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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

#21

Post by simianwords »

jakozaur wrote: post #15
simianwords wrote: post #11

I'm not an expert but about false positives: why not make the agent attempt to use the backdoor and verify that it is actually a backdoor? Maybe give it access to tools and so on.

So many models refuse to do that due to alignment and safety concerns. So cross-model comparison doesn't make sense. We do, however, require proof (such as providing a location in binary) that is hard to game. So the model not only has to say there is a backdoor, but also point out the location. Your approach, however, makes a lot of sense if you are ready to have your own custom or fine-tuned model.

Surprising that they still allow to catch the back doors but not use them.

A bad actor already has most of the work done.

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

#22

Post by magicmicah85 »

GPT is impressive with a consistent 0% false positive rate across models, yet its ability to detect is as high as 18%. Meanwhile Claude Opus 4.6 is able to detect up to 46% of backdoors, but has a 22% false positive rate.

It would be interesting to have an experiment where these models are able to test exploiting but their alignment may not allow that to happen. Perhaps combining models together can lead to that kind of testing. The better models will identify, write up "how to verify" tests and the "misaligned" models will actually carry out the testing and report back to the better models.

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

#23

Post by bitexploder »

akiselev wrote: post #7

Shameless plug: https://github.com/akiselev/ghidra-cli I’ve been using Ghidra to reverse engineer Altium’s file format (at least the Delphi parts) and it’s insane how effective it is. Models are not quite good enough to write an entire parser from scratch but before LLMs I would have never even attempted the reverse engineering. I definitely would not depend on it for security audits but the latest models are more th…

I can tell you how I am seeing agents be used with reasonable results. I will keep this high level. I don't rely on the agents solely. You build agents that augment your capabilities.

They can make diagrams for you, give you an attack surface mapping, and dig for you while you do more manual work. As you work on an audit you will often find things of interest in a binary or code base that you want to investigate further. LLMs can often blast through a code base or binary finding similar things.

I like to think of it like a swiss army knife of agentic tools to deploy as you work through a problem. They won't balk at some insanely boring task and that can give you a real speed up. The trick is if you fall into the trap of trying to get too much out of an LLM you end up pouring time into your LLM setup and not getting good results, I think that is the LLM productivity trap. But if you have a reasonable subset of "skills" / "agents" you can deploy for various auditing tasks it can absolutely speed you up some.

Also, when you have scale problems, just throw an LLM at it. Even low quality results are a good sniff test. Some of the time I just throw an LLM at a code review thing for a codebase I came across and let it work. I also love asking it to make me architecture diagrams.

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

#24

Post by bitexploder »

akiselev wrote: post #20
lima wrote: post #12

Earlier quoted context omitted.

How does this approach compare to the various Ghidra MCP servers?

There’s not much difference, really. I stupidly didn’t bother looking at prior art when I started reverse engineering and the ghidra-cli was born (along with several others like ilspy-cli and debugger-cli) That said, it should be easier to use as a human to follow along with the agent and Claude Code seems to have an easier time with discovery rather than stuffing all the tool definitions into the context.

That is pretty funny. But you probably learned something in implementing it! This is such a new field, I think small projects like this are really worthwhile :)

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

#26

Post by 7777332215 »

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.

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

#27

Post by Tiberium »

jakozaur wrote: post #18
Tiberium wrote: post #14

I highly doubt some of those results, GPT 5.2/+codex is incredible for cyber security and CTFs, and 5.3 Codex (not on API yet) even moreso. There is absolutely no way it's below Deepseek or Haiku. Seems like a harness issue, or they tested those models at none/low reasoning?

As I do eval and training data sets for living, in niche skills, you can find plenty of surprises. The code is open-source; you can run it yourself using Harbor Framework: git clone git@github.com:QuesmaOrg/BinaryAudit.git export OPENROUTER_API_KEY=... harbor run --path tasks --task-name lighttpd-* --agent terminus-2 --model openrouter/anthropic/claude-opus-4.6 --model openrouter/google/gemini-3-pro-preview --model o…

Are the existing trajectories from your runs published anywhere? Or is the only way is for me to run them again?

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

#30

Post by selridge »

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.

That’s hard. Sometimes you will do that and find it prompts the model into “strategy talk” where it deploys the words and frame you use in your .md files but doesn’t actually do the strategy.

Even where it works, it is quite hard to specify human strategic thinking in a way that an AI will follow.

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