8 ms·
We hid backdoors in ~40MB binaries and asked AI + Ghidra to find them
- BruceEel 7mo agoVery, very cool. Besides the top-performing models, it's interesting (if I'm reading this correctly) that gpt-5.2 did ~2x better than gpt-5.2-codex.. why?
- NitpickLawyer 7mo ago> gpt-5.2 did ~2x better than gpt-5.2-codex.. why? Optimising a model for a certain task, via fine-tuning (aka post-training), can lead to loss of performance on other tasks. People want codex to "generate code" and "drive agents" and so on. So oAI fine-tuned for that.
- jakozaur 7mo agoSee direct benchmark link: https://quesma.com/benchmarks/binaryaudit/ https://quesma.com/benchmarks/binaryaudit/ Open-source GitHub: https://github.com/QuesmaOrg/BinaryAudit https://github.com/QuesmaOrg/BinaryAudit
- Bender 7mo agoAlong this line can AI's find backdoors spread across multiple pieces of code and/or services? i.e. by themselves they are not back-doors, advanced penetration testers would not suspect anything is afoot but when used together they provide access. e.g. an intentional weakness in systemd + udev + binfmt magic when used together == authentication and mandatory access control bypass. Each weakness reviewed individually just looks like benign sub-optimal code.
- cluckindan 7mo agoStart with trying to find the xz vulnerability and other software possibly tying into that. Is there code that does something completely different than its comments claim?
- Bender 7mo agoAnother way to phrase what I am asking is ... Does AI understand the context of code deep enough to know everything a piece of code can do, everything a service can do vs. what it was intended to do. If it can understand code that far then it could understand all the potential paths data could flow and thus all the potential vulnerabilities that several piece of code together could achieve when used in concert with one another. Advanced multi-tier chess so to speak. Or put another way, each of these three through three hundred applications or services by themselves may be intended to perform x,y,z functions but when put together by happy coincidence they can perform these fifty-million other unintended functions including but not limited to bypassing authentication, bypassing mandatory access controls, avoiding logging and auditing, etc... oh and it can automate washing your dishes, too.
- DANmode 7mo agoSome models can, depending on the length of the piece of code, is probably the most honest answer right now.
- Bender 7mo agoFair enough. I suspect when they reach such a point that length no longer matters then a plethora of old and currently used state sponsored complex malware will be realized. Beyond that I think the next step would be to attain attribution to both individuals and perhaps whom they were really employed by. Bonus if the model can rewrite sanitize each piece of code to remove the malicious capabilities without breaking the officially intended functions.
- shablulman 7mo ago[flagged]
- bangaladore 7mo agoWhat's the point of posting what is clearly an AI generated comment.
- folex 7mo ago> 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.
- selridge 7mo agoThat’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.
- Arech 7mo agoThat'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.
- selridge 7mo agoWe 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 will happen. It creates this perverse scenario (which is no one's fault!) where we talk about one-shot performance but one-shot performance is useful in exactly 0 interesting cases.
- NitpickLawyer 7mo agoSomething 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.
- akiselev 7mo agoShameless plug: https://github.com/akiselev/ghidra-cli 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 than good enough to reverse engineer file formats.
- huflungdung 7mo ago[dead]
- jakozaur 7mo agoOh, nice find... We end up using PyGhidra, but the models waste some cycles because of bad ergonomics. Perhaps your cli would be easier. Still, Ghidra's most painful limitation was extremely slow time with Go Lang. We had to exclude that example from the benchmark.
- lima 7mo agoHow does this approach compare to the various Ghidra MCP servers?
- akiselev 7mo agoThere’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.
- bitexploder 7mo agoThat 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 :)
- simianwords 7mo agoI'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.
- jakozaur 7mo agoSo 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.
- simianwords 7mo agoSurprising that they still allow to catch the back doors but not use them. A bad actor already has most of the work done.
- garblegarble 7mo agoSounds like the pitch writes itself, "you'd better spend a lot of token money with us before the bad guys do it to you..."
- nisarg2 7mo agoI wonder how model performance would change if the tooling included the ability to interact with the binary and validate the backdoor. Particularly for models that had a high rate of false positives, would they test their hypothesis?
- Tiberium 7mo agoI 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?
- jakozaur 7mo agoAs 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 openrouter/openai/gpt-5.2 --n-attempts 3 Please open PR if you find something interesting, though our domain experts spend fair amount of time looking at trajectories.
- Tiberium 7mo agoAre the existing trajectories from your runs published anywhere? Or is the only way is for me to run them again?
- jakozaur 7mo agoI can provide trajectories. Though probably we are not going to publish them this time. This would need some extra safeguards. Email me. The address is in profile.
- Tiberium 7mo agoJust for fun, I ran dnsmasq-backdoor-detect-printf (which has a 0% pass rate in your leaderboard with GPT models) with --agent codex instead of terminus-2 with gpt-5.2-codex and it identified the backdoor successfully on the first try. I honestly think it's a harness issue, could you re-run the benchmarks with Codex for gpt-5.2-codex and gpt-5.2?
- stared 7mo ago
- dgellow 7mo agoRandom thoughts, only vaguely related: what’s the impact of AI on CTFs? I would assume that kills part of the fun of such events?
- not_a9 7mo agoThings are pretty brutal and some categories are more affected than others. A/D seems to be somewhat less affected.
- achierius 7mo agoBrutal as in, heavy AI usage? What sort of categories are more affected?
- not_a9 7mo agoFrom what someone told me rev/crypto/misc are the most broken, with pwn/web being more iffy and depending on challenge specifics. I can't speak on AI usage very clearly (fun fact: just putting the challenge into ChatGPT's web UI sometimes works!), but I think the most egregious is orchestration platforms for agents (with MCP/whatever else) to autonomously solve challenges.
- magicmicah85 7mo agoGPT 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.
- sdenton4 7mo agoIt would be really cool if someone developed some standard language and methodology for measuring the success of binary classificaiton tasks... Oh, wait, we have had that for a hundred years - somehow it's just entirely forgotten when generative models are involved.
- stared 7mo agoRerun it for "high" and "xhigh" effort settings, and GPT-5.2-Codex still get 0% false positive, while getting at the level of other best models for localization of backdoors: https://quesma.com/benchmarks/binaryaudit/ https://quesma.com/benchmarks/binaryaudit/
- ducktastic 7mo agoIt would be interesting to have some tests run against deliberate code obfuscation next
- 7777332215 7mo agoI 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.
- akiselev 7mo agoWhen I was developing my ghidra-cli tool for LLMs to use, I was using crackmes as tests and it had no problem getting through obfuscation as long as it was prompted about it. In practice when reverse engineering real software it can sometimes spin in circles for a while until it finally notices that it's dealing with obfuscated code, but as long as you update your CLAUDE.md/whatever with its findings, it generally moves smoothly from then on.
- halflife 7mo agoIsn’t LLM supposed to be better at analyzing obfuscated than heuristics? Because of its ability to pattern match it can deduce what obfuscated code does?
- bethekidyouwant 7mo agoHow much binary code is in the training set? (None?)
- stared 7mo ago
- stevemk14ebr 7mo agoThese results are terrible, false positives and false negatives. Useless
- Roark66 7mo agoAnd this one demonstration why these "1000 CTOs claim no effectiveness improvement after introducing AI in their companies" are 100% BS. They may have not noticed an improvement, but it doesn't mean there isn't any.
- HeWhoLurksLate 7mo agoit also generally takes a heck of a noisy bang for internal developments to make it to the c-suite
- snovv_crash 7mo agoEven without AI, many (most?) orgs are held back by internal processes and politics, not development speed.
- localuser13 7mo agoIs it? Gemini 3-pro-preview and 3-flash-preview, respectively top2 and top3, had 44% and 37% true positive and whooping 65% and 86% false positives. This is worse than a coin toss. Anything more than 0% (3% to be generous) is useless in the real world. This leaves only grok and GPT, with 18%, 9% and 2% success rate. In fact, this is what authors said themselves: "However, this approach is not ready for production. Even the best model, Claude Opus 4.6, found relatively obvious backdoors in small/mid-size binaries only 49% of the time. Worse yet, most models had a high false positive rate — flagging clean binaries." So I'm not sure if we're even discussing the same article. I also don't see a comparison with any other methodology. What is the success rate of ./decompile binary.exe | grep "(exec|system)/bin/sh"? What is the success rate of state-of-the-art alternative approaches?
- raphaelmolly8 7mo ago[dead]
- wangzhongwang 7mo ago[dead]
- snowhale 7mo ago[dead]
- selridge 7mo ago>While end-to-end malware detection is not reliable yet, AI can make it easier for developers to perform initial security audits. A developer without reverse engineering experience can now get a first-pass analysis of a suspicious binary. [...] The whole field of working with binaries becomes accessible to a much wider range of software engineers. It opens opportunities not only in security, but also in performing low-level optimization, debugging and reverse engineering hardware, and porting code between architectures. THIS is the takeaway. These tools are allowing *adjacency* to become a powerful guiding indicator. You don't need to be a reverser, you can just understand how your software works and drive the robot to be a fallible hypothesis generator in regions where you can validate only some of the findings.
- EB66 7mo agoThe fact that Gemini returns the highest rate of fake positives aligns with my experience using the Gemini models. I use ChatGPT, Claude and Gemini regularly and Gemini is clearly the most sycophantic of the three. If I ask those three models to evaluate something or estimate odds of success, Gemini always comes back with the rosiest outlook. I had been searching for a good benchmark that provided some empirical evidence of this sycophancy, but I hadn't found much. Measuring false positives when you ask the model to complete a detection related task may be a good way of doing that.
- monegator 7mo agothe interactive code viewer is neat!
- shevy-java 7mo agoSo the best one found about 50%. I think that is not bad, probably better than most humans. But what about the remaining 50%? Why were some found and others not? > Claude Opus 4.6 found it… and persuaded itself there is nothing to worry about > Even the best model in our benchmark got fooled by this task. That is quite strange. Because it seems almost as if a human is required to make the AI tools understand this.
- openasocket 7mo agoUmmm, is it a good idea to use AI for malware analysis? I know this is just a proof of concept, but if you have actual malware, it doesn’t seem safe to hand that to AI. Given the lengths of anti-debugging that goes in existing malware, making something to prompt inject, or trick AI to execute something, seems easier.
- hereme888 7mo ago> Claude Opus 4.6 found it… and persuaded itself there is nothing to worry about. Lol. > Gemini 3 Pro supposedly “discovered” a backdoor. Yup, sounds typical for Gemini...it tends to lie. Very good article. Sounds super useful to apply its findings and improve LLMs. On a similar note.... reverse engineering is now accessible to the public. Tons of old software is now be easy to RE. Are software companies having issues with this?
- fsniper 7mo agoSo these beat me to identifying backdoors too. This is going places in an alarming pace.
- johnbarron 7mo ago[dead]
- greazy 7mo agoVery nitpicky but because I spend a lot of time plotting data: don't arbitrarily color the bar plots without at least mentioning cut offs. Why 19% is orange and 20% is green is a mystery.
- godelski 7mo agoIt's a pretty common threshold, like 10% is. Be it the 80/20 "Pareto" rule, it's the value of one finger on one hand, or if you really want you stretch the p-value of 0.05 is 1 in 20 odds but that's definitely a stretch though arbitrary anyways. But 20 is a very human number and very common. It's just a division of 5 rather than 4 (I'm assuming you wouldn't have questioned a cutoff at 25%)
- manbash 7mo agoRE agents are really interesting! Too bad the author didn't really share the agents they were using so we can't really test this ourselves.
- deleted 7mo ago[deleted]
- umairnadeem123 7mo ago[dead]
- mbh159 7mo agoThe methodology debate in this thread is the most important part. The commenter who says "add obfuscation and success drops to zero" is right but that's also the wrong approach imo. The experiment isn't claiming AI can defeat a competent attacker. It's asking whether AI agents can replicate what a skilled (RE) specialist does on an unobfuscated binary. That's a legitimate, deployable use case (internal audit, code review, legacy binary analysis) even if it doesn't cover adversarial-grade malware. The more useful framing: what's the right threat model? If you're defending against script kiddies and automated tooling, AI-assisted RE might already be good enough. If you're defending against targeted attacks by people who know you're using AI detection, the bar is much higher and this test doesn't speak to it. What would actually settle the "ready for production" question: run the same test with the weakest obfuscation that matters in real deployments (import hiding, string encoding), not adversarial-grade obfuscation. That's the boundary condition.
- celeryd 7mo agoWhy does that matter? Being oblivious to obfuscated binaries is like failing the captcha test. Let's say instead of reversing, the job was to pick apples. Let's say an AI can pick all the apples in an orchard in normal weather conditions, but add overcast skies and success drops to zero. Is this, in your opinion, still a skilled apple picking specialist?
- sonofhans 7mo agoWhat if it’s 10x as fast during clear conditions? Then it doesn’t matter. No hate. My only point is that’s it’s easy for analogies to fail. I can’t tell the point of either of your analogies, where the OP made several clear and cogent points.
- xboxnolifes 7mo agoMaybe not, but also maybe you would no longer need skilled apple picking specialists.
- AlexeyBelov 7mo agoYou're replying to an LLM
- hilbert42 7mo agoWhat this tells me is that the era of code obfuscation through compilation is likely coming to an end. If anyone is able to reverse-engineer a program it'll have huge ramifications for the industry. This won't be welcomed by software developers who benefit from obfuscation but consumers could benefit. For example, AI could alter a program to remove or add features to suit users' requirements. Imagine being able to instruct AI to comb through Windows 11 and remove all telemetry and Copilot code and restore local accounts. I'd be very pleased with an AI agent tnat would do that.