ClawGuard is a security application designed to protect AI agent ecosystems through static code analysis, runtime monitoring, and sandboxed execution. The backend is a FastAPI server providing authentication (JWT), a code security check pipeline (AST scanning + sandbox execution with sys.settrace monitoring), and audit log storage in SQLite. The frontend is a single-page HTML dashboard with Chinese/English i18n, simulating a defense matrix with real-time feedback. The system demonstrates a multi-layered defense: static prevention (AST blacklist), dynamic detection (monkey-patched builtins, runtime behavior monitoring), and mitigation (timeout, permission errors). It is a well-structured, self-contained application with clear separation of concerns, though the frontend is monolithic. The code quality is strong, with reusable components in the core modules, but the overall architecture is tightly coupled to the specific security policy.
This repository is a manually curated awesome-list/registry of open-source security scanners and offensive/defensive tools. It spans traditional security testing categories (web, mobile, binary, smart-contract, IoT, APT) and an actively expanded section for AI/LLM-agent security. The actual content is a set of near-identical English, Chinese, and Spanish README files containing hundreds of one-line tool entries with shields.io badges for language, last commit, stars, and license. There is no application runtime, no build pipeline, and no test suite; the product is a navigable knowledge base rather than software. The repo is therefore classified as data_research only in the sense that it is a structured, human-maintained metadata registry, not a data pipeline. The primary data lineage is manual: maintainers sample external GitHub projects, annotate each with a one-sentence description and category, and publish the result as markdown. The taxonomy serves as an analytical schema, answering questions such as which open-source scanners exist for a given domain, which are active, and what maturity signals (stars, license, last commit) they carry. It does not provide quantitative benchmarks or automated validation, and the data is not exposed in machine-readable form beyond the markdown itself. Reuse is straightforward for human readers, but automation requires parsing the markdown and accepting that some badges or links may be stale or inconsistent. The repository is actively curated, with recent emphasis on AI security, a documented commit convention, and multilingual READMEs. Its main risks are extrinsic: link rot, renamed or abandoned upstream tools, and the CC BY-NC-ND license, which restricts derivative redistribution. Technical drift is moderate because some older categories and tools are outdated even though the overall list is kept current.