111–120 of 372 posts
Finding a needle in a haystack is easy if someone hands you the small handful of hay containing the needle up front, and raises their eyebrows at you saying “there might be a needle in this clump of hay”.
Earlier quoted context omitted.
This is addressed elsewhere in the comments, but it appears this is actually a direct comparison to how Anthropic got their Mythos headline results. https://news.ycombinator.com/item?id=47732322
How is that a direct comparison? The link you gave has a quote that says it’s not: > Scoped context: Our tests gave models the vulnerable function directly, often with contextual hints (e.g., "consider wraparound behavior"). A real autonomous discovery pipeline starts from a full codebase with no hints They pointed the models at the known vulnerable functions and gave them a hint. The hint part is what really breaks…
loop through each repo: loop through each file: opencode command /find_wraparoundvulnerability next file next repo
I can run this on my local LLM and sure, I gotta wait some time for it to complete, but I see zero distinguishing facts here.
If you cut out the vulnerable code from Heartbleed and just put it in front of a C programmer, they will immediately flag it. It's obvious. But it took Neel Mehta to discover it. What's difficult about finding vulnerabilities isn't properly identifying whether code is mishandling buffers or holding references after freeing something; it's spotting that in the context of a large, complex program, and working out how a…
It's also that humans are very bad at repetitive detailed tasks. Sitting down with a code base and looking at each function for integer overflow comparison bugs gets boring really fast. It's a rare person who can do that for as long as it takes to find a bug that they don't already have some clues about. It's the flaw in the "given enough eyeballs, all bugs are shallow" argument. Because eyeballs grow tired of lookin…
Would it be cheaper than Claude Mythos doing it? No idea. Maybe, maybe not.
But it’s weird how we’re willing to throw away money to a megacorp to do it with “automation” for potentially just as much if not more as it would cost to just have big bounty program or hiring someone for nearly the same cost and doing it “normally”.
It would really have to be substantially less cost for me to even consider doing it with a bot.
The Anthropic writeup addresses this explicitly: > This was the most critical vulnerability we discovered in OpenBSD with Mythos Preview after a thousand runs through our scaffold. Across a thousand runs through our scaffold, the total cost was under $20,000 and found several dozen more findings. While the specific run that found the bug above cost under $50, that number only makes sense with full hindsight. Like any…
Wasn't the scaffolding for the Mythos run basically a line of bash that loops through every file of the codebase and prompts the model to find vulnerabilities in it? That sounds pretty close to "any gold there?" to me, only automated. Have Anthropic actually said anything about the amount of false positives Mythos turned up? FWIW, I saw some talk on Xitter (so grain of salt) about people replicating their result with…
If you isolate the codebase just the specific known vulnerable code up front it isn’t surprising the vulnerabilities are easy to discover. Same is true for humans.
Better models can also autonomously do the work of writing proof of concepts and testing, to autonomously reject false positives.
Earlier quoted context omitted.
This is from the first of the caveats that they list: > Scoped context: Our tests gave models the vulnerable function directly, often with contextual hints (e.g., "consider wraparound behavior"). A real autonomous discovery pipeline starts from a full codebase with no hints. The models' performance here is an upper bound on what they'd achieve in a fully autonomous scan. That said, a well-designed scaffold naturally…
I get what you're saying, but I think this is still missing something pretty critical. The smaller models can recognize the bug when they're looking right at it, that seems to be verified. And with AISLE's approach you can iteratively feed the models one segment at a time cheaply. But if a bug spans multiple segments, the small model doesn't have the breadth of context to understand those segments in composite. The a…
Is Mythos some how more powerful than just a recursive foreloop aka, "agentic" review. You can run `open code run --command` with a tailored command for whatever vulnerabilities you're looking for.
Companies like Aisle.com (the blog) and other VAPT companies charge huge amounts to detect vulnerabilities.
If Cloud Mythos become a simple github hook their value will get reduced.
That is a disruption.
A while ago, the autoresearch[1] harness went viral, yet it's but a highly simplified version of AlphaEvolve[2][3][4].
In the cybersecury context, you can envision a clever harness that probes every function in a codebase for vulnerabilities, then bubbles the candidates up to their callsites (and probes whether the vulnerability can be triggered from there) and then all the way to an interface (such as a syscall) where a potential exploit can be manifested. And those would be the low hanging fruit, other vulnerabilities may require the interplay of multiple functions. Or race conditions.
[1] https://github.com/karpathy/autoresearch>
[2] https://deepmind.google/blog/alphaevolve-a-gemini-powered-co...>
[3] https://arxiv.org/abs/2506.13131>
[4] https://github.com/algorithmicsuperintelligence/openevolve>
> We took the specific vulnerabilities Anthropic showcases in their announcement, isolated the relevant code, and ran them through small, cheap, open-weights models. Those models recovered much of the same analysis. Eight out of eight models detected Mythos's flagship FreeBSD exploit, including one with only 3.6 billion active parameters costing $0.11 per million tokens. Impressive, and very valuable work, but isolat…
This is from the first of the caveats that they list: > Scoped context: Our tests gave models the vulnerable function directly, often with contextual hints (e.g., "consider wraparound behavior"). A real autonomous discovery pipeline starts from a full codebase with no hints. The models' performance here is an upper bound on what they'd achieve in a fully autonomous scan. That said, a well-designed scaffold naturally…
They measured false negatives on a handful of cases, but that is not enough to hint at the system you suggest. And based on my experiences with $$$ focused eval products that you can buy right now, e.g. greptile, the false positive rate will be so high that it won't be useful to do full codebase scans this way.
Intuitively every existing model has already been trained on all code, all vulnerabilities reported, all security papers. So they all have the capability. Small models fall short because they may not be able to find a vulnerability that spans across a large function chain but for the most part they should suffice too. Of course I say this without any knowledge of what mythos is doing or how it’s different. I am sure…
Using small models as a classifier "there might be a vulnerability here" is probably reasonable, if you have a model capable of proving it. There are many companies attempting this without the verification step, resulting in AI vulnerability checker being banned left and right, from the nonsense noise.