I Made an AI-Powered Static Analyzer for Binaries

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For at least a few years (since before COVID), in my circle everbody wants an AI tool that finds vulnerabilities in binaries. I have my reservations about such a tool because of my perception that AI is overhyped, so I decided to build one myself to see how useful it could be (or not). I was actually kinda surprised how well it worked for my collection of binaries with known CVEs. It definitely has false positives and negatives, as any static analysis tool would, but I think its actually somewhat usable. I dont think the results are super actionable, but interesting nonetheless. I still think the AI hype can be out there sometimes, but this project taught me a lot about the subject and forced me to think in ways that I wouldn't normally when doing RE/VR/program analysis. Right now, this is just a side project. It only looks for buffer overflows for now (still a common bug, believe it or not). I dont know if I'll expand this to more vulnerability types, work on trying to reduce the false positives, or if I'll even keep this going long term. Hoping to get some opinions on where to go next and find the bugs.

Visit product View on Hacker NewsApr 27, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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