Vulnerabilities in AI-Generated Code
AI code assistants like GitHub Copilot, ChatGPT, Cursor, and Claude frequently generate code containing security vulnerabilities. Studies show up to 40% of AI-generated code contains at least one security flaw. Precogs AI pre-LLM filters detect and prevent these flaws before they enter your codebase — including injection attacks, hardcoded secrets, broken authentication, and insecure deserialization patterns.
What vulnerabilities are common in AI-generated code?
The most frequent flaws introduced by AI assistants include SQL injection, cross-site scripting (XSS), hardcoded credentials, path traversal, SSRF, and insecure deserialization vulnerabilities. Because LLMs are trained on vast amounts of open-source code, they often reproduce common anti-patterns rather than secure coding standards.
Vulnerability Types
CWE-1236
HIGHCSV Injection in AI-Generated Export Functions
AI-generated CSV export functions write user-controlled data without escaping formula-triggering characters (=, +, -, @)...
CWE-942
HIGHPermissive CORS Policy in AI-Generated APIs
AI assistants generate CORS configurations with wildcard origins or overly permissive headers, enabling cross-origin dat...
Recently Discovered in AI-Generated Code
Browse the latest vulnerabilities and exposures dynamically tracked to the AI-Generated Code domain.
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