How to Secure AI-Generated Code with Endor Labs

Securing AI-generated code is increasingly vital as tools like GitHub Copilot and Cursor become staples in software development, introducing unique risks such as design flaws, vulnerabilities, and architectural issues that traditional security tools often overlook.

Endor Labs offers a robust platform to address these challenges by seamlessly integrating security into the software development lifecycle (SDLC).

The platform tackles the inherent risks of AI-generated code, which, according to a 2023 Snyk survey, often contains bugs or critical vulnerabilities due to its reliance on open-source software.

Endor Labs mitigates these risks through advanced program analysis, a comprehensive security dataset, and AI-driven tools designed to identify, prioritize, and remediate issues effectively.

The process begins with integrating Endor Labs into the development workflow. The Meta-Code Protocol (MCP) plugin works with AI coding tools to scan code in real-time within the Integrated Development Environment (IDE), catching potential issues before they reach pull requests (PRs).

During PRs, Endor Labs employs multiple AI agents to review code changes, acting like human developers, architects, and security engineers. These agents detect high-impact changes, such as new API endpoints, modified authentication logic, or insecure data handling, ensuring context-aware analysis that goes beyond traditional Static Application Security Testing (SAST).

Within the CI/CD pipeline, Endor Labs unifies Software Composition Analysis (SCA), SAST, secrets scanning, and container scanning, providing comprehensive visibility across the SDLC. Endor Labs excels in prioritizing and remediating vulnerabilities. Its proprietary function call graph analysis reduces false positives by 92% on average, allowing developers to focus on exploitable risks.

The platform’s Endor Score evaluates vulnerabilities based on security, activity, popularity, and code quality, while automated tools like Endor Patches apply fixes or backport security patches without requiring complex upgrades.

Additionally, Endor Labs secures open-source dependencies and AI models by generating accurate Software Bill of Materials (SBOMs) and evaluating models for security and quality. Continuous monitoring ensures ongoing protection, tracking AI model provenance and supporting compliance through automated SBOMs and integration with frameworks like PCI DSS.