AgentSafeLabs Launches safelabs-eval: Open-Source Security Framework for AI Agents
Key Takeaways
- ▸Zero-cost security testing framework covering all 10 OWASP ASI categories with 30 adversarial prompts—no LLM API costs
- ▸Works without code modifications, infrastructure setup, or agent inspection—test any HTTP endpoint or Python callable immediately
- ▸Five pattern-based detectors identify prompt injection, data leakage, jailbreaks, scope violations, behavioral drift, and hallucination risks
Summary
AgentSafeLabs has released safelabs-eval, an open-source red-teaming and evaluation framework designed to systematically test AI agents for security vulnerabilities aligned with the OWASP Agentic Security Initiative (ASI) Top 10. The framework addresses a critical gap in production AI deployment: most agents built on LangChain, CrewAI, AutoGen, and custom frameworks ship without systematic safety testing.
The tool fires 30 curated adversarial prompts across all 10 OWASP ASI categories and scores responses using five pattern-based detectors (PromptInjectionDetector, DataLeakageDetector, JailbreakDetector, ScopeViolationDetector, and HallucinationDetector). It requires no LLM API calls for detection, no modifications to agent code, and no infrastructure setup—users can test any HTTP agent endpoint or wrap Python callables via pip install.
The framework provides multiple deployment options: CLI-based testing against HTTP endpoints with support for authentication and custom timeouts, or a Python API for integration into existing codebases. Results include severity levels (CRITICAL, HIGH), confidence scores, remediation hints, and JSON output for CI pipeline integration.
- Seamless integration with popular agent frameworks (LangChain, CrewAI, AutoGen) with CLI and Python API options for flexible deployment
Editorial Opinion
safelabs-eval fills a critical gap in AI agent safety infrastructure. By making security evaluation free, code-agnostic, and instantly deployable, AgentSafeLabs removes friction from the adoption of security testing as agents proliferate into production systems. The focus on pattern-based detection rather than LLM-dependent scoring is pragmatic—fast, deterministic, and cost-effective for teams deploying agents at scale.
