by corca-ai · Agent Tool · ★ 51
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 51 |
| Forks | 6 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 54/100 |
| Last Updated | 2023-07-11 |
| Created | 2023-07-04 |
| Platforms | python |
| Est. Tokens | ~1k |
LLMFuzzAgent Can we create a fuzzer to discover vulnerabilities in the LLMs? This is the initial step towards finding the answer. Based on the following results, LLMs have the potential to be white-hat hackers. Gandalf Challenge - Write-up https://github.com/corca-ai/LLMFuzzAgent/assets/64528476/d0b1293f-f307-48a4-a75c-32bb864b085f Gandalf is a prompt injection game.
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: