How to Test AI Agents for Security
AI agent testing for security involves verifying that an agent doesn't have vulnerabilities like infinite loops, prompt injection paths, missing authorization checks, or compliance gaps. Unlike traditional testing, agent testing must account for non-deterministic LLM behavior and multi-step agentic workflows.
Frequently Asked Questions
How do you test AI agents for security?
Three layers: (1) Static analysis — scan code for structural vulnerabilities like missing loop bounds, unsafe prompt templates, and missing authorization. (2) Integration testing — test agent workflows with adversarial inputs. (3) Runtime monitoring — observe agent behavior in production for anomalies.
Why is static analysis important for AI agent testing?
AI agents are non-deterministic — runtime testing can't cover all execution paths. Static analysis examines the code structure to find vulnerabilities that might only trigger under specific conditions. It catches issues like missing max_iterations that work fine in testing but fail in production.
What should I test in an AI agent?
Key areas: loop termination (do all loops have bounds?), prompt injection resistance (can user input reach prompts unsanitized?), tool authorization (are high-risk tools gated?), error handling (what happens when tools fail?), and cost bounds (is there a token/cost ceiling?).
How do I add Inkog to my CI/CD pipeline?
Add the Inkog GitHub Action to your workflow. It scans on every PR and blocks merges if critical vulnerabilities are found. SARIF output integrates with GitHub Security tab.
How Inkog Detects This
Inkog provides static analysis as the first layer of AI agent security testing. It scans your codebase for structural vulnerabilities — infinite loops, prompt injection paths, missing authorization, and compliance gaps — before the code reaches production.
npx -y @inkog-io/cli scan .Start Testing Your Agents
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