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Security Cards – Reducing insecure AI-generated code by 72%

Hacker News

Security Cards – Reducing insecure AI-generated code by 72%

AI coding agents often generate functionally correct but insecure code. To address this issue, we have open-sourced Security Cards, targeted security guidance for 80+ widely used libraries across 13 programming languages. Our evaluation shows that the Security Cards reduce the rate of insecure code generation by up to 72.3% in Claude Code with Opus 4.7. You can find these security cards on GitHub ( https://github.com/Reware-Labs/securitycards ) or on our website ( https://securitycards.rewarelabs.com/ ) We are planning to constantly support more languages and libraries, and we’d be glad to hear your feedback, especially on where these cards would be most useful in your workflow, and which libraries we should support next.

Visit product View on Hacker NewsSep 10, 2026

Actual performance

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2comments

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% 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 · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

17%17% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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