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The coming industrialisation of exploit generation with LLMs

sean.heelan.io

11–20 of 174 posts

Re: The coming industrialisation of exploit generation with LLMs

#11
post #6

I genuinely dont know who to believe. The people who claim LLMs are writing excellent exploits. Or the people who claim that LLMs are sending useless bug reports. I dont feel like both can really be true.

Why can't they both be true? The quality of output you see from any LLM system is filtered through the human who acts on those results. A dumbass pasting LLM generated "reports" into an issue system doesn't disprove the efforts of a subject-matter expert who knows how to get good results from LLMs and has the necessary taste to only share the credible issues it helps them find.

They can't both be true if we're talking about the premise of the article, which is the subject of the headline and expounded upon prominently in the body:

  The Industrialisation of Intrusion

  By ‘industrialisation’ I mean that the ability of an organisation to complete a task will be limited by the number of tokens they can throw at that task. In order for a task to be ‘industrialised’ in this way it needs two things:

  An LLM-based agent must be able to search the solution space. It must have an environment in which to operate, appropriate tools, and not require human assistance. The ability to do true ‘search’, and cover more of the solution space as more tokens are spent also requires some baseline capability from the model to process information, react to it, and make sensible decisions that move the search forward. It looks like Opus 4.5 and GPT-5.2 possess this in my experiments. It will be interesting to see how they do against a much larger space, like v8 or Firefox.
  The agent must have some way to verify its solution. The verifier needs to be accurate, fast and again not involve a human.
"The results are contigent upon the human" and "this does the thing without a human involved" are incompatible. Given what we've seen from incompetent humans using the tools to spam bug bounty programs with absolute garbage, it seems the premise of the article is clearly factually incorrect. They cite their own experiment as evidence for not needing human expertise, but it is likely that their expertise was in fact involved in designing the experiment[1]. They also cite OpenAI's own claims as their other piece of evidence for this theory, which is worth about as much as a scrap of toilet paper given the extremely strong economic incentives OpenAI has to exaggerate the capabilities of their software.

[1] If their experiment even demonstrates what it purports to demonstrate. For anyone to give this article any credence, the exploit really needs to be independently verified that it is what they say it is and that it was achieved the way they say it was achieved.

Re: The coming industrialisation of exploit generation with LLMs

#12

I genuinely dont know who to believe. The people who claim LLMs are writing excellent exploits. Or the people who claim that LLMs are sending useless bug reports. I dont feel like both can really be true.

With the exploits, you can try them and they either work or they don't. An attacker is not especially interested in analysing why the successful ones work.

With the CVE reports some poor maintainer has to go through and triage them, which is far more work, and very asymmetrical because the reporters can generate their spam reports in volume while each one requires detailed analysis.

Re: The coming industrialisation of exploit generation with LLMs

#13
post #3

I think the author makes some interesting points, but I'm not that worried about this. These tools feel symmetric for defenders to use as well. There's an easy to see path that involves running "LLM Red Teams" in CI before merging code or major releases. The fact that it's a somewhat time expensive (I'm ignoring cost here on purpose) test makes it feel similar to fuzzing for where it would fit in a pipeline. New tool…

> I think the author makes some interesting points, but I'm not that worried about this.

Given the large number of unmaintained or non-recent software out there, I think being worried is the right approach.

The only guaranteed winner is the LLM companies, who get to sell tokens to both sides.

Re: The coming industrialisation of exploit generation with LLMs

#14
post #12

I genuinely dont know who to believe. The people who claim LLMs are writing excellent exploits. Or the people who claim that LLMs are sending useless bug reports. I dont feel like both can really be true.

With the exploits, you can try them and they either work or they don't. An attacker is not especially interested in analysing why the successful ones work. With the CVE reports some poor maintainer has to go through and triage them, which is far more work, and very asymmetrical because the reporters can generate their spam reports in volume while each one requires detailed analysis.

There's been several notable posts where maintainers found there was no bug at all, or the example code did not even call code from their project and had just found running a python script can do things on your computer. Entirely AI generated Issue reports and examples wasting maintainer time.

Re: The coming industrialisation of exploit generation with LLMs

#15
post #6

Earlier quoted context omitted.

Why can't they both be true? The quality of output you see from any LLM system is filtered through the human who acts on those results. A dumbass pasting LLM generated "reports" into an issue system doesn't disprove the efforts of a subject-matter expert who knows how to get good results from LLMs and has the necessary taste to only share the credible issues it helps them find.

They can't both be true if we're talking about the premise of the article, which is the subject of the headline and expounded upon prominently in the body: The Industrialisation of Intrusion By ‘industrialisation’ I mean that the ability of an organisation to complete a task will be limited by the number of tokens they can throw at that task. In order for a task to be ‘industrialised’ in this way it needs two things:…

After setting the environment and the verifier you can spawn as many agents as you want until the conditions are met, this is only possible because they run without human assistance, that's the "industrialisation".

Re: The coming industrialisation of exploit generation with LLMs

#16
post #3

I think the author makes some interesting points, but I'm not that worried about this. These tools feel symmetric for defenders to use as well. There's an easy to see path that involves running "LLM Red Teams" in CI before merging code or major releases. The fact that it's a somewhat time expensive (I'm ignoring cost here on purpose) test makes it feel similar to fuzzing for where it would fit in a pipeline. New tool…

This + the fact software and hardware has been getting structurally more secure over time. New changes like language safety features, Memory Integrity Enforcement, etc will significantly raise the bar on the difficulty to find exploits.

Re: The coming industrialisation of exploit generation with LLMs

#17
post #6

I genuinely dont know who to believe. The people who claim LLMs are writing excellent exploits. Or the people who claim that LLMs are sending useless bug reports. I dont feel like both can really be true.

Why can't they both be true? The quality of output you see from any LLM system is filtered through the human who acts on those results. A dumbass pasting LLM generated "reports" into an issue system doesn't disprove the efforts of a subject-matter expert who knows how to get good results from LLMs and has the necessary taste to only share the credible issues it helps them find.

Theres no filtering mentioned in the OP article. It claims GPT only created working useful exploits. If it can do that, it could also submit those exploits as perfectly as bug reports?

Re: The coming industrialisation of exploit generation with LLMs

#19
post #12

I genuinely dont know who to believe. The people who claim LLMs are writing excellent exploits. Or the people who claim that LLMs are sending useless bug reports. I dont feel like both can really be true.

With the exploits, you can try them and they either work or they don't. An attacker is not especially interested in analysing why the successful ones work. With the CVE reports some poor maintainer has to go through and triage them, which is far more work, and very asymmetrical because the reporters can generate their spam reports in volume while each one requires detailed analysis.

All the attackers I’ve known are extremely, pathologically interested in understanding why their exploits work.

Re: The coming industrialisation of exploit generation with LLMs

#20
post #6

Earlier quoted context omitted.

Why can't they both be true? The quality of output you see from any LLM system is filtered through the human who acts on those results. A dumbass pasting LLM generated "reports" into an issue system doesn't disprove the efforts of a subject-matter expert who knows how to get good results from LLMs and has the necessary taste to only share the credible issues it helps them find.

Theres no filtering mentioned in the OP article. It claims GPT only created working useful exploits. If it can do that, it could also submit those exploits as perfectly as bug reports?

The OP is the filtering expert.
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