No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by cyberark · Agent Tool · ★ 1.6k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is FuzzyAI safe to install? View the security audit →
FuzzyAI Fuzzer The FuzzyAI Fuzzer is a powerful tool for automated LLM fuzzing. It is designed to help developers and security researchers identify jailbreaks and mitigate potential security vulnerabilities in their LLM APIs. Getting Started Quick start #1 - Using an existing python project Install fuzzyai Run the fuzzer Quic
| Stars | 1,576 |
| Forks | 217 |
| Language | Jupyter Notebook |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 71.3880862534625/100 |
| Open Issues | 7 |
| Last Updated | 2026-02-06 |
| Created | 2024-12-03 |
| Est. Tokens | ~1403k |
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FuzzyAI is A powerful tool for automated LLM fuzzing. It is designed to help developers and security researchers identify and mitigate potential jailbreaks in their LLM APIs.. It is categorized as a Agent Tool with 1.6k GitHub stars.
FuzzyAI is primarily written in Jupyter Notebook. It covers topics such as ai, ai-red-team, fuzzing.
You can find installation instructions and usage details in the FuzzyAI GitHub repository at github.com/cyberark/FuzzyAI. The project has 1.6k stars and 217 forks, indicating an active community.
FuzzyAI is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to FuzzyAI on Agent Skills Hub include AI-Infra-Guard, agentic-radar, cursor-talk-to-figma-mcp. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: