AI assistance was explicitly disclosed on yesterday's. Today's has Claude as one of two contributors on this GitHub Pages site at least so it's also very likely.

Agents are capable of finding this kind of stuff now and people are having a field day using them to find high-profile CVEs for fun or profit.

Security through obscurity

The Anthropic researchers have said their flow is as simple as:

1. Pick a file to seed as a starting place.

2. Ask the LLM (in an agent harness) to find a vulnerability by starting there.

3. If it claims to have found something, ask another one to create an exploit/verify it/prove it or whatever.

4. If both conclude there is a vuln, then with the latest models you almost certainly found something real.

Just run it against every file in a repo, or select a subset, or have an LLM select files with a simple "what X files look likely to have vulns?".

So basically yes, it is that simple. It's just a matter of having the money to pay for the tokens.

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I agree. It is not like Mythos or other LLMs are insanely smart/superhuman. Many of these vulnerabilities could be discovered fairly easily by trained human experts as well. The problem is more that it requires an insane amount of attention and time of highly-paid experts to shake out these issues vs. an LLM that never gets tired and can analyze a large amount of code at low cost.

Linus' law was wrong because there were never enough (qualified) eyeballs to check the code. LLMs provide an ample supply of eyeballs (though it's not a benefit to open source, since proprietary developers can use the same LLMs).

Same applies to them being good enough to program, but many are so focused on source code generation that they don't get the whole picture.

Thanks to agents and tool calling, there are now business cases that can be fully described by AI tooling, the next step in microservices, serverless and what not.

Naturally with a much smaller team than what was required previously.