getdebug 0.4.0 adds Python AI-app security patterns, outperforms generic SAST tools

getdebug is addressing a genuine blind spot in the open-source security-linting landscape. Bandit and Semgrep are mature, widely-used tools designed for general Python security and cross-language SAST respectively; they excel at catching SQL injection, unsafe deserialization, and missing integrity checks. But neither was built to understand LLM-specific risks—serializing user objects into prompts, streaming without timeouts, merging incompatible role fields in message arrays. The benchmark reveals the cost of that generality: Bandit's 1,228 assert_used warnings on a 49-file codebase are noise for most production environments, and Semgrep's three findings are real but generic, not AI-app specific.

vulnhuntr, positioned as the category leader for LLM-driven AI-app analysis, takes a different approach by invoking an LLM to infer entry points and vulnerabilities. The tradeoff is scope: it selects files based on web-app heuristics, leaving CLI tools unanalyzed. getdebug's regex-based layer sidesteps that by running on any Python that touches an LLM SDK, whether web or command-line. The trade-off in the other direction is that hand-written regex patterns can miss novel attack surfaces that an LLM might infer; the tool's designers acknowledge this and invite open community feedback.

The public benchmark methodology and open-source corpus (CodeSecBench on GitHub) set a high bar for transparency. In the real-world test on simonw/llm, getdebug's seven findings included two that depend on threat model (prompt-injection between two CLI-user inputs in a single-user tool) and role-merge logic routed through API responses rather than request input. The tool surfaces everything flagged, lets teams triage via .getdebug-ignore files, and acknowledges the borderline cases—a stance that trades off noise reduction for completeness.