The ai red teaming tools for llm safety worth shortlisting in 2026 are Promptfoo for CI-gated application testing, NVIDIA’s garak for model-level scans, Microsoft’s PyRIT for custom multi-turn campaigns, and DeepTeam for a quick OWASP-mapped baseline. For most teams, the recommendation is to run Promptfoo on every pull request and garak on every model change, then add PyRIT once a dedicated red team exists.
Two managed platforms complete the shortlist: Microsoft’s Foundry AI Red Teaming Agent, which wraps PyRIT, and Palo Alto Networks’ Prisma AIRS AI Red Teaming. Buy one for managed reporting or stack fit, not attack variety. None of these tools certifies a system as safe. Microsoft’s AI red team, writing up lessons from red teaming 100 generative AI products, states that “red teaming can’t be automated entirely” and that mitigations “do not eliminate risk entirely.”
What each tool does
The six tools fall into three groups: a framework you build on (PyRIT), scanners you point at a target (garak, Prisma AIRS, Foundry’s agent), and test suites you check into a repository (Promptfoo, DeepTeam).
Promptfoo (MIT) generates attacks with plugins, which its quickstart calls “adversarial generators,” then wraps them in strategies ranging from Base64 encoding to the multi-turn crescendo, goat and jailbreak:hydra attackers. Configs are declarative YAML, so a finding becomes a regression test in the same commit. An owasp:llm shorthand enables plugins mapped to the OWASP Top 10 for LLM Applications 2025, though LLM03 Supply Chain and LLM05 Improper Output Handling get no dedicated plugin. Two procurement facts matter. Attack generation uses a separate provider that defaults to OpenAI, and Promptfoo agreed to be acquired by OpenAI on 9 March 2026. The company says it “will remain open source,” and the repository still carries an MIT license.
garak (Apache-2.0, NVIDIA) is the model-level scanner. Its architecture separates probes that generate interactions, detectors that decide whether the model failed, generators that wrap the target, and harnesses that pair probes with detectors. The repository ships more than 40 probe modules, from dan and encoding jailbreaks to latentinjection, leakreplay, packagehallucination and sysprompt_extraction. Targets include Hugging Face Hub, AWS Bedrock, NIM, LiteLLM and, per the README, “pretty much anything accessible via REST.” The garak paper explains the design. It tests what the model will do, not what your application wraps around it.
PyRIT (MIT, Microsoft AI Red Team) is a framework, not a turnkey scan. You compose targets, converters, scorers and attack logic in Python, which suits campaigns no preset covers. Check your pins: the old Azure/PyRIT repository was archived on 27 March 2026 and the project now lives at microsoft/PyRIT. Release v1.1.0, published 4 September 2026, added Best-of-N, split-payload, multilingual and FigStep attacks, 10 new converters, ShieldGemma and system-prompt-extraction scorers, and 488 in-the-wild jailbreak templates from TrustAIRLab. It also made scores possibly undetermined, a breaking change for pipelines that read score values without checking. The PyRIT paper describes the composable design.
DeepTeam (Apache 2.0, Confident AI) advertises “50+ ready-to-use vulnerabilities” and “20+ research-backed adversarial attack methods”. Single-turn attacks include prompt injection, roleplay and encoding obfuscation; multi-turn ones include linear and tree jailbreaking, Crescendo and Bad Likert Judge. Built-in framework support covers the OWASP Top 10 for LLMs 2025, NIST AI RMF and MITRE ATLAS, which makes its output easy to hand to a risk committee. It also ships seven binary-classification guardrails for inputs and outputs; evaluate those separately from the red-teaming engine.
Microsoft Foundry AI Red Teaming Agent combines PyRIT attack strategies with Foundry’s risk and safety evaluators and reports Attack Success Rate (ASR). Per Microsoft’s documentation, only text scenarios are supported. Agent-specific categories (prohibited actions, sensitive data leakage, task adherence) run only in cloud red teaming, available in five regions, and agent support covers Foundry-hosted prompt and container agents with Azure tool calls. Function tool calls and non-Foundry agents are listed as unsupported.
Prisma AIRS AI Red Teaming is Palo Alto Networks’ API-driven scanner. Per its API documentation, you register a target, submit a scan against the model endpoint and retrieve an asynchronous report that draws on a built-in library of hundreds of vulnerabilities, plus custom prompt sets and attack objectives tailored to sectors such as finance and healthcare. Onboarding runs through Strata Cloud Manager and a Customer Support Portal deployment profile.
| Tool | License / owner | Primary target | Multi-turn attacks | Pipeline fit | Runs where |
|---|---|---|---|---|---|
| Promptfoo | MIT / OpenAI | Prompts, RAG, agents | Yes (crescendo, goat, hydra) | Native CLI and CI, YAML configs | Local; attacker defaults to OpenAI |
| garak | Apache-2.0 / NVIDIA | Model and endpoint | Iterative probes (tap, goat) | CLI scan; gating is yours | Local, any REST target |
| PyRIT | MIT / Microsoft | Whatever you code a target for | Yes (incl. Crescendo) | Python code; most effort | Local |
| DeepTeam | Apache 2.0 / Confident AI | LLM apps, RAG, agents | Yes (Crescendo, tree, Bad Likert Judge) | Python; framework-labelled reports | Local |
| Foundry AI Red Teaming Agent | Managed / Microsoft | Models; Foundry agents only | Yes (multi-turn, Crescendo) | Foundry portal and SDK | Local or Azure cloud |
| Prisma AIRS AI Red Teaming | Commercial / Palo Alto | Model and application endpoints | Not detailed in public API docs | REST API, async scans | SaaS |
Where do red teaming tools sit in the pipeline?
Red teaming tools sit in the build and pre-deployment path, not the request path. They attack a staging copy of the system and feed findings into the runtime controls that do sit on the request path: the LLM firewall, jailbreak detector and output validator. A typical placement:
Pull request → static attack suite against staging → merge gate → nightly multi-turn scan → release → production path: client → LLM firewall → model → output validator → client
Out of band, run garak whenever the base model or system prompt changes, and a PyRIT or human-led campaign before major releases. For agents, Microsoft recommends a “purple environment”: non-production, with production-like resources.
Run each scan twice, once against the bare model and once with guardrails enabled. The difference shows what the guardrail contributes; the residual failures show what your jailbreak detection and prompt injection detection tools still miss. For retrieval-augmented apps, plant poisoned documents in a test corpus and confirm the controls in content moderation for RAG applications catch them.
The OWASP GenAI Red Teaming Guide splits the work into model evaluation, implementation testing, infrastructure assessment and runtime behavior analysis. These tools concentrate on the first two; infrastructure still needs a conventional penetration test.
Trade-offs
Scoring error. Dynamic attacks are mostly graded by an LLM judge, and judges are classifiers with their own error rates. Microsoft says its ASR scoring uses generative models, can be non-deterministic and may produce false positives, so results need review before anyone acts on them. The judge-model limits covered in the Llama Guard benchmark review apply here.
Cost and run time. Static strategies (Base64, ROT13, leetspeak, homoglyphs) are deterministic and need no attacker model. Dynamic and multi-turn strategies call an attacker, your target and a grader on each attempt, and Promptfoo’s strategy documentation rates crescendo, goat and hydra as high cost. No public source gives a portable per-scan figure, so estimate from your own token prices and keep multi-turn scans in scheduled jobs.
Integration effort. Promptfoo and DeepTeam are the lightest lift. garak is a CLI scanner, so turning its report into a merge gate is your job. PyRIT needs engineers who will own Python attack code. Foundry is low effort for Azure teams, but its agent testing covers Foundry-hosted agents only. Prisma AIRS requires Strata Cloud Manager onboarding first.
Data handling. Adversarial prompts and responses are sensitive artifacts. Reconfigure Promptfoo’s OpenAI-default attacker if policy forbids sending test data to a third party. Foundry redacts adversarial inputs from cloud results. The open-source tools can run on your infrastructure when attacker and judge point at self-hosted models.
Agent coverage. Every tool here is weaker on agents than on chat. Foundry’s sensitive-data-leakage test is single-turn and English-only. OWASP’s vendor evaluation criteria for AI red teaming, published February 2026, separates chatbots and RAG from “tool-calling agents, MCP architectures, and multi-agent workflows.” Hold any vendor’s agent-coverage claim to that split.
Ownership churn. Promptfoo changed owners and PyRIT changed repositories in 2026. Pin versions and re-read licenses at each upgrade.
Who should pick which tool
Pick Promptfoo if you ship prompt or model changes weekly and want regressions to block merges.
Pick garak if you are selecting, fine-tuning or self-hosting a base model and need a model-level baseline before application testing.
Pick PyRIT if a dedicated AI red team writes custom attack chains, converters or scorers and accepts that nothing is turnkey.
Pick DeepTeam or a managed platform if you need reports labelled against OWASP, NIST AI RMF and MITRE ATLAS (DeepTeam), your agents are Foundry-hosted (Microsoft), or your runtime security already lives in Strata Cloud Manager (Prisma AIRS).
Who should not
- Teams wanting a compliance certificate. A clean scan means the attacks you ran failed, not that the system is safe.
- Teams testing non-Foundry agents or function tool calls; Foundry lists both as unsupported.
- Teams with no one to own Python attack code should skip PyRIT for Promptfoo or DeepTeam.
- Organizations whose policy bars OpenAI-owned tooling should send Promptfoo through procurement review first.
FAQ
what is the best free tool for llm red teaming
Promptfoo is the best free starting point for most application teams because it is MIT-licensed, runs from a CLI, and turns findings into YAML regression tests. If you are evaluating the model itself rather than your application, start with NVIDIA’s garak, which is Apache-2.0 and scans the model endpoint directly.
what is attack success rate in ai red teaming
Attack success rate (ASR) is the share of attack attempts that produced an undesired response, which Microsoft defines as successful attacks divided by total attacks. It is only meaningful against a fixed seed set, strategy mix and judge model, so track it over time within one tool rather than comparing ASR across vendors.
does ai red teaming replace llm guardrails
No: red teaming tools measure how a system fails before release, while guardrails such as LLM firewalls, jailbreak detectors and output validators enforce policy on live traffic. Use red-team findings to tune guardrail thresholds and add regression tests, then rerun the scan with guardrails enabled to confirm the fix holds.
how often should you red team an llm application
Run cheap static attack suites on every change to prompts, models, retrieval sources or tools, multi-turn scans nightly or weekly, and a human-led campaign before major releases. Microsoft’s AI red team describes red teaming as a continuous process, and a new base model or tool integration resets most of what earlier scans proved.
Related across the network
- Best LLM Vulnerability Scanners 2026: Tools Compared — bestllmscanners.com
- LLM Security Testing Platform: Buyer’s Guide for 2026 — jailbreakdb.com
