DeepTeam provides structured adversarial testing for LLM applications, covering the full spectrum of vulnerabilities cataloged by OWASP and NIST guidelines. The framework implements over 40 attack types organized into categories including jailbreak attempts that bypass safety filters, prompt injection attacks that redirect model behavior, data extraction techniques that expose training data or system prompts, hallucination-inducing inputs that generate plausible but incorrect outputs, and multi-turn attacks that gradually escalate through seemingly benign conversation patterns.
The framework integrates naturally with CI/CD pipelines, allowing teams to run adversarial test suites automatically before deploying LLM features to production. Each attack type generates detailed reports showing which vulnerabilities were exploited, the severity of potential impact, and specific inputs that triggered unsafe behavior. Teams can define custom attack scenarios relevant to their domain and track security improvements over time as defenses are strengthened. As the companion project to DeepEval, which handles functional evaluation, DeepTeam completes the testing picture by adding the security dimension that production LLM applications require.
The growing importance of LLM security testing is reflected in regulatory frameworks and enterprise procurement requirements that increasingly mandate adversarial testing before deployment. DeepTeam provides the tooling to meet these requirements with repeatable, automated test suites rather than ad-hoc manual testing. Its Apache-2.0 license and Python-native implementation make it accessible to any team building LLM-powered applications, from startups to enterprises with stringent compliance requirements.
