AgentSafeLabs – Launched Open-source Security framework for AI agents
AgentSafeLabs has launched an open-source security framework called safelabs-eval for evaluating AI agents. This framework aligns with the OWASP Agentic Security Initiative and allows users to test AI agents for vulnerabilities without requiring modifications to the agent code. It provides a structured security report based on 30 curated adversarial prompts across various categories.
- ▪safelabs-eval is designed to evaluate AI agents against the OWASP Agentic Security Initiative Top 10.
- ▪The framework can test any agent endpoint and does not require any infrastructure setup.
- ▪It generates a security report in seconds, scoring responses with pattern-based detectors.
Opening excerpt (first ~120 words) tap to expand
safelabs-eval Open-source red-teaming and evaluation framework for AI agents — aligned to the OWASP Agentic Security Initiative (ASI) Top 10. AI agents built on LangChain, CrewAI, AutoGen, and custom frameworks ship to production without systematic safety testing. safelabs-eval changes that. Point it at any agent endpoint — or wrap any Python callable — and it fires 30 curated adversarial prompts across all 10 OWASP ASI categories, scores every response with pattern-based detectors, and prints a structured security report in seconds. No LLM calls required for detection. No agent code modifications required. No infrastructure setup.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.