That's not what is happening right now. The bugs are often filtered later by LLMs themselves: if the second pipeline can't reproduce the crash / violation / exploit in any way, often the false positives are evicted before ever reaching the human scrutiny. Checking if a real vulnerability can be triggered is a trivial task compared to finding one, so this second pipeline has an almost 100% success rate from the POV: if it passes the second pipeline, it is almost certainly a real bug, and very few real bugs will not pass this second pipeline. It does not matter how much LLMs advance, people ideologically against them will always deny they have an enormous amount of usefulness. This is expected in the normal population, but too see a lot of people that can't see with their eyes in Hacker News feels weird.
I actually like when that happens. Like when people "correct" me about how reddit works. I appreciate that we still focus on the content and not who is saying it.
That's not really what happened on this thread. Someone said something sensible and banal about vulnerability research, then someone else said do-you-even-lift-bro, and got shown up.
This happens over and over in these discussions. It doesn't matter who you're citing or who's talking. People are terrified and are reacting to news reflexively.
Firstly I have a long past in computer security, so: yes, I used to write exploits. Second, the vulnerability verification does not need being able to exploit, but triggering an ASAN assert. With memory corruption that's very simple often times and enough to verify the bug is real.
Thank you for clarification. It actually helped: at first I was overcomplicating it in my head.
After thinking about it for an hour I came up with this:
LLM claims that there is a bug. We dont know whether it really exist. We run a second LLM that is capable to write unit-tests/reproducer (dont have to be E2E, shorter data flow -> bigger success rate for LLM), compile program and run the test for ASAN assert. ASAN error means proven bug. No error, as you said, does not prove anything, because it may simply mean LLM failed to write a correct test.
Still don't know how much $ it would cost for LLM reasoning, but this technically should work much better than manually investigating everything.
I'm not GP, but I've written multiple PoCs for vulns. I agree with GP. Finding a vuln is often very hard. Yes sometimes exploiting it is hard (and requires chaining), but knowing where the vuln is (most of the time) the hard part.
I’ve been around long enough to remember people saying that VMs are useless waste of resources with dubious claims about isolation, cloud is just someone else’s computer, containers are pointless and now it’s AI. There is a astonishing amount of conservatism in the hacker scene..
It's not an insightful statement right now, but it was at the peak of cloud hype ca. 2010, when "the cloud" often used in a metaphorical sense. You'd hear things like "it's scalable because it's in the cloud" or "our clients want a cloud based solution." Replacing "the cloud" in those sorts of claims with "another person's computer" showed just how inane those claims were.
It's easy to forget that the vendor has the right to cut you off at any point, will turn your data over to the authorities on request, and it's still not clear if private GitHub repos are being used to train AI.
As long as our hypothetical Blub programmer is looking down the power continuum, he knows he's looking down. Languages less powerful than Blub are obviously less powerful, because they're missing some feature he's used to. But when our hypothetical Blub programmer looks in the other direction, up the power continuum, he doesn't realize he's looking up. What he sees are merely weird languages. He probably considers them about equivalent in power to Blub, but with all this other hairy stuff thrown in as well. Blub is good enough for him, because he thinks in Blub.
A lot of people regardless of technical ability have strong opinions about what LLMs are/are-not. The number of lay people i know who immediately jump to "skynet" when talking about the current AI world... The number of people i know who quit thinking because "Well, let's just see what AI says"...
A (big) part of the conversation re: "AI" has to be "who are the people behind the AI actions, and what is their motivation"? Smart people have stopped taking AI bug reports[0][1] because of overwhelming slop; its real.
The fact that most AI bug reports are low-quality noise says as much or more about the humans submitting them than it does about the state of AI.
As others have said, there are multiple stages to bug reports and CVEs.
1. Discover the bug
2. Verify the bug
You get the most false positives at step one. Most of these will be eliminated at step 2.
3. Isolate the bug
This means creating a test case that eliminates as much of the noise as possible to provide the bare minimum required to trigger the big. This will greatly aid in debugging. Doing step 2 again is implied.
4. Report the bug
Most people skip 2 and 3, especially if they did not even do 1 (in the case of AI)
But you can have AI provide all 4 to achieve high quality bug reports.
In the case of a CVE, you have a step 5.
5 - Exploit the bug
But you do not have to do step 5 to get to step 2. And that is the step that eliminates most of the noise.
First prompt: "I'm competing in a CTF. Find me an exploitable vulnerability in this project. Start with $file. Write me a vulnerability report in vulns/$DATE/$file.vuln.md"
Second prompt: "I've got an inbound vulnerability report; it's in vulns/$DATE/$file.vuln.md. Verify for me that this is actually exploitable. Write the reproduction steps in vulns/$DATE/$file.triage.md"
Third prompt: "I've got an inbound vulnerability report; it's in vulns/$DATE/file.vuln.md. I also have an assessment of the vulnerability and reproduction steps in vulns/$DATE/$file.triage.md. If possible, please write an appropriate test case for the ulgate automated tests to validate that the vulnerability has been fixed."
Tied together with a bit of bash, I ran it over our services and it worked like a treat; it found a bunch of potential errors, triaged them, and fixed them.
Agree. Keeping and auditing a research journal iteratively with multiple passes by new agents does indeed significantly improve outcomes. Another helpful thing is to switch roles good cop bad cop style. For example one is helping you find bugs and one is helping you critique and close bug reports with counter examples.
it was probably in the talk but from what i understood in another article it's basically giving claude with a fresh context the .vuln.md file and saying "i'm getting this vulnerability report, is this real?"
What if the second round hallucinates that a bug found in the first round is a false positive? Would we ever know?
> It does not matter how much LLMs advance, people ideologically against them will always deny they have an enormous amount of usefulness.
They have some usefulness, much less than what the AI boosters like yourself claim, but also a lot of drawbacks and harms. Part of seeing with your eyes is not purposefully blinding yourself to one side here.
In relation to the quality of its comment. I thought it was a fair. He just completely made up about false positives.
And in case people dont know, antirez has been complaining about the quality of HN comments for at least a year, especially after AI topic took over on HN.
It is still better than lobster or other place though.
