Do you have a link to that? A rather important piece of context.

Wasn't trying to downplay this submission the way, the main point still stands:

But finding a bug and exploiting it are very different things. Exploit development requires understanding OS internals, crafting ROP chains, managing memory layouts, debugging crashes, and adapting when things go wrong. This has long been considered the frontier that only humans can cross.

Each new AI capability is usually met with “AI can do Y, but only humans can do X.” Well, for X = exploit development, that line just moved.

Not all CVEs are the same, some aren't important. So it really depends on what gets founds as a CVE. The bad part is you risk a flood a CVEs that don't matter (or have already been reported).

> That meant only bad actors had the incentive to look for them

Nah. Lot's of people look for CVEs. It's good resume fodder. In fact, it's already somewhat of a problem that people will look for and report CVEs on things that don't matter just so they can get the "I found and reported CVE xyz" on their resume.

What this will do is expose some already present flaws in the CVE scoring system. Not all "9"s are created equal. Hopefully that leads to something better and not towards apathy.

1. Unit testing is (somewhat) dead, long live simulation. Testing the parts only gets you so far. These tests are far more durable, independent artifacts (read, if you went from JS to rust, how much of your testing would carry over)

2. Testing has to be "stand alone". I want to be able to run it from the command line, I want the output to be wrapper so I can shove the output on a web page, or dump it into an API (for AI)

3. Messages (for failures) matter. These are not just simple what's broken, but must contain enough info for context.

4. Your "failed" tests should include logs. Do you have enough breadcrumbs for production? If not, this is a problem that will bite you later.

5. Any case should be an accumulation of state and behavior - this really matters in simulation.

If you have done all the above right and your tool can return all the data, dumping the output into the cheapest model you can find and having it "Write a prompt with recommendations on a fix (not actual code, just what should be done beyond "fix this") has been illuminating.

Ultimately I realized that how I thought about testing was wrong. Its output should be either dead simple, or have enough information that someone with zero knowledge could ramp up into a fix on their first day in the code base. My testing was never this good because the "cost of doing it that way" was always too high... this is no longer the case.

https://blog.mozilla.org/en/firefox/hardening-firefox-anthro...

I think the Mozilla example is a good one because its a large codebase, lots of people keep asking "how does it do with a large codebase" well there you go.

Does it fix them as fast as it finds them? Bonus if it adds snarky code comments

A theoretical random tweet and a clear demonstration are two different things.