Code Property Graph data-flow analysis, with an LLM evaluating context at every node. Near-zero false positives on taint-style vulnerabilities.
Not just "is there a sanitizer" but "does this encoding prevent this injection in this context." A Code Property Graph combines AST, control flow, and data flow. An LLM evaluates every node. An agent validates every path. Only confirmed findings reach the report.
Validated taint-style data-flow vulnerabilities and point issues that live at a single line of code. Every finding ships with a traced path or concrete exploit context, never a hypothetical warning.
User input reaches a query parameter via unescaped concatenation. Parameterization and escaping evaluated at each node.
Reflected or stored input renders in the DOM without contextual encoding. HTML, attribute, JS, and URL contexts evaluated separately.
User-controlled URL parameter reaches an outbound HTTP client. Allowlists, URL parsing, and metadata-endpoint risk checked per path.
User input reaches file I/O without path normalization. Command injection (input reaching shell execution without sanitization) is traced with the same pipeline.
Hardcoded credentials and API keys embedded in source code. Verified against the broader codebase to confirm they aren't overridden elsewhere.
MD5 for passwords, ECB mode, weak RNG in security-sensitive contexts. Flagged only when used in a security-relevant code path.
Missing Secure, HttpOnly, or SameSite flags on session cookies. Permissive CORS policies that expose authenticated endpoints to untrusted origins.
Susceptible to timing attacks; missing constant-time equality. Detected in token, signature, and credential comparison paths.
We compile your codebase into a Code Property Graph (AST, control flow, and data flow in one structure), then analyze it in three phases. Only validated, high-confidence paths reach the final report.
Sources (user inputs, API parameters, file reads, environment variables) and sinks (SQL queries, shell commands, file writes, response outputs) are extracted using deterministic patterns plus AI-discovered custom input handlers unique to your framework.
We trace backward from every sink toward potential sources. At each node, an LLM evaluates whether the specific sanitization actually addresses the specific risk in the specific context.
An autonomous agent validates each candidate path for control flow correctness (can this branch actually execute?) and logic correctness (do the types and constraints line up?), producing confidence scores. Only validated, high-confidence paths make the final report.
The UI renders source (green), sink (red), and intermediate hops (blue) with line-by-line code context. Developers see the complete attack chain, not just endpoints.
Some vulnerabilities don't need cross-function data flow. They live at a single line of code. A cost-optimized two-stage LLM pipeline runs on every file in the repository to catch them.
Quickly classifies each file to determine whether it's production code and whether it likely contains a vulnerability (crypto, auth, misconfiguration, access control). Low-confidence files are filtered out immediately.
Category-specific deep scanning of full files. Only HIGH-confidence, concretely exploitable vulnerabilities are reported. Theoretical or speculative issues are discarded.
Stage 2 is intentionally conservative: a finding ships only if the agent can describe how an attacker would actually trigger it.
Schedule a demo and watch Agentic SAST run against a real codebase.