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.
No, it doesn't at all. "it's scalable because it's in the cloud" may be reductive nonsense or it could be true. It's scalable because it's on someone elses computer and in a matter of minutes it can be on one of their computers with twice the ram and vCPUs. That is a meaningful thing to say when the alternative is CAPEX heavy investment in your own infrastructure. Same with "our clients want a cloud based solution" in contrast with on-prem installs. They don't want your shitty pizza box in their closet, they want someone else to be doing the hosting.
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.
Two of these are basic contractual problems, your company should have a lawyer who can sort them out easily. The third (data being turned over to authorities) is something that the vast majority of companies do not care about in the slightest.
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.
Another potentially usable trick is the following: based on the observation that longer token budget improves model performances, one could generate solutions using a lot of thinking budget, then ask the LLM to turn the trace into a more compact one, and later SFT on that. That said, I have the feeling the result of the paper will likely be hard to apply in practice without affecting other capabilities, and/or not superior to other techniques that provide similar improvement in sampling.
That is, basically, you just rotate and use the 4 bit centroids given that the distribution is known, so you don't need min/max, and notably, once you have that, you can multiply using a lookup table of 256 elements when doing the dot product, since two vectors have the same scale. The important point here is that for this use case it is NOT worth to use the 1 bit residual, since for the dot product, vector-x-quant you have a fast path, but quant-x-quant you don't have it, and anyway the recall difference is small. However, on top of that, remember that new learned embeddings tend to use all the components in a decent way, so you gain some recall for sure, but not as much as in the case of KV cache.
Featuring the ELO score as the main benchmark in chart is very misleading. The big dense Gemma 4 model does not seem to reach Qwen 3.5 27B dense model in most benchmarks. This is obviously what matters. The small 2B / 4B models are interesting and may potentially be better ASR models than specialized ones (not just for performances but since they are going to be easily served via llama.cpp / MLX and front-ends). Also interesting for "fast" OCR, given they are vision models as well. But other than that, the release is a bit disappointing.
Public benchmarks can be trivially faked. Lmarena is a bit harder to fake and is human-evaluated.
I agree it's misleading for them to hyper-focus on one metric, but public benchmarks are far from the only thing that matters. I place more weight on Lmarena scores and private benchmarks.
Lm arena is so easy to game that it's ceased to be a relevant metric over a year ago. People are not usable validators beyond "yeah that looks good to me", nobody checks if the facts are correct or not.
I agree; LMArena died for me with the Llama 4 debacle. And not only the gamed scores, but seeing with shock and horror the answers people found good. It does test something though: the general "vibe" and how human/friendly and knowledgeable it _seems_ to be.
It's easy to game and human evaluation data has its trade-offs, but it's way easier to fake public benchmark results. I wish we had a source of high quality private benchmark results across a vast number of models like Lmarena. Having high quality human evaluation data would be a plus too.
Well there was this one [0] which is a black box but hasn't really been kept up to date with newer releases. Arguably we'd need lots of these since each one could be biased towards some use case or sell its test set to someone with more VC money than sense.
I know Arc AGI 2 has a private test set and they have a good amount of results[0] but it's not a conventional benchmark.
Looking around, SWE Rebench seems to have decent protection against training data leaks[1]. Kagi has one that is fully private[2]. One on HuggingFace that claims to be fully private[3]. SimpleBench[4]. HLE has a private test set apparently[5]. LiveBench[6]. Scale has some private benchmarks but not a lot of models tested[7]. vals.ai[8]. FrontierMath[9]. Terminal Bench Pro[10]. AA-Omniscience[11].
So I guess we do have some decent private benchmarks out there.
But is arc-agi really that useful though? Nowadays it seems to me that it's just another benchmark that needs to be specifically trained for. Maybe the Chinese models just didn't focus on it as much.
Is it though? Do we still have the expectation that LLMs will eventually be able to solve problems they haven't seen before? Or do we just want the most accurate auto complete at the cheapest price at this point?
It indicates that there's a good chance that they have trained on the test set, making the eval scores useless. Even if you have given up on the dream of generalization entirely, you can't meaningfully compare models which have trained on test to those which have not.
I find the benchmarks to be suggestive but not necessarily representative of reality. It's really best if you have your own use case and can benchmark the models yourself. I've found the results to be surprising and not what these public benchmarks would have you believe.
I can't find what ELO score specifically the benchmark chart is referring to, it's just labeled "Elo Score". It's not Codeforces ELO as that Gemma 4 31B has 2150 for that which would be off the given chart.
It's referring to the Lmsys Leaderboard/Lmarena/Arena.ai[0]. It's very well-known in the LLM community for being one of the few sources of human evaluation data.
Love Picol, and love this! When I first revisited Tcl, I was a bit miffed about needing [expr] but now really appreciate both it and the normal Tcl syntax.
I have a Tcl Improvement Proposal (TIP 676) currently being voted on which introduces an alternative compact form of calculation. The implementation uses a Pratt parser: https://core.tcl-lang.org/tcl/file?ci=cgm-equals-command&nam... which directly generates bytecode rather than creating a parse tree.
> If a harness is needed, it can make its own. If tools are needed, it can chose to bring out these tools.
If I understand correctly the model can carry only very limited memory among tests, so it looks like it's not really possible for the model to self specialize itself under this assumptions.
Basically this is true for most startups in the world BUT Cursor, so here you are kinda inverting the logic of the matter. Cursor is at a size that, if they wanted to use K2.5, they could clearly state that it was K2.5 or get a license to avoid saying it.
IF we assume that the modified MIT clause is enforceable. And if we assume Cursor Inc. is running the modification. It could very well be the case that Cursor Research LTD is doing the modifications and re-licensing it to Cursor Inc. That would make any clause in the modified MIT moot.
In programming, the only rule to follow is that there are no rules: only taste and design efforts. There are too many different conditions and tradeoffs: sometimes what is going to be the bottleneck is actually very clear and one could decide to design with that already in mind, for instance.
As yourself: what kind of tool I would love to have, to accomplish the work I'm asking the LLM agent to do? Often times, what is practical for humans to use, it is for LLMs. And the reply is almost never the kind of things MCP exports.
You interact with REST APIs (analogue of MCP tools) and web pages (analogue of MCP resources) every day.
I'd recommend that you take a peek at MCP prompts and resources spec and understand the purpose that these two serve and how they plug into agent harnesses.
So you love interacting with web sites sending requests with curl?
And if you need the price of an AWS service, you love to guess the service name (querying some other endpoint), then ask some tool the price for it, get JSON back, and so forth? Or you are better served by a small .md file you pre-compiled with the services you use the most, and read from it a couple of lines?
> I'd recommend that you take a peek at MCP prompts and resources spec
Don't assume that if somebody does not like something they don't know what it is. MCP makes happy developers that need the illusion of "hooking" things into the agent, but it does not make LLMs happy.
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