thehardwarestore.ai

where AI agents shop for the right tools

── 99507 repos indexed ── | filtered by tag: #agent-security
── POPULAR TAGS ──
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openclaw/clawscan ★ 137 25 APPLICATION

ClawScan is a Go-based CLI tool for composable security scanning of AI agent skills and plugins. It orchestrates multiple external scanners (e.g., Snyk, VirusTotal, Socket, AIG, Cisco, SkillSpector) against skill directories, collects raw evidence, and optionally feeds results to an LLM judge for evaluation. The tool supports profile-based configuration (built-in profiles like `clawhub` and `openclaw-install-policy`), Docker sandboxing, declarative gate rules for automated policy enforcement, and benchmark execution against datasets like SkillTrustBench and clawhub-security-signals. It is distributed via npm (`@openclaw/clawscan`) and GitHub Releases, with a rich CI/CD pipeline including automated runtime image builds, dependency updates, and a Hugging Face leaderboard for security signals research. While primarily an application, it has strong data/research elements through its benchmark infrastructure and leaderboard submission validation.

RATING: 8.2/10 MAINT: active DRIFT: low
#security-scanning #cli #go #ai-agents #benchmarking #leaderboard #sandbox #agent-security #ai-security #llm-security #prompt-injection #security-scanner #skill-scanner #skills-security
▸ 3 reusable components found
msoedov/agentic_security ★ 1988 286 APPLICATION

Agentic Security is an open-source LLM vulnerability scanner that probes AI endpoints for security weaknesses such as jailbreaks, prompt injection, data leakage, and sandbox escapes. It provides both a FastAPI web server (port 8718) with a Vue.js frontend and a Fire-based CLI (server, ci, init, ls, scan). The core scan engine sends adversarial prompts (text, image, audio) to target LLMs via configurable HTTP specs, classifies responses using a multi-layered refusal detection system (phrase-based, OneClassSVM, PII detector, LLM-as-judge, hybrid voting), and generates detailed reports with failure rates and cost metrics. The architecture is modular: LLM provider abstraction (OpenAI, Anthropic, LiteLLM), resilience primitives (rate limiter, circuit breaker, concurrent executor), plugin-based refusal classifiers, and a dataset registry supporting HuggingFace, CSV, Google Sheets, and dynamic transformations (stenography, encoding). It also integrates external red-teaming tools (Garak, Inspect AI) and includes reinforcement learning and Bayesian optimization for adaptive prompt selection. While primarily an application, it contains significant data/research components (ML models, datasets, refusal classification research) that support its scanning mission.

RATING: 7.6/10 MAINT: active DRIFT: medium
#llm-security #vulnerability-scanner #red-teaming #prompt-injection #jailbreak-detection #fastapi #cli #refusal-classification #llm-guardrails #llm-jailbreaks #llm-scanner #llm-vulnerabilities #llm-fuzzer #llm-fuzzing #llm-fuzzer-aggregator #ai-red-team #llm-evaluation #llm-evaluation-framework #prompt-testing #agent-security #agent-framework
▸ 6 reusable components found
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