Anthropic-Cybersecurity-Skills
817 structured cybersecurity skills for AI agents Β· Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Frβ¦
Open-source red-teaming framework that stress-tests LLMs and AI agents.
deepteam is an open-source agents project by confident-ai (confident-ai/deepteam on GitHub). It has 2,982 GitHub stars and 488 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 1 October 2026.
Point the framework at the LLM or agent you want to test.
Select jailbreaks, prompt injection, and other vulnerability categories.
Automated attacks fire at the model and every response is recorded.
Review severity-scored results and patch the weaknesses it surfaced.
Ready-made jailbreaks, injections, and edge-case attacks.
Tests multi-step agents, not just single chat replies.
Every run produces severity-ranked findings to triage.
Drops into existing test suites and CI pipelines.
Maps findings to known LLM risk categories.
Write custom attack modules for your own threat model.
Download the source and move into the project folder.
git clone https://github.com/confident-ai/deepteam.git
cd deepteamEvery project documents its own setup; start with install requirements.
cat README.md | head -60Follow the projectβs launch command β Docker, pip, npm, or a binary release.
# see README for the exact launch commandIt is an open-source project at confident-ai/deepteam with 488 forks. Check the repoβs contributors and recent commits to judge how active maintenance is.
Yes. It is Apache-2.0 licensed, so commercial use is allowed with attribution.
No. It automates repetitive attacks; skilled testers still handle novel exploits.
Any LLM or agent you can send prompts to, self-hosted or API-based.
A Python install plus a config pointing at your target; the README walks through a first attack.
817 structured cybersecurity skills for AI agents Β· Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Frβ¦
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Test prompts, agents and RAGs β LLM evals, red teaming and model comparisons from the CLI
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