Running AI-Generated Code Safely

AI-generated code breaks pipeline assumptions—scan and block must replace scan and report.

Adversarial inputs to code-executing AI agents can bypass containment and reach production systems.

Attackers can register hallucinated package names before developers discover they're fake.

Hybrid LLM-static analysis systems outperform rule-based scanners on vulnerability detection.

Running AI agents under dedicated low-privilege OS users prevents unauthorized access.

AI-generated code contains nearly three times more vulnerabilities than human-written code.

Market projections vary wildly because vendors disagree on what RASP actually includes.

Trusted repositories can be silently compromised through tag hijacking and account takeovers.

Legacy security tools can't see attacks that happen inside the model's inference process.