What is the AI Red Team? Top 18 AI Red Group Tools (2025)

What is the AI Red Team?

AI Red Team It is the process of systematically testing artificial intelligence systems (especially generated AI and machine learning models). The Red Team goes beyond classic penetration testing; the penetration testing targets known software flaws, but the Red Team probes unknown AI-specific vulnerabilities, unforeseen risks and emerging behaviors. The process adopts the mindset of a malicious opponent, simulates attacks such as timely injection, data poisoning, jailbreak, evasion of models, bias exploitation, and data leakage. This ensures that AI models can not only resist traditional threats, but also resist new abuse solutions unique to current AI systems.

Key features and benefits

• Threat Modeling: Identify and simulate all potential attack scenarios – from rapid injection to adversarial operations and data penetration.
• Realistic opponent behavior: In addition to what is covered by penetration testing, manual and automated tools are used to simulate actual attacker techniques.
• Vulnerability Discovery: Discover risks such as bias, fairness gap, privacy exposure and reliability failure that may not appear in pre-issuance tests.
• Regulatory compliance: Support compliance requirements (EU AI Act, NIST RMF, US enforcement order) increasingly mandatory red teams for high-risk AI deployment.
• Continuous security verification: Integrated into the CI/CD pipeline, allowing for continuous risk assessment and elastic improvement.

The Red Team can be a platform built by an internal security team, a professional third party, or only for adversarial testing of AI systems.

Top 18 AI Red Group Tools (2025)

Here is a rigorously researched list of the latest and most well-known AI red team tools, frameworks and platforms, open source, commercial and industry-leading solutions for general and AI-specific attacks:

• Mindgard – Automated AI Red Group and Model Vulnerability Assessment.
• Garak – Open Source LLM Adversarial Testing Toolkit.
• pyrit (Microsoft) – Python risk identification toolkit for AI Red Team.
• AIF360 (IBM) – The AI Fair 360 toolkit is used for bias and fairness assessment.
• Foolbox – A library for adversarial attacks on AI models.
• Granica – Sensitive data discovery and protection for AI pipelines.
• AdvertTorch – Adversarial robustness test for ML models.
• Adversarial Robustness Toolbox (ART) – IBM’s open source toolkit for ML model security.
• BROCKHILL – A generator that automatically jailbreaks attempts LLM.
• BURPGPT – Web security automation using LLMS.
• Cleverhans – Benchmarking of adversarial attacks in ML.
• CounterFit (Microsoft) – CLI for testing and mocking ML model attacks.
• DreadNode Crucible – ML/AI Vulnerability Detection and Red Team Toolkit.
• Galah – AI Honeypot framework that supports LLM use cases.
• Meerkat – Data visualization and adversarial testing of ML.
• GHIDRA/GPT-WPRE – Code reverse engineering platform with LLM analysis plugin.
• Guardrail – LLMS application safe, timely injection defense.
• SNYK – Developer-centric LLM Red Team Tool simulates fast injection and adversarial attacks.

in conclusion

In the era of generating AI and large language models, AI Red Team Has become the basis for responsible and resilient AI deployment. Organizations must adopt adversarial testing to identify hidden vulnerabilities and adjust their defenses into new threat media, including attacks driven by rapid engineering, data breaches, biased exploitation, and emerging model behavior. The best practice is to combine manual expertise with an automation platform that utilizes the top red team tools listed above to integrate, proactive security postures in AI systems.