The first framework that treats your AI agent like a hostile target just went open source.
The Summary
- DeepTeam is an open-source red teaming framework that simulates attacks on LLM systems to find vulnerabilities before production deployment
- Tests for 50+ vulnerability types including PII leakage, prompt injection, broken access control, and SQL injection using LLM-as-a-Judge scoring
- Runs entirely locally, built on top of DeepEval evaluation framework, with optional platform integration for production monitoring
The Signal
Security testing for AI agents has been manual theater. Teams write test cases, probe chatbots by hand, maybe hire a consultant to try jailbreaking their RAG pipeline for a week. DeepTeam flips that. It's automated penetration testing for LLMs, treating your agent like infrastructure that needs hardening.
The framework covers the full threat surface. Data privacy tests look for PII leakage and prompt exposure. Security checks test for BOLA (broken object-level authorization), BFLA (broken function-level authorization), and RBAC bypass. The responsible AI category hits bias, toxicity, ethics violations, and fairness issues. Each vulnerability type runs as an LLM-as-a-Judge metric, producing binary pass/fail scores with reasoning, all running locally on your machine.
"Think of it as penetration testing, but for LLMs."
Here's what matters: agent security is not LLM safety. Safety research focuses on model behavior, alignment, RLHF tuning. Security focuses on system integration, access control, data handling. When you connect an LLM to your database, give it function-calling abilities, or let it execute code, you've created an attack surface. DeepTeam tests that surface systematically.
The attack simulation covers:
- Jailbreaking attempts to bypass safety guardrails
- Prompt injection to override system instructions
- Multi-turn exploitation that builds trust before attacking
- SQL injection through natural language interfaces
Built on DeepEval, the existing open-source evaluation framework, which means it inherits that ecosystem's momentum. The local-first architecture matters for security-conscious teams who can't send production data to third-party APIs for testing. You can run the entire red teaming suite on your infrastructure, using your choice of LLM as the judge.
The production integration angle is smart. DeepTeam connects to Confident AI's platform for risk assessment tracking, vulnerability monitoring in production, and team reporting. Test locally during development, monitor continuously after deployment. That's the workflow companies actually need as they move agents from demos to production systems handling real user data.
The Implication
Every company building AI agents will need this workflow within six months. Not because they're security-conscious, but because their insurance provider will require proof of red teaming before coverage. Or their enterprise customer will demand it in the security questionnaire. Or their first data breach will force it.
The open-source release accelerates that timeline. Security through obscurity doesn't work when attackers have the same frameworks. DeepTeam standardizes what "tested" means for LLM systems. Start running it on your agents now, before testing becomes a checkbox you're rushing to fill for a contract deadline.