Detect vulnerabilities in LLM applications with Datadog’s AI-native SAST

Datadog Code Security’s AI-native SAST is designed to identify LLM-specific security vulnerabilities earlier in development, addressing limitations of conventional pattern-based static analysis tools. It aligns its coverage with the OWASP Top 10 for LLM Applications, including prompt injection, sensitive-information disclosure, excessive agency, supply-chain risks, data and model poisoning, unbounded consumption, misinformation, hidden-context exposure, vector and embedding weaknesses, and improper output handling. Available for Python, Go, Java, C#, TypeScript, and JavaScript, the system uses LLM-based reasoning about code context and data flow, independently verifies findings, and delivers results through the Datadog platform, pull-request comments, and CI checks. Examples include taint analysis to trace unsanitized user input into LLM prompts, control-flow analysis to detect agents given unrestricted shell or file-system access without authorization checks, and pattern matching to find system prompts or other hidden context exposed through logs or API responses.

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