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.

Verified by Precogs Threat Research

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.

Explore AI-Generated Code by Category

Deep-dive into specific areas of ai-generated code to understand the attack surfaces, common vulnerability patterns, and how Precogs AI provides protection.

Vulnerability Types

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Detect AI-Generated Code Vulnerabilities Automatically

Precogs AI scans your code and binaries for AI-Generated Code vulnerabilities and generates AutoFix PRs — no manual review needed.