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We hid backdoors in ~40MB binaries and asked AI + Ghidra to find them

quesma.com

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Re: We hid backdoors in ~40MB binaries and asked AI + Ghidra to find them

#71

Post by NitpickLawyer »

selridge wrote: post #48
Arech wrote: post #37

Earlier quoted context omitted.

That's what I thought of too. Given their task formulation (they basically said - "check these binaries with these tools at your disposal" - and that's it!) their results are already super impressive. With a proper guidance and professional oversight it's a tremendous force multiplier.

We are in this super weird space where the comparable tasks are one-shot, e.g. "make me a to-do app" or "check these binaries", but any real work is multi-turn and dynamically structured. But when we're trying to share results, "a talented engineer sat with the thread and wrote tests/docs/harnesses to guide the model" is less impressive than "we asked it and it figured it out," even though the latter is how real work…

Something I found useful is to "just figure it out" the first part (usually discovery, or library testing, new cli testing, repo understanding, etc.) and then distill it into "learnings" that I can place in agents.md or relevant skills. So you get the speed of "just prompt it" and the repeatability of having it already worked in this area. You also get more insight into what tasks work today, and at what effort level.

Sometimes it feels like it's not dissimilar to spending 4 hours to automate a 10 minute task that I thought I'll need forever but ended up just using it once in the past 5 months. But sometimes I unlock something that saves a huge amount of time, and can be reused in many steps of other projects.

Re: We hid backdoors in ~40MB binaries and asked AI + Ghidra to find them

#73

Post by anamexis »

stared wrote: post #69
botusaurus wrote: post #65

Earlier quoted context omitted.

have you tried stuffing a whole set of tutorials on how to use ghidra in the context, especially for the 1 mil token context like gemini?

No. To give it a fair test, we didn't tinker with model-specific context-engineering. Adding skills, examples, etc is very likely to improve performance. So is any interactive feedback. Our example instruction is here: https://github.com/QuesmaOrg/BinaryAudit/blob/main/tasks/lig...

Why, though? That would make sense if you were just trying to do a comparative analysis of different agent's ability to use specific tools without context, but if your thesis is:

> However, [the approach of using AI agents for malware detection] is not ready for production.

Then the methodology does not support that. It's "the approach of using AI agents for malware detection with next to zero documentation or guidance is not ready for production."

Re: We hid backdoors in ~40MB binaries and asked AI + Ghidra to find them

#74

Post by [deleted] »

folex wrote: post #6

> The executables in our benchmark often have hundreds or thousands of functions — while the backdoors are tiny, often just a dozen lines buried deep within. Finding them requires strategic thinking: identifying critical paths like network parsers or user input handlers and ignoring the noise. Perhaps it would make sense to provide LLMs with some strategy guides written in .md files.

[deleted]

Re: We hid backdoors in ~40MB binaries and asked AI + Ghidra to find them

#75

Post by comex »

achille wrote: post #59
7777332215 wrote: post #26

I know they said they didn't obfuscate anything, but if you hide imports/symbols and obfuscate strings, which is the bare minimum for any competent attacker, the success rate will immediately drop to zero. This is detecting the pattern of an anomaly in language associated with malicious activity, which is not impressive for an LLM.

in the article they explicitly said they stripped symbols. If you look at the actual backdoors many are already minimal and quite obfuscated, see: - https://github.com/QuesmaOrg/BinaryAudit/blob/main/tasks/dns... - https://github.com/QuesmaOrg/BinaryAudit/blob/main/tasks/dro...

The first one was probably found due to the reference to the string /bin/sh, which is a pretty obvious tell in this context.

The second one is more impressive. I'd like to see the reasoning trace.

Re: We hid backdoors in ~40MB binaries and asked AI + Ghidra to find them

#76

Post by stared »

anamexis wrote: post #73
stared wrote: post #69

Earlier quoted context omitted.

No. To give it a fair test, we didn't tinker with model-specific context-engineering. Adding skills, examples, etc is very likely to improve performance. So is any interactive feedback. Our example instruction is here: https://github.com/QuesmaOrg/BinaryAudit/blob/main/tasks/lig...

Why, though? That would make sense if you were just trying to do a comparative analysis of different agent's ability to use specific tools without context, but if your thesis is: > However, [the approach of using AI agents for malware detection] is not ready for production. Then the methodology does not support that. It's "the approach of using AI agents for malware detection with next to zero documentation or guidan…

You can solve any problem with AI if you give enough hints.

The question we asked is if they can solve a problem autonomously, with instructions that would be clear for a reverse engineering specialist.

That say, I found these useful for many binary tasks - just not (yet) the end-to-end ones.

Re: We hid backdoors in ~40MB binaries and asked AI + Ghidra to find them

#77

Post by achierius »

not_a9 wrote: post #43
dgellow wrote: post #16

Random thoughts, only vaguely related: what’s the impact of AI on CTFs? I would assume that kills part of the fun of such events?

Things are pretty brutal and some categories are more affected than others. A/D seems to be somewhat less affected.

Brutal as in, heavy AI usage? What sort of categories are more affected?
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