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 meta-llama · Agent Tool · ★ 4.4k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is PurpleLlama safe to install? View the security audit →
🤗 Models on Hugging Face  Llama Guard Paper  Purple Llama Purple Llama is an umbrella project that over time will bring together tools and evals to help the community build responsibly with open generative AI models. The initial release will include tools and evals for Cyber Security and Input/Output safeguards but we plan to contribute more in the near future. Why purple? Borrowing a concept from the cybersecurity world, we believe that to truly mitigate the challenges which generative AI presents, we need to take both attack (red team) and defensive (blue team) postures. Purple teaming, composed of both red and blue team responsibilities, is a collaborative approach to evaluating and mitigating potential risks and the same ethos a
| Stars | 4,357 |
| Forks | 768 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 54.5596856812448/100 |
| Open Issues | 82 |
| Last Updated | 2026-08-18 |
| Created | 2023-12-06 |
| Platforms | python |
| Est. Tokens | ~15k |
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PurpleLlama is Set of tools to assess and improve LLM security.. It is categorized as a Agent Tool with 4.4k GitHub stars.
PurpleLlama is primarily written in Python.
You can find installation instructions and usage details in the PurpleLlama GitHub repository at github.com/meta-llama/PurpleLlama. The project has 4.4k stars and 768 forks, indicating an active community.
The top alternatives to PurpleLlama on Agent Skills Hub include open-saas, notebooklm-skill, agent-skills. 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: