Breaking

DeepTeam, an Open-Source Red-Teaming Framework, Simulates Jailbreaks and Prompt Injection Against LLM Systems

July 14, 2026 at 02:53 EDT

Security
AI Agents
Open Source

Confident AI has released DeepTeam, an open-source framework that simulates adversarial attacks such as jailbreaking and prompt injection to surface vulnerabilities in large language model systems before they reach production. Positioned as "penetration testing for LLMs," the tool aims to automate the kind of red-teaming that security guidelines increasingly recommend for AI applications.

Open Source · LLM Security

DeepTeam: "Penetration Testing" for Large Language Models

An open-source framework that simulates adversarial attacks — jailbreaks, prompt injection, data leakage — to surface AI vulnerabilities before they reach production, automating the red-teaming that security guidelines now recommend.

50+

vulnerability categories across privacy, safety, security & agentic risk

20+

adversarial attack methods, single-turn and multi-turn

industry frameworks mapped: OWASP, NIST AI RMF, MITRE ATLAS

Attack Surface, By Type

Each block = an attack method. Multi-turn attacks escalate an exchange gradually — the harder class to defend.

~11

Single-turn
prompt injection, roleplay, Leetspeak, Base64, ROT13

Multi-turn
Linear, Tree & Crescendo Jailbreaking, Bad Likert Judge

How It Works

Simulate adversarial attacks locally

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Probe agents, RAG & chatbots

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LLM judge renders pass / fail

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Report failures in CI/CD

Seen As Strength

Ease of use, research-backed attacks
Automated multi-turn attacks
Explicit framework mapping, guardrails & CI/CD integration

Open Questions

Inherits the accuracy limits of its LLM judge
Newly released — maturity unproven at scale
Benchmarks & real-world use cases still accumulating

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