The market for open source LLM red teaming tools matured fast. In early 2024 you had a handful of academic proof-of-concepts and Microsoft’s freshly released PyRIT. By mid-2026 there are at least six production-viable frameworks covering everything from single-turn jailbreak probing to multi-turn agentic exploitation chains. The problem is no longer finding tools — it’s knowing which one addresses which class of risk and how to combine them without duplicating work.
This guide focuses on the tools security teams actually deploy, maps each to the threat categories in the OWASP Top 10 for Large Language Model Applications, and gives you enough operational detail to make a stack decision.
The Four Core Tools and What They Actually Cover
Garak (NVIDIA)
Garak is the closest thing the LLM security space has to Nessus. Install with pip install -U garak, point it at an endpoint, and it runs a structured battery of probes across 20+ vulnerability categories: prompt injection, jailbreaks, data leakage, hallucination, encoding-based bypasses, toxicity generation, and malware-adjacent outputs. The architecture decouples probes from detectors, so domain-specific probes are straightforward to add. Results log as JSONL.
What Garak does well is breadth and speed. For CI/CD integration, you select a probe subset, bring scan time from hours to minutes, and get repeatable JSONL output you can diff between releases. It supports OpenAI, Hugging Face, AWS Bedrock, Replicate, Cohere, Groq, GGML/llama.cpp, and custom REST targets.
What Garak does not centre on is multi-turn attack chaining. Most probes are a single exchange, with the exception of the multi-turn GOAT probe added in v0.15.0. That shape matters for agentic applications where the dangerous path requires building context across many turns. Our garak vulnerability scanner walkthrough covers the probe and detector architecture in depth.
PyRIT (Microsoft)
PyRIT fills that gap. Microsoft designed it specifically for multi-turn orchestration — what the team calls “crescendo attacks” where an adversarial conversation gradually shifts model behavior across several exchanges. PyRIT handles text, image, audio, and video modalities, and its converter layer lets you layer prompt obfuscation on top of any attack strategy: encoding, translation, paraphrasing, persona injection.
The tradeoff is operational overhead. PyRIT is a framework, not a CLI scanner. You write Python to define attack chains, scorers, and memory state. That flexibility is essential for testing autonomous agents with tool access, but it’s the wrong starting point if you just want a quick pre-deploy scan of a chat endpoint. Use Garak for breadth, PyRIT when depth and multi-turn coverage matter. Our PyRIT framework explainer walks through the attacks, converters, and scorers that make those multi-turn campaigns work.
HarmBench (Center for AI Safety)
HarmBench approaches the problem from a different angle: standardized benchmarking. Rather than running live attacks against a production endpoint, it evaluates red teaming methods against a curated set of 510 harmful behaviors spanning cybercrime, bioweapons, disinformation, and harassment — and separately evaluates LLM robustness against those methods. The paper tested 33 models and 18 attack strategies.
The practical value for security teams is reproducibility. HarmBench gives you a shared baseline so you can compare model versions, fine-tuned variants, or the effect of adding a guardrail layer, against an established reference point. It is not a live scanner but the reference benchmark your scanner output should eventually map to.
TextAttack (QData Lab, University of Virginia)
TextAttack targets adversarial robustness at the NLP layer — character, word, sentence, and semantic-level perturbations. It ships about 20 attack recipes from the literature (TextFooler, DeepWordBug, BAE, PWWS and others), aimed at BERT-class models and standard classification tasks. For teams working on fine-tuned classifiers (content moderation, intent classification, PII detectors used as guardrail components), TextAttack provides adversarial training datasets that Garak and PyRIT don’t generate.
Building a Pipeline That Actually Holds
The mistake most teams make is treating red teaming as a one-time audit before launch. The threat surface for LLMs shifts continuously: new jailbreak techniques appear weekly, the 2025 OWASP list added System Prompt Leakage and Vector and Embedding Weaknesses as distinct categories, and any model update can regress safety behavior that was previously stable.
A sustainable pipeline looks like this:
Stage 1 — Pre-deploy gate (CI/CD). Run a Garak probe subset on every candidate model version. Select probes that cover your actual exposure surface: if you’re not serving audio, skip audio injection probes; if you’re building an agent with tool access, include the plugin and agentic abuse probes. Set a failure threshold — for example, flag any build where jailbreak success rate exceeds 2% on a defined probe set. For a side-by-side of how garak, PyRIT, and Promptfoo each fit this CI stage, see Automated LLM Red-Teaming in CI: garak vs PyRIT vs Promptfoo.
Stage 2 — Depth testing (release candidates). On release candidates, run PyRIT multi-turn scenarios against your specific system prompt and tool configuration. Agentic applications that execute code, query databases, or browse the web need attack chains that simulate goal hijacking across multiple turns, not single-shot payloads.
Stage 3 — Regression benchmarking. After each significant model update or guardrail change, run the relevant HarmBench subset to track whether refusal behavior improved or regressed versus the prior release. This gives you a number to put in a security review, not just qualitative notes.
Stage 4 — Runtime complement. Red teaming tools are pre-deployment instruments. They do not replace runtime guardrails. For the runtime layer (input classification, output filtering, PII redaction), put a maintained guardrail in the request path: Choosing an LLM Guardrail compares Llama Guard, NeMo Guardrails and Guardrails AI, and guardml.io tracks the wider defensive guardrail tooling, including NeMo Guardrails, AWS Bedrock Guardrails and LLM Guard (archived July 2026; reference only).
For teams tracking the evolving attack taxonomy that informs which probes matter, aisec.blog maintains a running breakdown of prompt injection variants and jailbreak technique categories mapped to OWASP LLM01 and related entries.
Emerging Tools Worth Watching
Two tools that have gained traction in 2025-2026:
DeepTeam (Confident AI) — a penetration testing framework designed explicitly for LLM applications. It maps attack types to OWASP LLM Top 10, NIST AI 600-1, and MITRE frameworks, and supports multi-turn exploitation workflows similar to PyRIT but with tighter out-of-the-box coverage of agent-specific risks.
OpenRT — a multimodal red teaming toolkit from AI45Lab covering 42+ attack methods against vision-language models. Relevant if your deployment includes image or document inputs alongside text, where cross-modal injection is an emerging attack vector that single-modality tools miss entirely.
The field moves fast enough that any tool list is partially stale on publication. The more durable principle: match the attack modality to your deployment surface, automate the baseline in CI/CD, and reserve manual PyRIT-style depth testing for the highest-risk agent configurations. For how these open-source tools sit alongside the commercial options and runtime guards in a complete stack, see the best LLM vulnerability scanners for 2026.
Frameworks are one layer of a larger stack, and the layers do not substitute for each other: probe suites, injection detectors, guardrail policy layers, gateways and model-artefact scanners each catch something the others structurally cannot. Where each belongs is mapped in LLM Security Tools: The 2026 Category Map. To turn your own deployment, threat model, integration point and budget into a ranked shortlist, run the interactive scanner picker.
Related across the network
- PyRIT Review: Microsoft’s AI Red Teaming Framework — aisecreviews.com
