AI Code Security Anti-Patterns distilled from 150+ sources to help LLMs generate safer code. Use as context for AI assistants or deploy as a standalone security review agent.
Real security vulnerabilities AI commonly generates, with secure alternatives.
AI-generated code has significantly higher vulnerability rates than human-written code.
Ranked by Priority Score = (Frequency x 2) + (Severity x 2) + Detectability
Deploy Sec-Context as a security review agent between AI generation and production.
Copilot, Claude, GPT
Security Review
Production-ready
Synthesized from 150+ individual sources across 6 primary categories.
NVD, MITRE CWE, Wiz - 40+ CVEs including IDEsaster collection
Stanford, ACM, arXiv, IEEE, USENIX - Empirical vulnerability studies
Dark Reading, Veracode, Snyk, Checkmarx, OWASP
HackerNews (17+ threads), Reddit (6 subreddits)
Twitter/X security researchers - Real-time incidents
Security advisories, academic studies, code analysis
Large files by design - comprehensive security references for AI consumption.
Full coverage of 25+ patterns. Quick reference format with BAD/GOOD examples for each vulnerability type.
Deep-dive on 7 critical patterns. Multiple examples, attack scenarios, edge cases, and complete mitigations.