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 AI-focused executive order President Donald Trump signed last month gave the Treasury Department, the National Security Agency, and the Cybersecurity and Infrastructure Security Agency (CISA) 30 days to establish a new “AI cybersecurity clearinghouse.” The deadline passed last week.

The clearinghouse is meant to coordinate the scanning, discovery, and validation of software vulnerabilities in critical infrastructure, and then prioritize how those vulnerabilities get patched and distributed.

It’s the right problem to solve. The question now is whether what is created will actually solve it.

The risk is that urgency produces something that looks like a clearinghouse, but functions like a committee: collecting information, convening meetings, and then stalling when it gets to the hard part.

Going beyond bug discovery is mission critical

It’s counterintuitive at a moment when AI-assisted vulnerability discovery is advancing rapidly, but the hard part is no longer just finding bugs. Those of us working at the intersection of AI and cybersecurity know where the real bottleneck is. HackerOne has seen it firsthand as a launch partner in Patch the Planet, OpenAI‘s initiative to use AI to find and fix vulnerabilities in critical open-source software at internet scale. The lesson underpinning that work, and informed by more than a decade of running vulnerability disclosure programs, is consistent: AI tools can surface vulnerabilities faster than anyone can act on them. What lags behind is everything that comes after discovery: deciding which findings are real, assessing severity in context, writing and testing a fix, and getting a patch accepted and deployed by the people responsible for maintaining the affected code.

Experienced human reviewers frequently disagree with AI-assigned severity ratings, because a model cannot see a project’s threat model or operational context. Software providers, especially the many volunteer open-source maintainers that so much of today’s digital infrastructure rely upon, face a relentless queue: verify the claim, assess the importance, write the patch, coordinate disclosure. AI has accelerated the incoming volume without yet equally accelerating our people and processes’ capacity to manage it. Better bug-finding tools mean you find more bugs. The improvements that really matter are the ones that help defenders push patches out and get them deployed faster.

That lesson should sit at the center of how the clearinghouse is designed.

If the clearinghouse focuses primarily on scanning coordination, which the executive order’s text emphasizes, it risks widening that gap rather than closing it. A body that finds more vulnerabilities but cannot move them to resolution is not a security win. At national scale, it is a backlog generator.

Laying a foundation for success

The administration can get this right, but it requires building the correct infrastructure now, not layering it on later.

The clearinghouse needs to do more than coordinate scanning. It needs to actually triage the results. Its core job should be filtering reports to identify which findings are truly credible, exploitable, and consequential for critical infrastructure. Using shared validation standards and risk-based prioritization, it can determine what warrants a national response. Otherwise, it’s just automating bigger backlogs.

Second, the clearinghouse also needs to tackle something more fundamental. Defenders don’t have the resources to respond to what gets reported. Vulnerabilities in critical infrastructure often live in open-source code maintained by small teams or individuals with no formal obligation to respond to disclosures and limited capacity to act quickly. The clearinghouse should work with the National Institute of Standards and Technology (NIST) to develop guidelines for open-source maintainers on structuring repositories and workflows to speed up patch review and deployment.

These guidelines should include how to use AI-assisted patching and clarify what downstream consumers of open-source code should do to help maintainers address vulnerabilities.  Federal policy should create incentives for downstream users to share responsibility for remediation through funding, engineering support, AI-assisted patch development, and procurement requirements that reward participation in coordinated vulnerability response.

Third, the clearinghouse should treat software bills of materials (SBOMs), the structured inventories of the components that make up a software product, as foundational infrastructure. SBOMs are what make it possible to trace where a vulnerable component lives across the supply chain. Without them, validated findings won’t be fixed fast enough at scale.

Finally, the clearinghouse should measure success based on what is fixed, not based on what is discovered.  Agencies need to publish data on validation rates, time-to-patch, adoption of fixes, and recurring classes of vulnerabilities. These metrics help AI systems, software vendors, and policymakers to continuously improve how vulnerabilities are addressed.

Most importantly: the agencies standing up this clearinghouse should resist the temptation to build its operational model from scratch. The private sector and the open-source security community have years of experience running exactly the kind of vulnerability intake, triage, and coordinated disclosure workflows the clearinghouse needs. The executive order wisely calls for voluntary collaboration with industry. That collaboration should be structural, not advisory, embedded in how the clearinghouse operates from the start, not bolted on after the architecture is already set.

The clearinghouse can work. But the challenge is no longer finding vulnerabilities. It is building a system that can turn discoveries into action. That is how its success should be measured.

The post Found fast, fixed slow: The gap the AI clearinghouse must close appeared first on CyberScoop.

The Trump administration issued a revised executive order Tuesday focused on artificial intelligence, offering a significantly pared-back vision for the federal government’s role vetting AI systems compared with a draft version that was spiked weeks ago.

The order keeps in place the administration’s largely voluntary framework for companies to engage with the federal government around testing new models before release, but appears to considerably weaken or loosen provisions that had been opposed by industry.

Under the order, AI companies would voluntarily provide the federal government access to frontier models before release, but now it will be for “up to” 30 days instead of the 90-day timeline included in previous drafts.

It also explicitly states that nothing in the program will be construed as mandatory or part of a federal licensing or permitting regime, and gives AI companies significant influence to help define what models would and would not be covered under for testing.

It also states that all federal testing and access to the models would be subject to “confidentiality, cybersecurity, insider-risk, and intellectual-property protection, use, and nondisclosure requirements.”

Section one of the order highlights the central friction that has plagued the Trump administration’s AI policy since assuming power: While the White House increasingly sees national security implications in the rapid release of frontier models from the private sector, it has also been one of the loudest critics of regulating the technology for fear it could harm American businesses.

“The United States continues to lead the world in Artificial Intelligence (AI) because of the enormous talent and innovation of our AI industry, and because we refuse to stifle this innovation with overly burdensome regulation,” the order reads.

That argument was bolstered in recent days as industry members and top advisers to Trump, like tech investor and AI czar David Sacks, lobbied against previous draft language, arguing it would put too much of a regulatory burden on U.S. businesses.

On X, Sacks called the revised EO, including changes reducing the government’s access from 90 days to up to 30 days “a game changer” because it would allow frontier labs to comply without delaying new model releases. He also said the discussions he’s had with the White House indicate that not all new model releases would be subject to even that level of scrutiny.

The White House characterization that the order is not a program for conducting oversight of all new AI models “is completely consistent with the discussions that I have participated in, where it was agreed that the EO is intended to apply only to models that represent a meaningful step-change in cyber capabilities (eg Mythos), not to incremental version numbers of existing models,” Sacks wrote.

The order also puts the Department of Treasury at the head of a new interagency cybersecurity clearinghouse on AI, where the private sector, critical infrastructure operators and federal agencies voluntarily collaborate to coordinate and deconflict scanning for software vulnerabilities, discovery and validation and remediation activities, like patching.

Treasury, the Cybersecurity and Infrastructure Security Agency, the NSA, the Office of the National Cyber Director and other agencies would also be responsible for developing classified benchmarks that would be used to identify or flag the kind of advanced cyber and hacking capabilities that agencies are interested in testing.  

Questions linger over implementation, politicization

Consisting of less than 1200 words, the directive is vague in many areas about exactly how implementation will work.

“On frontier capability access, vulnerability discovery for critical infrastructure, and sharing with trusted partners, many questions remain,” wrote American Enterprise Institute fellow Ryan Fedasiuk.

Senator Mark Warner, D-Va., said the order would help the White House “begin to grapple” with the threats that new frontier models and their hacking capabilities pose to critical infrastructure and praised certain provisions, like putting the NSA in charge of classified testing of new models. But he was also sharply critical of the administration’s about face on the need for federal scrutiny of emerging AI technologies.

“Once again, the Trump administration has belatedly discovered the need to redo something it hastily dismantled in its first year,” Warner said in a statement. “While this course correction – a rehash of proposals contained in the last administration’s 2023 executive order, bipartisan congressional legislation, and each of the last three years of intel authorization bills the Senate Intel Committee has passed – can begin to grapple with widespread impacts that new frontier models will have on our critical infrastructure, it can’t undo the years wasted on dismantling some of the most vital pillars of our nation’s cybersecurity response, including key information sharing initiatives and the federal agency established to protect the security of U.S. critical infrastructure.”

Warner also said he will be “watchful” for indications the administration may politicize any testing regime, for instance, such as using the partnerships “to pressure U.S. firms into making changes to their products or Terms of Service to suit partisan or legally questionable objectives of the president and his allies.”

The administration’s lighter touch approach around voluntary testing yielded approval from some experts who have traditionally been more in favor of regulation, but who also expressed similar worries about the downsides of putting the federal government in charge of vetting AI models.

Samir Jain of the Center for Democracy and Technology, said that while AI models pose real cybersecurity threats to critical services, the order “attempts to avoid the deeply concerning implications of a mandatory licensing regime for release of new models.”

“Testing and benchmarking programs are important to promote cybersecurity and address other risks,” Jain said in a statement. “However, the EO should not become a mechanism for the Administration to punish companies for political or other arbitrary reasons, and so we will be closely monitoring the details of its implementation as they emerge.”

You can read the full order on the White House’s website.

The post Trump administration releases scaled-back AI executive order appeared first on CyberScoop.

President Donald Trump said he would postpone the release of an executive order that would set up a 90-day testing and vetting regime for frontier AI models, hours before the White House was set to publicly announce the signing. 

Speaking to reporters in the Oval Office Thursday, Trump said he opted to delay the order “because I didn’t like certain aspects of it” and expressed concerns that it could harm U.S. AI industry competition with countries like China. 

According to multiple sources, a draft version of the order circulating in the last 24 hours would have set up a voluntary testing regime between the U.S. federal government and frontier AI companies that would allow the government to study new models for 90 days before they’re publicly released. In addition to the government, the draft order would also facilitate access to the models for cybersecurity testers in critical infrastructure sectors, like finance and healthcare.

The draft order empowered the National Security Agency to conduct classified evaluations of frontier AI models, while the Department of the Treasury would have set up a new information sharing agreement between AI companies and cybersecurity defenders in critical infrastructure.

Other agencies, like the Office of the National Cyber Director, the Cybersecurity and Infrastructure Security Agency and the National Institute for Standards and Technology, would also be involved in defining which models are covered under the vetting regime.

In some sense, the order would formalize an already cooperative relationship between AI companies and governments like the U.S. and UK, where tech-focused agencies and regulators have already been provided access to previous models ahead of their release for testing and evaluation. 

A former federal official who has seen the latest draft circulated before Thursday’s announcement told CyberScoop that based on their conversations with the administration, the order was intended to facilitate more robust testing from government agencies compared to evaluations conducted for previous models. They said that is in part a reflection of the federal government’s maturing understanding of AI technology over the past five years.

“In the past there has been containerized optionality for the intelligence community and others to take a look at things, but it was really a lot of hand holding [from AI companies] and self-explanation of what they expect this thing to do,” said the official, granted anonymity to discuss sensitive conversations with the administration. “And now the government is coming forward and saying now we feel we’re prepared enough for you to just give us your tool…and we’ll go from there.”

But it also represents a stark pivot by the Trump administration, which came into office openly dismissive of AI safety policies and arguing that they would inhibit U.S. industry. Trump’s latest comments in delaying the order echo those same attitudes. 

The former official said that while the Trump White House doesn’t view its mission as telling AI companies “don’t develop AI that can do X, which was perceived to be the previous administration’s role,” they also acknowledged the administration’s early rhetoric on AI regulation has painted them into a corner. 

“I think the biggest challenge the administration has is that their tone was ‘no institution of guardrails’ and they don’t have a better word for making sure that the capabilities of emergent frontier models don’t disrupt security than to say ‘let’s test it and institute guardrails,’” the official said.  

While debate about how best to regulate AI-related harms continues, most agree there are genuine national security concerns around the technology.

Ram Shankar Siva Kumar, founder of Microsoft’s AI red team, told CyberScoop that in 2019, his staff consisted of himself and a few other security and machine learning specialists. Now a much larger staff of technologists are supported by specialists in psychology, linguistics, bioweapons and other fields.

“Because of frontier harms, what we have done has really morphed,” Siva Kumar said.

The United States, along with Israel, Russia, Ukraine and others have already deployed AI in targeted military operations or integrated the technology into their larger command and control structure. AI is being used to supercharge drone warfare, global hacking campaigns, and sophisticated surveillance and targeting of military personnel and civilians, imbuing the engineering choices of frontier AI companies with life and death consequences.

Some congressional members who previously opposed allowing AI to make autonomous kill decisions on the battlefield have been reconsidering their position.

Rep. Don Beyer, D-Va., who co-chaired the Congressional AI Caucus and was appointed to a bipartisan AI task force in 2024. said that while he thinks “we need to guard against dehumanizing” those decisions, he also worries that adversarial countries will use the same technology against the United States.

“It’s like if we say that Americans have to have a human in the loop and the Chinese don’t have to have a human in a loop, the non-human one will beat the human one every time,” Beyer said at an AI conference in Washington D.C. earlier this month.  

Meanwhile, experts have been increasingly concerned about the technology’s impact on cybersecurity, as current models are remarkably good at finding software bugs and vulnerabilities, while newer models like Anthropic’s Mythos and OpenAI’s Daybreak are capable of chaining together multiple exploits to conduct more sophisticated attacks.

While state-sponsored hackers are experimenting with the technology and using it to gain targeted efficiencies in their hacking operations, cybersecurity experts in the private sector and law enforcement agencies say the technology has mostly benefitted cybercriminals and scammers.

The post Trump postpones executive order focused on AI security  appeared first on CyberScoop.