AI Red Teaming: 5 Vendors and 4 Tools Compared for 2026
AI Red Teaming covers tools and services that attack your model before a real adversary does, hunting injection paths, jailbreaks, and data leaks. It maps most closely to OWASP LLM risks LLM01 Prompt Injection, LLM07 System Prompt Leakage, LLM09 Misinformation. 5 vendors and 4 open-source tools sit in this category, scored as of .
5 of 5 AI Red Teaming vendors score Covered for LLM01 Prompt Injection, as of . See the whole market in the coverage matrix.
Vendors
Ranked by llmthreat's editorial coverage score, as of 2026-07-13 — public documentation, not a third-party audit.
| Vendor | Covered | Partial | Status | Funding | Best for |
|---|---|---|---|---|---|
| TrojAI | 5/10 | 4 | Acquired | — | Enterprises wanting model/agent red teaming with OWASP mapping plus runtime firewall for LLM and MCP/agentic workflows |
| Giskard | 4/10 | 4 | Active | €7.5M + $5M round (2025) | ML/AI teams needing continuous, automated red teaming and hallucination/robustness testing before and after deployment, with open-source and sovereign options |
| Mindgard | 4/10 | 4 | Active | $11.6M+ | Security teams wanting continuous automated red teaming and attack-surface mapping for LLMs, image models and agents |
| Robust Intelligence | 3/10 | 5 | Acquired | ~$44M total (seed + Series A + $30M Series B) | Automated pre-deployment red teaming plus runtime guardrails, especially for Cisco-aligned enterprise stacks |
| CalypsoAI | 2/10 | 7 | Acquired | ~$40M+ venture funding | Continuous agentic red-teaming plus real-time inference guardrails for large enterprise and national-security use cases |
Open-source tools
| Tool | License | Language | Stars | Last push | Status | Primary use |
|---|---|---|---|---|---|---|
| garak | Apache-2.0 | Python | 8,401 | 2026-07-10 | Active | LLM vulnerability scanner — automated probing for jailbreaks, prompt injection, data leakage, hallucination, and other failure modes |
| Giskard | Apache-2.0 | Python | 5,506 | 2026-07-10 | Active | Open-source evaluation and testing library for LLM agents/RAG — detects hallucination, bias, prompt injection, and other quality/security issues |
| promptfoo | MIT | TypeScript | 23,165 | 2026-07-12 | Ownership changed | Prompt/agent/RAG testing and LLM red-teaming — vulnerability scanning, eval, and CI/CD security testing for AI applications |
| PyRIT | MIT | Python | 4,086 | 2026-07-12 | Active | Python Risk Identification Tool — open-source framework for red-teaming/risk-identification of generative AI systems |
How to choose a AI Red Teaming product
AI red-teaming products differ less on "does it find jailbreaks" and more on what they attack (a bare model vs. your deployed RAG/agent stack), how (a fixed public prompt library vs. an adaptive attacker LLM), and where your data goes while they do it.
- 01 Static attack library vs. adaptive multi-turn agent
- garak (NVIDIA, Apache-2.0) and PyRIT (Microsoft, MIT) run fixed probe/prompt libraries — reproducible and auditable, but public, so model vendors patch the known jailbreaks within weeks and your score inflates over time. Platforms like Mindgard, HiddenLayer AutoRT, and DeepTeam run an attacking LLM that adapts across turns, reframing intent when turn one gets refused — catches more real failures but results aren't perfectly reproducible run-to-run and cost scales with attacker-model tokens.
- 02 Model-only testing vs. full application/agentic surface
- garak and PyRIT mostly attack the raw model endpoint: prompt injection, jailbreak, data leakage. They don't touch your RAG pipeline, tool-calling, system prompt, or multi-agent handoffs. Tools built for agentic red teaming (DeepTeam's agentic guide, Mindgard's multimodal coverage, agentic_security) add tool-misuse and cross-agent attack paths — the layer where most production incidents actually happen, since the base model itself was probably already tested by its provider.
- 03 Continuous/CI-integrated scanning vs. point-in-time engagement
- A one-off pentest (or a single garak/promptfoo run) gives you a report dated the day you ran it — worthless after your next prompt or model change. Mindgard and similar platforms sell scheduled, CI-triggered campaigns that re-run on every deploy, catching regressions, for a recurring subscription instead of a one-time cost. If you ship prompt changes weekly, point-in-time coverage is stale within days.
- 04 Where your prompts and outputs go
- SaaS red-teaming platforms send your model's inputs/outputs (and often your system prompt) through their infrastructure to score attacks — a real problem if you're testing a regulated or IP-sensitive deployment. Open-source tools (garak, PyRIT, promptfoo, agentic_security) run wherever you host them, keeping data in your network, but you own the judge model, the reporting, and the framework mapping yourself.
- 05 Framework mapping is only as good as the judge
- Every platform claims OWASP LLM Top 10 and MITRE ATLAS mapping, but the number that matters — did the attack actually succeed — comes from an LLM-as-judge scoring the transcript. Vendors rarely publish the judge's accuracy or false-positive rate. Open-source stacks (PyRIT + your own scorer, or promptfoo's eval assertions) let you inspect and tune the judge; closed platforms ask you to trust their dashboard number.
A "covers 10/10 OWASP LLM Top 10 risks" claim usually means one test fired per category, not that the vulnerability was found and confirmed exploitable — the pass/fail call comes from an LLM judge whose accuracy the vendor rarely discloses. Check the judge and the probe library's last-update date before trusting the score; public jailbreaks get patched by model providers within weeks, so a report older than a release cycle tells you nothing about current risk.
Frequently asked questions
What is AI Red Teaming?
Tools and services that attack your model before a real adversary does, hunting injection paths, jailbreaks, and data leaks.
Which vendors lead AI Red Teaming coverage?
TrojAI, Giskard, Mindgard cover the most OWASP LLM Top 10 risks in AI Red Teaming, by llmthreat's scoring, as of 2026-07-13. See the ranked table above.
What is the best AI Red Teaming tool for prompt injection?
TrojAI leads AI Red Teaming for Prompt Injection, by llmthreat's coverage scoring: Enterprises wanting model/agent red teaming with OWASP mapping plus runtime firewall for LLM and MCP/agentic workflows. Compare the full ranked list above.