Are clearinghouses the answer to the AI hacking threat?

This summer, the White House rolled out the Gold Eagle Initiative, billed as an “unprecedented” effort to coordinate cybersecurity vulnerability disclosure across government and industry. It was easy to miss this news with all of the other safety concerns in the world of AI, but the timing isn’t a coincidence — and neither is the terminology. Gold Eagle is just one in a fast-growing category of security clearinghouses. Although it has already been scaled back once, recast in an August CISA fact sheet as a narrower triage layer rather than the sweeping coordination system first announced, it still indicates a trend we’re seeing in the world of cybersecurity.

To understand what a clearinghouse is, and why the number of them is increasing, it’s important to understand what has broken — namely (and hugely), the vulnerability disclosure process. For many years, vulnerability disclosure worked like this: A researcher found a bug, reported it privately, and honored an embargo period that gave vendors time to patch quietly (and hopefully quickly) and ship the fix before the bug was made public. This gave the good guys a chance to patch before the bad guys had a chance to use the vulnerability for bad guy things. The process was tedious, but it worked because the economics worked — historically, only nation-states had the resources to hunt for — or reward teams of experts to hunt for — undisclosed vulnerabilities at scale.

Of course, AI has changed all that — all of that. The average gap between the discovery of a vulnerability and its weaponization has collapsed from something measured in years to negative seven days. The bad guys are finding CVEs and exploiting them long before the good guys have had a chance to patch them. A terrifying prospect, to be sure. And it’s something I embarrassingly admit has happened to me personally several times recently.

Frontier AI models are democratizing the finding, chaining, and weaponization of flaws faster and cheaper than any team of human experts. Indeed, it’s the chaining — and AI’s mastery of it — that may be the most debilitating. In chaining attacks, two or three low-severity and moderate-severity bugs get stitched together by an AI system. On their own, these bugs are pretty unremarkable. However, by joining the “right” ones together (something AI is very, very good at), a model can achieve a full compromise.

Several major AI labs have already disclosed that their own models were used to breach unrelated systems, evidence that the old assumption about who can afford to hunt for exploits no longer holds. Anyone with API access to frontier models can now run what amounts to a small (and very efficient and intelligent) hacking team. Worse, these models can be resold illicitly to embargoed countries, and there’s an entire ecosystem of jailbreaking models to get them to do even more nefarious things.

Why clearinghouses?

At its core, the AI hacking threat is a scale problem that software development, be it proprietary or open source, was never set up to handle. It’s not just big projects like Linux and Kubernetes that are targets; it’s every one of the libraries underneath every application. There are literally millions and millions of packages available on PyPi (Python), npm (Node.js), RubyGems (Ruby), Crates (Rust), NuGet (C#/.NET), and Packagist (PHP), along with tons of smaller language ecosystems. These are ecosystems that power almost everything built today.

AI-generated vulnerability reports arrive faster than volunteer maintainers can read them, let alone verify and fix them. A single, real bug can spawn a dozen duplicate CVE filings from different automated scanners, each with its own tracking number, essentially serving as a distributed denial of service attack on the upstream. And, this is from the good guys, trying to do the right thing.

This “gap” (to put it mildly) is what clearinghouses are designed to close. Historically, a bug fix loop looks like:

1. Discover bug
2. Report to maintainer
3. Honor embargo
4. Maintainer patches
5. Release fix to the world

In the world of clearinghouses, here’s what it looks like:

1. Discover bug
2. Report it to clearinghouse
3. Receive patch
4. Clearinghouse handles the rest (handle embargos, reporting upstream, etc.)

It could look even as simple as:

1. Sign up for clearinghouse
2. Receive patches from clearinghouse
3. Clearinghouse handles the rest

The clearinghouse is essentially a trusted intermediary, run by a vendor, an association, or other entity, which triages the flood of AI-generated reports, identifies which are real, accepts or builds a fix, validates it, sends it to customers and partners, and only then works to merge it back into the upstream project. This still must be done at a pace that the upstream can handle.

There are also ethical dilemmas to handle here. It is not fair to force upstream projects to operate at the speed that for-profit and

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