Comment by jjjutla

Thank you. SAST tools built on AST or call graph parsing will struggle to detect code logic vulnerabilities because their models are too simplistic. They lose the language-specific semantics in dynamically typed languages where objects change at runtime, or in microservices where calls span multiple services. So they are limited to simple pattern-based detections and miss vulnerabilities that depend on long cross-file call chains and reflected function calls. These are the types of paths that auth bypasses and privilege escalations occur in.

AI code review tools aren’t designed for security analysis at all. They work using vector search or RAG to find relevant files, which is imprecise for retrieving these code paths in high token density projects. So any reasoning the LLM does is built on incomplete or incorrect context.

Our indexer uses LSIF for compiler-accurate symbol resolution so we can reconstruct full call chains, spanning files, modules, and services, with the same accuracy as an IDE. This code reasoning, tied with the LLM's threat modelling and analysis, allows for higher fidelity outputs.