The garak vs PyRIT LLM red teaming question gets asked as a procurement decision, and that framing is wrong. These two tools sit at different points in the testing lifecycle: garak is a scanner that gives you broad, repeatable, single-command coverage against a model endpoint, and PyRIT is a red-team library for building attacks that adapt across turns and carry state. If you are choosing only one, choose based on where your risk actually lives: garak for model selection and CI regression gates, PyRIT for pre-release campaigns against a deployed application. Teams with budget for both should wire garak into the build and reserve PyRIT for the scenarios garak’s probe corpus cannot express.
What each tool actually is
garak is maintained by NVIDIA under Apache 2.0 and originated with Leon Derczynski. The accompanying paper describes four components: generators (the connection to the target), probes (the attack prompts), detectors (the classifiers deciding whether a probe landed), and buffs (transforms that perturb probes or model parameters), coordinated by a harness. The probe modules named in the repository README cover promptinject, dan, encoding, gcg, leakreplay, malwaregen, packagehallucination, snowball, xss, glitch, realtoxicityprompts and others, with generators for OpenAI, Hugging Face, AWS Bedrock, LiteLLM, Cohere, Groq, NVIDIA NIM, llama.cpp and generic REST endpoints. A run produces a JSONL report plus a hit log recording each individual failure with the probe name, the prompt, the model output and the trigger. Databricks’ engineering blog, published July 2025, put the corpus at “more than 150 different attacks and 3,000 prompts and prompt templates,” which is a third-party count worth treating as a snapshot rather than a current figure.
One operational note: the CLI flags moved. Older tutorials use --model_type and --probes, while the current README shows python3 -m garak --target_type huggingface --target_name gpt2 --spec probes.dan.Dan_11_0. Copy-pasted commands from 2024 write-ups will need updating.
PyRIT is Microsoft’s Python Risk Identification Tool, MIT licensed. The 2024 paper documents six components: memory, targets, converters, datasets, scorers and orchestrators. Converters are the piece with no real garak analogue, transforming a prompt into ASCII art, Atbash cipher, Leetspeak, a different tense, or a different modality, while memory retains both the original and the converted value so results stay interpretable. Orchestrators compose the rest into an attack, which the paper says can range from a single converter pass to “half a dozen generative AI models” iteratively refining a multi-turn attack. Shipped strategies named in the paper include PAIR, Tree of Attacks with Pruning, GCG, Crescendo, Skeleton Key, GPTFuzzer, persuasive adversarial prompts and many-shot jailbreaking. The current documentation index adds a scenario framework for standardized large-scale evaluations, a CoPyRIT GUI for human-led sessions, targets including OpenAI, Azure, Anthropic, Google, Hugging Face, raw HTTP or WebSocket endpoints and Playwright-driven web apps, and memory backed by SQLite or Azure SQL. Note the repository move: Azure/PyRIT was archived on 27 March 2026 and development continues at microsoft/PyRIT.
Where they diverge in practice
State is the real dividing line. garak’s model is prompt in, response out, detector verdict. PyRIT’s orchestrators hold conversation history, so an attack like Crescendo can escalate gradually across turns using an adversarial LLM to write each next message. garak does have an atkgen module that uses a red-team model to generate prompts dynamically, but it is narrower in scope than PyRIT’s orchestrator layer.
Cost follows from that. A garak scan costs one inference call per prompt against the target. A PyRIT multi-turn campaign runs an adversarial model and usually a scorer model alongside the target, so token spend per finding is materially higher and campaigns need budget planning.
Output shape differs too. garak’s JSONL report and hit log drop straight into a CI job that fails a build on a threshold. PyRIT’s memory store is richer but expects you to write the query and reporting layer yourself, which is the tax you pay for a library rather than a scanner.
Both inherit the same weak spot: detection. The Databricks write-up flags that “the presence of false positives often necessitates manual inspection of results,” and that a low attack success rate indicates a narrower accessible attack surface rather than immunity. Budget triage time on either tool’s output.
Residual risk neither tool closes
The garak paper is explicit about scope. LLM vulnerabilities are an open class, so garak “cannot offer comprehensive answers regarding model security” and is designed to be used as part of human assessment. Its probes are currently English only, which matters if you serve other languages, and it “does not deal with security issues presenting in a broader system context, such as code execution or insufficient access controls.” Microsoft is equally direct that PyRIT “is not a replacement for manual red teaming of generative AI systems,” positioning it as augmentation that automates the tedious parts. Microsoft’s Lessons From Red Teaming 100 Generative AI Products makes the same point across two of its eight lessons: automation extends coverage, and the human element remains crucial. Its third lesson, that AI red teaming is not safety benchmarking, is the one most often lost when a scan report becomes a compliance artifact.
Neither tool is a runtime control. Both are test-time instruments, and findings still need a filtering layer in front of production traffic. The defensive guardrail tooling tracked at guardml.io covers that layer, and OWASP’s LLM01:2025 guidance treats regular penetration testing as a validation step for trust boundaries, not as the boundary itself. For attack technique background that informs custom probes and orchestrators, offensive AI security research is the better starting point than either tool’s default corpus.
Who should pick which
Reach for garak if you are comparing candidate base models before selection, if you need a gate that runs unattended in CI on every model version bump, if your team is security-side rather than ML-side and wants results without writing Python, or if you need a defensible breadth artifact for an audit.
Reach for PyRIT if your risk is application-layer rather than model-layer, if you test agents with tool-calling or multimodal inputs, if your threat model includes conversational escalation such as Crescendo or Skeleton Key, or if you already have a red team that will write custom orchestrators and read raw results.
Run both if you ship a customer-facing GenAI product. The sequence that fits most pipelines is garak in CI for regression breadth, PyRIT quarterly or per major release for depth, and a manual pass on top of both. For a wider survey of the surrounding tooling, see the AI red teaming tools guide. For single-tool deep dives rather than this head-to-head, bestllmscanners covers garak as a scanner and PyRIT as a framework in the scanner niche this comparison defers to.
Related across the network
- Best AI Red Teaming Tools for ML Models in 2026 — adversarialml.dev
- Promptfoo Alternatives for LLM Red Teaming — jailbreakdb.com
- Building an Internal Adversarial Testing Pipeline for LLMs — aidefense.dev
- AI Red Team Engagement Methodology: Scoping to Reporting — aisec.blog
- PyRIT Review: Microsoft’s AI Red Teaming Framework — aisecreviews.com
