Hackmenot – Security scanner for AI-generated code

Hacker News

Hey HN! I built hackmenot because I kept seeing the same security vulnerabilities in AI-generated code. The problem: AI assistants optimize for "code that works," not "code that's secure." They routinely generate SQL injection via f-strings, hardcode API keys, use os.system() with user input, and pick weak crypto like MD5. What hackmenot does: - 100+ rules purpose-built for AI code patterns - Python, JavaScript, Go, Terraform - Auto-fix mode (hackmenot scan . --fix) - Detects hallucinated packages (dependencies AI made up that don't exist) - Sub-second scans with caching - GitHub Action with SARIF support Install: pip install hackmenot It's Apache 2.0, no API keys needed, works offline. Would love feedback on the rules coverage and any patterns I'm missing. Happy to answer questions!

Visit product View on Hacker NewsJan 31, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, code · Missing: mac, agents, macos

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, io · Missing: https docs, excited, just released

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

30%30% 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

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

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