Top AI Red Teaming & Testing Tools for 2026

Developer-friendly CLI tool for testing, evaluating, and red teaming LLM applications.

Generative AI Red-teaming & Assessment Kit, now maintained by NVIDIA. Scans LLMs for hallucinations, data leakage, and prompt injection with a comprehensive probe library.

Automated evaluation and security testing platform for Large Language Models to catch hallucinations and safety issues.

Open-source testing framework dedicated to ML models and LLMs, covering bias, performance, and security flaws.

Offensive-security platform automating adversarial testing for LLMs and custom agents to identify vulnerabilities before deployment.

Automated adversary emulation platform protecting commercial and custom GenAI models, powered by dark web intel.

AI agent trust platform spanning evaluation (Diamond), runtime defense (Dome), hardened components (Depot), and continuous improvement via reinforcement learning (Darwin), producing agent trust scores mapped to NIST AI RMF and the EU AI Act. Raised a $17M round in November 2025 and was named a Gartner Cool Vendor in agentic AI trust and security.

Automates red teaming ('haizing') of LLMs and AI applications, using search and fuzzing algorithms to surface inputs that trigger unsafe behavior, paired with calibrated Judges for evaluation and monitoring. Works with AI labs including Anthropic and AI21; closed a $12.5M seed announced December 2025.

Focuses on rigorous red teaming, offering a platform to simulate attacks on AI models to uncover vulnerabilities.

Offers 'Citadel Lens' for automated red teaming and evaluation of LLM applications, focusing on reliability and fairness.

San Francisco startup providing automated AI red teaming with post-trained attacker models, AI detection and response, runtime guardrails, and AI asset management across agents, copilots, and MCPs. Known for headline exploits like the Cursor/Supabase MCP data-leak disclosure; founded 2025 by researchers from Cohere, NVIDIA, and DeepMind; $10M seed led by Altos Ventures (April 2026).

Protects the behavior of AI/ML and GenAI models at build time (testing) and run time (firewall).

Combines ARTEMIS, an automated red-teaming engine testing AI systems against 15M+ attack patterns in 100+ languages, with ARGUS runtime guardrails that block malicious inputs in under 100ms, plus AI asset inventory and an MCP gateway. Guardrail policies are auto-calibrated from red-team findings.