TDDF: Behaviour Regression Tests for AI Agents
What it is
TDDF is a local-first testing framework that runs deterministic behaviour regression tests on AI agents using YAML-defined attack scenarios. It stages traps on local HTTP servers, executes the agent as a subprocess, and reports pass/fail results mapped to security compliance standards like OWASP LLM and NIST AI RMF.
The problem it solves
Existing AI agent security testing often relies on non-deterministic LLM judges or cloud services, making it difficult to create reproducible, local, and compliance-ready regression tests for agent behavior.
What you could use it for
- Detecting prompt injection vulnerabilities in LangGraph or OpenAI Agents SDK applications
- Verifying that agents do not exfiltrate secrets via hidden web content or document masking
- Generating compliance evidence (JUnit XML) for EU AI Act or ISO 42001 audits
- Regression testing agent behavior after model swaps or framework upgrades
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.