garak vs PyRIT vs DeepTeam (2026): LLM Red Teaming Tools Compared

If you are picking between the three main open-source LLM red teaming tools in 2026, the short answer is this: run garak when you want a fast, broad vulnerability sweep with no code, PyRIT when you want to build adaptive multi-turn attack campaigns in Python, and DeepTeam when you need findings mapped to OWASP and NIST categories for a report someone will audit. This post compares all three on probes, attack depth, agent coverage, licences, and CI fit. For the checklist those findings should map back to, see our OWASP LLM Top 10 testing checklist.

The short answer

• garak - NVIDIA’s Apache 2.0 command-line LLM vulnerability scanner. Point it at a model or endpoint, pick probe families, get a graded report. Best when you want coverage breadth fast and nobody on the team wants to write attack code.
• PyRIT - Microsoft’s MIT-licensed Python red team framework. Targets, prompt converters, scorers, memory, and multi-turn attack strategies you compose yourself. Best when a canned scan is not enough and you need adaptive, campaign-style attacks.
• DeepTeam - Apache 2.0, from the founders of Confident AI and DeepEval. Vulnerabilities and attacks mapped to published frameworks. Best when the output has to survive a compliance conversation.

Deciding factor to pick

Match your deciding factor to the recommendation. This is the garak vs PyRIT vs DeepTeam call in one table:

Rule of thumb: garak answers “what is broken,” PyRIT answers “how far can an attacker push it,” and DeepTeam answers “how do I report it”.

What each tool is

• garak is NVIDIA’s open-source LLM vulnerability scanner, originally created by Leon Derczynski and now maintained in the NVIDIA/garak repository under Apache 2.0. It ships probe modules covering prompt injection, DAN-style jailbreaks, encoding and obfuscation bypasses, glitch tokens, adversarial suffixes, training-data leakage, toxicity, package hallucination, and cross-site scripting in generated output, each paired with detector plugins that grade the responses. It talks to Hugging Face, OpenAI, AWS Bedrock, Cohere, Groq, Replicate, NVIDIA NIM endpoints, local models via llama.cpp, and arbitrary REST endpoints, and writes JSONL reports plus a hit log. Version 0.15.0 in May 2026 added a multi-turn GOAT probe, an agent-breaker probe aimed at agent tooling, a system-prompt-extraction probe, a ModernBERT refusal detector, and NeMo Guardrails server support.
• PyRIT, the Python Risk Identification Tool, is Microsoft’s open-source red team framework, MIT licensed. The original Azure/PyRIT repository was archived in March 2026 and development now lives at microsoft/PyRIT. Its architecture is deliberately compositional: targets are the systems you attack, converters transform a prompt before it is sent, scorers grade what comes back, memory keeps conversation state, and attacks orchestrate the whole loop. It implements published multi-turn strategies including Crescendo, which escalates gradually from benign to adversarial across turns, and Tree of Attacks with Pruning, which uses an attacker model to explore and prune adversarial prompt paths.
• DeepTeam is an open-source red teaming framework from the founders of Confident AI, built on the same foundations as DeepEval and released under Apache 2.0. Its repository advertises 50+ ready-to-use vulnerabilities and 20+ research-backed attack methods spanning single-turn and multi-turn scenarios, with mappings to the OWASP Top 10 for LLMs, the OWASP Top 10 for Agents, NIST AI RMF, MITRE ATLAS, and safety datasets like BeaverTails and Aegis. You run it from a CLI with YAML configs or programmatically in Python, and it also ships an MCP server so you can drive it from an AI coding tool.

garak vs PyRIT vs DeepTeam: head-to-head

Counts published for each tool move between releases and differ across secondary write-ups, so treat the numbers above as directional and check the current repository before you quote them in a report.

Verdict by use case

You have a new model or endpoint and 30 minutes. garak. One command against your target with a sensible probe selection gives you a graded baseline before lunch. Nothing else in this space has a lower cost of first result.

You are a security team running a real red-team engagement. PyRIT. The converter and scorer abstractions are what turn a scan into an operation: stack a translation converter on top of a Base64 converter, drive Crescendo across ten turns, and score with an LLM judge tuned to your risk taxonomy. It is the only one of the three that feels like tooling built by people who run offensive campaigns for a living.

You need to hand a report to a regulator, auditor, or enterprise customer. DeepTeam. Its framework mappings mean a finding arrives pre-labelled against the OWASP Top 10 for LLMs or NIST AI RMF instead of needing manual translation. If your team is preparing for the EU AI Act, pair it with our EU AI Act adversarial testing checklist.

You already run DeepEval for evaluation. DeepTeam. Same authors, same mental model, and your evaluation and red-team results end up in one place.

You are testing an agent with tools, not a chatbot. Start with PyRIT for the campaign and garak’s agent-breaker probe for the tool surface, and accept that neither covers the full agentic failure surface. Tool poisoning, excessive agency, and unsafe multi-step trajectories need dedicated agent testing.

You want a CI regression gate. Any of the three, with a curated subset. Do not put a full sweep on the pull-request path.

Can you use them together?

Yes, and the layering is natural. Use garak as the broad sweep that runs on a schedule and tells you when something regresses across a wide surface. Use PyRIT for depth on the specific weaknesses garak surfaces, because a scanner tells you a jailbreak family works while an adaptive campaign tells you how far an attacker gets once they have a foothold. Use DeepTeam for reporting when the finding has to leave the engineering team.

The pattern that fails is running all three in full on every commit. You triple the token bill, triple the runtime, and get three overlapping sets of findings nobody triages. Pick one primary tool, add the second for a specific gap, and keep the third for the artefact you actually need.

Cost comparison

All three tools are free and open source with no paid tier for the tool itself. DeepTeam optionally connects to Confident AI’s commercial cloud for storing and sharing results, which is where its pricing lives.

Your real spend is inference, and it is easy to underestimate:

• garak runs fire a large volume of prompts at your target. Against a hosted commercial model, a broad probe selection can be a meaningful bill on its own.
• PyRIT spends on both ends. Attack strategies like TAP use an attacker model to generate and refine prompts, and LLM-as-judge scorers grade every response, so you pay for attacker tokens, target tokens, and judge tokens in the same run.
• DeepTeam simulates attacks and judges results with a model too, so the same three-way token spend applies.

The honest way to budget LLM red teaming tools is per run, not per year: count prompts fired, multiply by your target’s per-token price, and add the attacker and judge models on top. Probe and attack selection is your cost lever, and running a curated subset frequently beats running everything rarely.

Common pitfalls

• Treating a clean scan as proof of safety. These tools test known attack families. A novel jailbreak tuned to your system prompt and your data will not appear in any probe library until someone finds it.
• Testing the model instead of the system. Attacking a raw model endpoint tells you nothing about your retrieval layer, tool calls, or output rendering. Point the tool at the application, guardrails included.
• Ignoring the guardrail layer. If you run NeMo Guardrails or a validator stack in front of the model, test with it enabled, then again with it disabled, so you know what the rails are actually buying you. Our Guardrails AI vs NeMo Guardrails comparison covers that layer.
• Unpinned tool versions in CI. New probes and attack methods land regularly. If your pass rate moves after an upgrade, you will waste a day deciding whether your application regressed or the tool got better.
• No triage budget. Automated red teaming produces false positives at volume. Plan the human hours to sort real findings from detector noise, or the report goes unread.

Related reading

• OWASP LLM Top 10 testing checklist - the categories your red-team findings should map back to
• Guardrails AI vs NeMo Guardrails - the runtime defence layer you are attacking through
• EU AI Act adversarial testing checklist - what regulators expect adversarial testing to look like

Getting help

We help Series A-C AI companies turn a raw scanner run into an assurance story. A genai.qa Red-Team Sprint runs garak, PyRIT, and DeepTeam against your live system, triages the findings by hand, and hands you real attack success rates with a prioritised hardening plan. For agents with tool access, an Agentic AI Safety Assessment covers the failure modes probes miss.

Book a free scope call.