continuous-llm-red-teaming-with-promptfoo
SolidWire Promptfoo and DeepTeam into CI/CD for automated regression red-teaming of LLM apps against OWASP LLM Top 10 and OWASP Agentic presets, failing the build when jailbreak or injection vulnerabilities regress.
Install
Quality Score: 90/100
Skill Content
Details
- Author
- adriannoes
- Repository
- adriannoes/awesome-agentic-ai
- Created
- 9 months ago
- Last Updated
- 2 days ago
- Language
- Jupyter Notebook
- License
- MIT
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
red-team-llm-app
Use this to adversarially test an LLM/agent app before attackers do - prompt injection, jailbreaks, data exfiltration, tool misuse, and unsafe output. Trigger on "red team my LLM", "test for prompt injection", "is my agent secure", "jailbreak testing", "security review of my AI app", especially before shipping anything customer-facing or with tools/data access. Test systematically against the known attack classes, not ad-hoc.
adversarial-prompt-testing
Test LLM applications for prompt injection, jailbreak, data exfiltration, and indirect injection attacks — attack taxonomy, test harness design, automated red-team probes, defense patterns, and evaluation rubrics. Use when asked about "prompt injection", "jailbreak", "LLM red team", "adversarial prompts", "indirect injection", "exfiltration via LLM", "test AI security", "LLM attack surface", "OWASP LLM Top 10", "system prompt leak", "prompt leaking", or "AI safety testing". Do NOT use for: traditional app security — see red-team-check or security-review. Do NOT use for: model alignment — focus is on app layer.
llm-red-team
Systematic LLM red-teaming across the OWASP LLM Top 10 (2025). Use when assessing an LLM app, chatbot, RAG pipeline, or agent for prompt injection, data leakage, output-handling flaws, excessive agency, or unbounded consumption, and when producing a structured, evidence-backed red-team report.