelligence Integration Center, established in 2019 to integrate AI technologies into the Army and train personnel on emerging technologies.

The defacements were initially discovered by independent cybersecurity researcher Ronald Lovelace, who notified U.S. Army officials and CyberScoop.

404 hijacking exploits a website’s error-handling system — often by compromising a plugin, content management system, or server configuration — to control what content gets displayed when a page isn’t found, rather than breaching the site’s core pages directly. This lets malicious users insert defacement messages, malicious redirects, or other unauthorized content that visitors see specifically on error pages, sometimes making the compromise harder to detect since the rest of the site appears untouched.

Lovelace said the affected sites run on WordPress and Microsoft cloud infrastructure. It’s not clear how long the subdomains have been compromised or whether other subdomains are affected. 

“It raises the severity a decent amount because it shows it’s a bit deeper than just one single path” that’s being corrupted, Lovelace said.

However, while the defacement’s presence across multiple subdomains suggests the potential for “broad reach,” it doesn’t appear to affect all Army websites, with many  still showing normal 404 error pages.

Also unclear at this time is how the hackers gained the ability to edit error pages for those websites, whether the breach originated internally if it was due to an internal or through a third party breach, and whether the intrusion extends beyond limited website defacement.

The websites were taken offline after CyberScoop reached out to the Army for comment. An Army spokesperson told CyberScoop that the pages were hosted on a legacy third-party platform that is not connected to the Army’s enterprise network and have since been removed.

The spokesperson said incident response by Army cyber investigators remains ongoing, and that it’s too early to say whether the third-party platform will be patched or discontinued. 

“We are aware of unauthorized defacements on the error pages of oil.army.mil and ai2c.army.mil, which are hosted on a legacy, non-authoritative platform,” said Army spokesperson Maj. Sean Minton in a statement. “Technical teams took immediate action to mitigate the issue, and the affected pages have been secured. The Army takes all cyber incidents seriously and is actively investigating this matter to enforce our strict cyber defense and network security standards.”

It’s not clear who is behind the defacement beyond  the references to Kurdistan— a geographic region spanning parts of  Turkey, Iraq, Iran and Syria that is home to more than 30 million Kurdish people. The Kurdish separatist movement has fought for decades to establish an independent nation, and defacing government websites has long been a popular tactic among Kurdish hacktivists.

Trump and Barrack drew the ire of Kurdish proponents earlier this year for seeming to back a Syrian government military campaign to reestablish federal control over Kurdish-majority lands.

It’s not the first time that Army websites have been seemingly compromised by foreign hackers. In 2015, Army officials had to temporarily shut down major websites, including the Army main home page and the Department of Defense’s U.S. Strategic Command, after hackers from the Syrian Electronic Army defaced them.

The post US Army websites defaced with pro-Kurdish sentiments, insults to Trump appeared first on CyberScoop.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The post US lifting export control restrictions on Anthropic’s Mythos, Fable appeared first on CyberScoop.