Guardrails and Security Testing for AI Agents

Invariant Labs specializes in automated security scanning and testing solutions built specifically for AI agent systems, a rapidly emerging risk surface. Invariant Labs’ tools detect vulnerabilities in agentic workflows, including what the company describes as “toxic agent flows” (situations where agents can be manipulated into leaking data, executing malicious code, or performing unintended high-impact actions).

Invariant Labs analyzers automatically discover flaws in real-world agent integrations, such as GitHub’s MCP implementations, while its flagship product, MCP-scan, continuously monitors interactions between agents and MCP systems using a proxy-based approach that requires no changes to existing infrastructure.

Beyond scanning, Invariant Labs provides a full suite for understanding, testing and benchmarking AI agent behavior. The company’s Explorer platform offers real-time visualization and debugging through trace analysis while its Testing library enables controlled simulations of edge cases, regressions, and performance bottlenecks.

Invariant Labs also maintains a public registry of agent benchmarks, turning large, unwieldy datasets like SWE-bench and WebArena into human-readable formats optimized for collaborative evaluation.

Acquired by Snyk in June 2025, Invariant Labs positions itself as a foundational layer for secure, test-driven agentic AI development.