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 user1342 · Agent Tool · ★ 124
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Awesome-LLM-Red-Teaming safe to install? View the security audit →
Awesome LLM Red Teaming A comprehensive list of tools and resources for red-teaming large language models (LLMs), vulnerability research, and reverse engineering using various techniques like prompt manipulation, adversarial testing, and model interpretation. LLM Red Teaming Resources by Technical Function Playgrounds and practice targets Folly - Open-source LLM prompt injection and jailbreak playground for hands-on experimentation. DamnVulnerableMathLLM - Intentionally vulnerable maths LLM app for practising prompt injection and logic exploits. DamnVulnerableShoppingLLM - Intentionally vulnerable shopping assistant scenario for testing data leakage and unsafe tool use. [RedTeam Arena
| Stars | 124 |
| Forks | 24 |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 56.031650113801/100 |
| Open Issues | 9 |
| Last Updated | 2025-09-04 |
| Created | 2024-11-11 |
| Est. Tokens | ~53k |
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Awesome-LLM-Red-Teaming is A curated list of awesome LLM Red Teaming training, resources, and tools.. It is categorized as a Agent Tool with 124 GitHub stars.
You can find installation instructions and usage details in the Awesome-LLM-Red-Teaming GitHub repository at github.com/user1342/Awesome-LLM-Red-Teaming. The project has 124 stars and 24 forks, indicating an active community.
Awesome-LLM-Red-Teaming is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Awesome-LLM-Red-Teaming on Agent Skills Hub include curiso, cai, superml. 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: