Valve is continuing to broaden SteamOS support beyond its own hardware, with the latest beta adding "initial gamepad support" for a few recent gaming handhelds and improving compatibility for a few others. Ars Technica reports: Those newly supported handhelds include MSI's Claw 8 EX AI+, the extremely expensive, extremely powerful handheld built around Intel's Arc G3 processor that launched this summer. While SteamOS has long included beta support for "other AMD powered handhelds," Valve has only recently been working on offering compatibility with Intel-based handhelds like the MSI Claw line. This weekend's update suggests the company hasn't abandoned those efforts and that Intel chips aren't being left out of the SteamOS conversation. SteamOS 3.8.25 also newly supports the controllers built into Ayaneo's Android-focused Pocket S2 and the more recently released Windows-based Konkr Fit. Valve also says the new OS offers "improved support" for older MSI Claw devices and the OneXPlayer line of portable gaming PCs. This weekend's update follows June's SteamOS 3.8 update, which stressed "improved compatibility with recent Intel and AMD platforms." That update specifically called out improved controller support on handhelds from the likes of GPD Win, Anbernic, and OrangePi, as well as improvements for various Lenovo Legion Go, MSI Claw, and OneXPlayer devices. The report notes that the new SteamOS update also updates the Linux driver powering the new Steam Controller, "allowing it to work with 'native controller applications' even when Steam is not running."

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Remote, unauthenticated attackers could exploit the bugs to cause a denial-of-service (DoS) condition.

The post Cisco Warns of High-Severity ClamAV Vulnerabilities With Public PoC appeared first on SecurityWeek.

PUBLIC DEFENDER By Brian Livingston Realistic-looking videos that are the most-often recommended by the popular TikTok social media app regarding health care contain no real people 40 percent of the time, a new analysis of the site’s mini-movies has found. Many of TikTok’s top-rated videos on every topic are AI-generated but look exactly like actual […]

UI changes, lost productivity, untransferrable knowledge... Long-time Slashdot reader theodp notes the folly of switching to an unfamiliar new environment to be "more productive"... Champions of the switch often insist workers will be able to hit the ground running and get the same or even more work done from the get-go in the new unfamiliar environment because it's 'more productive', a conclusion often shaped by conventional wisdom or their 'gut' feeling. Decision makers may also mistake their own 'light-user experience' for universal experience and believe all platforms are fungible. And while it's one thing for a business to delay their delivery dates and increase costs of by introducing a switch in computing environments that's of unquantified and possibly dubious value, how about when a switch in computing environments is made that affects schoolchildren's ability to learn for a period of time? While a MacBook Neo is indeed cute, what might be the possible consequences of a decision to move 25,000 students off Windows and onto MacBook Neos...? Or even the impact on a single kid who moves from a MacBook school to a Windows or Chrome school (or whose home computing environment isn't aligned with their school). Or students whose teachers decide learning in their classrooms should now additionally require an understanding of the Minecraft computing platform/UI (used in over 40,000 school systems.). Learning Management Systems and now various AI Edtech tools used by schoolkids are also subject to change. "Even if students and schools eventually benefit from a new platform, one wonders how much learning is lost during the switch," theodp asks, "and what the financial and educational ROI and payback period look like." Are there bigger problems than we realize? Share your own thoughts and experiences in the comments. What are the costs of switching between Windows, MacOS, and Chrome?

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"I've been a Linux IT administrator for 30 years now," says crhylove (Slashdot reader #205,956), in a new video on his YouTube channel. "I used to be a data center architect, at one point I was the IT director for 13 school districts... I just haven't been happy overall with all the other operating systems out there... " I've been working really diligently for the last few weeks on a new distro called Spaced Linux that is exactly what I want to use. It's heavily focused on the [zoom-friendly 3D] Compiz desktop, very efficient using the rolling Devuan Ceres backend, and avoids dependency hell by promoting mostly Flatpak usage. It's still early, but it's already working great for me, it's been my daily driver for a few weeks now. There's also user-friendly themes — everything from Linux, Android, GeoWorks, and Mac OS X to Windows 11, Windows XP, and even Windows 3.11 (dark or light theme). And you can also run Windows apps under Wine. "And let me tell you, Nvidia runs perfectly on here now..." he adds in the video. "It just works really, really well out of the box." He's now working on voice-command features, bur argues that Spaced Linux "is already extremely usable, extremely efficient, extremely fast, and extremely reliable.... And it's only going to get better and better."

Read more of this story at Slashdot.

Senators from both parties Thursday probed Trump administration officials about whether federal agencies and foreign governments are coordinated enough in the battle against scammers, something witnesses told the Foreign Relations Committee they were working to remedy.

At least 13 federal agencies have authorities to counter scams, raising questions about whether someone needs to be in charge of all those efforts. And while there was some bipartisan sentiment at Thursday’s hearing that the Trump administration has taken good actions to battle scammers, both lawmakers and administration officials said that scam operations have demonstrated that cracking down on them in one place often just leads to them going elsewhere.

Sen. Pete Ricketts, R-Neb., compared the situation to an international initiative that gained prominence in the 1990s to counter drug trafficking, Joint Interagency Task Force South.

“Given that today’s scam centers are similarly transnational, combining cybercrime, human trafficking, money laundering and cryptocurrency, has the threat reached the point that we should establish a comparable multinational coordination mechanism?” he asked.

Sen. Jeanne Shaheen, D-N.H., focused on federal coordination: She paraphrased a former federal official who said, “there is nobody that is heading that effort up across agencies. We need to treat this like combat, and so we need somebody in charge.”

Shaheen, the top Democrat on the panel, is a co-sponsor of the bipartisan Scam Compound Accountability and Mobilization (SCAM) Act, which seeks to unify federal efforts on the subject.

A State Department official told Shaeen scammers were a national security priority for President Donald Trump, and that his executive order on the topic sought to tackle coordination.

“I do understand that this is a whole-of-government approach, and many agencies are focused on this,” said David Bedard, deputy assistant secretary at State’s Bureau of International Narcotics and Law Enforcement Affairs “The Action plan that was directed by the president is currently in the interagency review process to deconflict some of the concerns that you have raised. We certainly think the task force that will be implanted through the executive order will solve the problems you might be referencing.”

There’s also an international plan under the task force, he said. Currently, the administration shares intelligence on scammers with foreign allies, and Interpol has “productive” channels to work through there and is setting up its own task force, Bedard said, but there are concerns about other countries taking similar, duplicative action.

There have been signs of progress on the international front, Bedard and another State Department witness told the panel.

Michael DeSombre, assistant secretary at the Bureau of East Asian and Pacific Affairs, said Trump has raised the subject with Chinese President Xi Jinping, and that China has used its influence in Asia as its own citizens have become scam victims. Still, there’s been more progress in countries where the United States has stronger relations, such as Cambodia, than in those where ties aren’t as close, like Burma and Laos.

In Cambodia, one key has been pursuing scam center bosses first and foremost, Bedard said.

The post Capitol Hill wants to know if executive branch, foreign allies coordinated enough to combat scams appeared first on CyberScoop.

AI Security Institute reports Anthropic and OpenAI models going rogue against real people, organizations, and open source projects.

The post AI Agents Targeted Real People and Projects During Cybersecurity Tests appeared first on SecurityWeek.

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

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

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

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

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

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

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

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

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

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

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

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

The post Dem senators criticize Trump administration decisionmaking on AI security risks appeared first on CyberScoop.

Amazon’s security researchers say a hacking group tied to North Korea targeted small, little-noticed software packages more than a year before it struck one of the internet’s most widely used programming tools.

The company’s threat intelligence team said Wednesday at a media roundtable at its Arlington, Va., offices that the same group linked to the recent compromise of the open-source axios software library also planted malicious code in a package called typo-crypto in March 2025, a full year before the axios breach. Researchers found the connection while tracing domain records tied to the axios attack back to earlier activity.

“We believe the March 2025 typo-crypto campaign was a rehearsal,” said CJ Moses, Amazon’s chief information security officer, adding that the target’s small scale let the group test its methods “without putting that on the big stage.”

Amazon said the group also compromised two other packages, debug and chalk, in September 2025. Until now, those three incidents had not been publicly linked to the same actor. Security researchers track the group under several names, including UNC1069, Sapphire Sleet and Stardust Chollima. 

Axios, debug and chalk are code libraries used by software developers around the world to build applications. Axios alone is downloaded more than 100 million times a week. “That number represents real organizations putting real code into production systems every single week,” Moses said.

In the typo-crypto case, the malicious file was named “core.js” and was made to look like a legitimate, unrelated package called core-js. Amazon said the file activated only when it received a specific numeric input, then reached out to a server controlled by the attackers to download a second piece of code. That second stage was written differently depending on whether the infected computer ran Windows, macOS or Linux. The code combined encoded text with a cipher, a method Moses said was meant to slow down analysis, including by AI-based review tools, without relying on heavy encryption.

Amazon said the typo-crypto package had few downloads compared with axios, debug or chalk. Researchers believe that initial target served as practice, letting the group refine its approach before turning to more widely used software. “They did what a lot of people do: crawl, walk, run,” Moses said. 

In each of the four cases, Amazon said, the attackers built a relationship with a maintainer who already had access to a package, then used that access to publish an update containing hidden code. “They didn’t break through a window,” Moses said. “They basically earned the trust of an employee to hand them the keys.”

Cybersecurity firm Wiz separately found that about 1 in 10 cloud computing environments were affected by the debug and chalk incident within a two-hour span, a finding Moses cited to illustrate how fast the impact spread. “Going from there not being a vulnerability, to there being a vulnerability, to there being an exploited vulnerability … used to be days to weeks. Now it’s hours to minutes,” he said.

Rick Anthony, senior engineering manager at Amazon Web Services, said the research further shows how attackers face two basic problems in these types of incidents: getting malicious code into a package that will eventually run inside an organization, and keeping that code hidden from developers or security tools. He said groups are increasingly building reputations as legitimate contributors over time. 

“Let me get my package deployed in as many places as possible so that I can spring the trap later,” said Anthony, describing the mindset behind the approach.

Researchers said generative AI has made it easier for attackers to produce code, documentation and contribution histories that look authentic. Anthony also described a technique in which attackers register package names that AI coding tools sometimes generate by mistake, so a developer following an AI suggestion could install malicious software without making any typing error of their own.

The findings come two years after a separate incident involving a program called xz-utils, in which an attacker spent time gaining the trust of the software’s maintainers before inserting a backdoor. Moses pointed to that case as an early example of a pattern now appearing “at scale” and tied to a nation-state.

Since that incident, separate groups have been running roughshod over open-source software. Another group known as TeamPCP has compromised and injected malicious code into more than 1,000 software packages over a four-month span this year. 

The post A little-known npm package was North Korea’s warm-up act for the axios hack appeared first on CyberScoop.

The company will use the fresh investment to grow its customer success and AI R&D teams.

The post OT Security Startup Frenos Raises $1.52 Million appeared first on SecurityWeek.

ISSUE 23.30.1 • 2026-07-28 By Susan Bradley • Comment about this alert Today, I want to tell you that I’m less worried about the side effects of this month’s security updates from Windows, Apple, and Linux distros. I’m more concerned about security in the AI industry in general. July saw the first side effects of […]

Code found in the iOS 27 beta suggests Apple is developing a system that could restrict leased iPhones when customers fall behind on payments. The discovery follows a recent Bloomberg report that Apple may soon launch a new "Apple Upgrade" leasing program, allowing customers to pay for hardware through monthly installments. 9to5Mac reports: The code describes a system called App Managed Features, which allows an authorized financing or provider app to enroll an iPhone and perform ongoing status checks. If the contract is no longer in good standing, Apple's system services can place the iPhone in "Restricted Mode," which blocks access to most apps until the payment or contract issue is resolved, while keeping a small set of apps available. The fixed allowlist currently found in the iOS 27 beta includes: Accessibility Reader, App Store, Health, Magnifier, Phone, Clock, Settings, Wallet, Passwords, and the Restricted Mode interface itself. Apps that can send critical alerts, such as Messages, Home, and certain medication or safety apps, may also remain accessible. However, the provider appears to have some control over those exceptions. The code does not appear to cancel, suspend, or otherwise modify App Store subscriptions associated with blocked apps. As a result, a subscription could continue billing even while access to its app is restricted. Additionally, there isn't a fixed number of missed payments that automatically triggers the restrictions. The financing provider's app decides when to lock the device based on its own policies. Finally, the new framework also introduces a new type of activation lock called "Partner Finance Lock," which is meant to prevent users from erasing, reselling, or stripping a restricted device for parts.

Read more of this story at Slashdot.

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 home appliance giant LG Electronics USA said this week it plans to suspend any apps built for its smart TVs that turn one’s television into an always-on residential proxy node. The move comes less than a month after researchers found that more than 42 percent of games and other apps available for download on LG’s webOS store allow unknown third-parties to route their Internet traffic through a user’s TV.

On July 2, we featured research by the security firm Spur that examined the prevalence of residential proxy software development kits (SDKs) in smart TV apps. Spur found more than 42 percent of apps available for download on LG smart TVs include SDKs that turn one’s television in a proxy node indefinitely, and that more than a quarter of the apps made for Samsung’s Tizen operating system had similar residential proxy components.

Responding to questions about Spur’s research, LG Senior Vice President John Taylor told KrebsOnSecurity the company was working with app developers to remove the residential proxy option from their apps on the webOS platform. Developers that fail to comply, he said, will find their apps suspended.

“A residential proxy network is not an intended use for LG smart TVs, and LG Electronics is working with developers to remove the residential proxy option from their apps on the webOS platform,” Taylor said. “If this option is not removed, these apps will be suspended.”

Taylor said LG is committed to keeping residential proxy networks out of its smart TV apps going forward, and that the company’s review of those apps is “well underway now.”

“As part of our ongoing efforts to enhance platform quality and the user experience, LG will continue to strengthen our evaluation process for developer-submitted apps, including those that incorporate residential proxy SDKs,” Taylor wrote in an emailed statement.

App makers looking for ways to monetize their creations can turn to residential proxy providers, which pay developers to include SDKs that turn the user’s device into a residential proxy node that is rented to paying customers. In the case of LG and Samsung smart TVs, Spur found residential proxy SDKs bundled with everything from simple games like Pac-Man to screensavers and file utilities.

Spur’s report found the residential proxy network Bright Data accounted for a majority of proxy SDKs across both Samsung and LG smart TVs. In a statement shared with KrebsOnSecurity, Bright Data said its network is built on consent and responsibility and operates by LG and Samsung terms.

“Every peer opts in through a dedicated screen and receives value in return; every customer is vetted, and our practices have now undergone a second independent audit by PwC,” the statement reads. “We remain committed to an open, transparent internet where legitimate businesses, researchers, and institutions can responsibly access data that lives in the public domain.”

Bright Data and other proxy providers named in Spur’s report all say they follow rigorous know-your-customer processes to validate legitimate uses of their services, which is often heavily tied to content-scraping activities by said customers. The proxy companies also say they incorporate technological countermeasures to prevent proxy service customers from being able to interact with and control other devices on the proxy user’s local network.

Spur argues the problem is not that residential proxy networks exist, but rather that they are being embedded at scale in devices that most consumers do not think of as computers and are not equipped to audit.

“A one-time consent prompt buried in a TV app is not a substitute for meaningful transparency, ongoing control, and platform oversight,” Spur’s Trevor Sutter wrote. “The risk is amplified when consent comes from individuals within the household who use the device but shouldn’t give consent, such as minors.”

LG’s announcement that it is culling residential proxy SDKs from its app store is welcome news, but the company recently came under fire for another questionable partnership: Pimping McAfee security products via software drivers included in its high-end LCD monitors.

Earlier this week, the Youtube channel Gamers Nexus showed that certain LG LCD monitors will automatically install an app that promotes paid McAfee antivirus subscriptions, and that the app arrives through Windows Update without an approval prompt.

Update, July 22, 1:06 p.m. ET: Added statement from Bright Data.

BrianFagioli writes: Valve has released Proton 11.0-1, a major update to its Windows compatibility layer for Linux that makes more games playable while fixing a long list of bugs affecting existing titles. The release restores compatibility for many EA games after a recent EA App update, moves classics like Resident Evil (1996), Resident Evil 2 (1998), Dino Crisis, and SHOGUN: Total War from Proton Experimental into the stable release, and adds support for games including Gothic 1 Classic, X-Plane 12, Breath of Fire IV, and Deadly Premonition. Valve also fixed crashes in HELLDIVERS 2, restored No Man's Sky VR support, improved Steam Overlay compatibility with EA games, addressed KDE and GNOME desktop issues, and rebased Proton on Wine 11.0 with updated graphics components. The full list of changes can be found here.

Read more of this story at Slashdot.

The audits are reportedly being spearheaded by CISA’s Attack Surface Evaluation team, a specialized unit tasked with conducting digital defense assessments and simulated hacking exercises.

The post CISA Reportedly Using Anthropic’s Mythos to Scan Government Software for Flaws appeared first on SecurityWeek.

The updates fix vulnerabilities in WebKit, the kernel, WebRTC, Web Extensions, and other components affecting iPhone, iPad, Mac, and Safari users.

The post Apple Patches Dozens of Vulnerabilities Across iOS, macOS, and Safari appeared first on SecurityWeek.

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.