Red Team Your Own Chatbot in One Sprint: Garak, PyRIT, and Rivals Compared

You can red team your own LLM app in one sprint: pick one scanner for breadth and one orchestrator for depth, map findings to the OWASP LLM Top 10, block the attacks with guardrails, then re-scan and keep the before-and-after log as compliance evidence.

Look, attackers do not wait for your security roadmap. Prompt injection, jailbreaks, and data leakage are production incidents now, not conference talks. The good news: the best scanning tools are free, and one sprint produces evidence your auditors and buyers will actually accept.

Here is the field:

What is LLM red teaming, and why run it now?

LLM red teaming means attacking your own AI system on purpose, using adversarial prompts and multi-step exploits, to find failures like prompt injection, jailbreaks, and data leakage before real attackers or auditors do.

Three clocks make "now" the right answer:

• The EU AI Act wants testing evidence. High-risk obligations, including accuracy and robustness requirements, apply from 2 December 2027. A dated red-team log is exactly the kind of technical documentation that survives review. Start with our classification self-check to see if that clock applies to you.

• Enterprise buyers ask first. Security questionnaires now include "how do you test your AI?" A concrete answer with tool names and findings beats a paragraph of policy.

• The attack catalog is public. The OWASP LLM Top 10 and MITRE ATLAS give attackers and defenders the same playbook. If you have not run it against your app, assume someone else eventually will.

One honest limit up front: automated scanning has a ceiling. Scanners find known attack classes. Novel exploits, chained vulnerabilities, and business-logic abuse still need human red teamers. Automate the floor, then buy or staff the ceiling.

Which open-source tool fits your stack?

Match the tool to the target: Garak for breadth on models, PyRIT for depth on multi-turn agents, Giskard for RAG pipelines, DeepTeam for eval-adjacent engineering workflows, and Promptfoo for CI/CD-native testing.

• Garak is NVIDIA's vulnerability scanner with the largest probe library among open-source options, covering leakage, injection, toxicity, and hallucination across nearly every model format, from Hugging Face to REST APIs. Its next version, in development, adds agentic coverage.

• PyRIT came out of Microsoft's internal AI Red Team. Its composable orchestrators, converters, and scorers let you build multi-step attack chains that exist in no probe library. Pick it for agent loops and Azure-heavy stacks.

• Giskard shines where your risk lives in retrieval: RAG evaluation, plus bias and fairness probing that also covers tabular and vision models.

• DeepTeam sits naturally beside DeepEval if evaluation is already part of your pipeline, keeping tests and attacks in one workflow.

• Promptfoo is the CI/CD-native choice: a YAML config, a GitHub Action, and both evals and red teaming run on every release. One ownership fact to weigh: OpenAI announced its acquisition of Promptfoo on 9 March 2026, committing to keep the open-source project alive. More on what that means below.

Practical pairing for a first sprint: Garak for the wide sweep, PyRIT for the targeted follow-up. Both free.

When do you need a commercial platform?

You need a commercial platform when non-engineers must act on the results: open-source scanners produce raw findings, while platforms like Mindgard and HiddenLayer add continuous scheduling, structured reporting, and compliance mapping.

The gap is not attack quality. It is packaging. Reviewers of the category consistently note that open-source tools ship no validation layer and no enterprise reporting, so someone on your team becomes the report writer. That trade is fine for an engineering-led sprint. It stops being fine when audit season arrives:

• Mindgard runs continuous automated red teaming in CI/CD with findings mapped to the OWASP LLM Top 10, including multimodal coverage.

• HiddenLayer extends into model supply chain security, scanning model files themselves alongside runtime defense, for teams whose threat model includes poisoned artifacts.

Our directory data mirrors the market: of the 4 dedicated red-teaming tools we track, 3 are open source. Testing is cheap. Reporting is what you pay for.

How do you run the one-sprint red team?

Run five steps over two weeks: scope against the OWASP LLM Top 10, baseline-scan with Garak, build targeted multi-turn attacks in PyRIT, block confirmed findings with guardrails, then re-scan and archive the before-and-after log.

1. Day 1-2, scope and threat-model. List your app's entry points (user chat, retrieved documents, tool calls) and pick the OWASP LLM Top 10 categories that apply. Tag each to MITRE ATLAS techniques for the audit trail.

2. Day 3-5, baseline scan. Point Garak at your endpoint and run the full probe sweep. Export everything, including the passes. Evidence of what did not break matters too.

3. Day 6-8, targeted attacks. Take the top findings and build multi-turn exploit chains in PyRIT: the indirect injection through a retrieved document, the slow jailbreak across five turns.

4. Day 9-11, block and fix. Deploy runtime guardrails: Guardrails AI, NeMo Guardrails, Lakera Guard, or LLM Guard, and patch prompts and retrieval filters for what guardrails cannot catch.

5. Day 12-14, re-scan and archive. Re-run the exact same probe set. The delta between scan one and scan two, dated and signed, is your adversarial-defense evidence for buyers, for ISO 42001 monitoring records, and for future AI Act documentation. File it with your AI inventory from the Shadow AI playbook.

Does OpenAI owning Promptfoo change your choice?

For most teams, no: the open-source project continues and the tooling still works. For teams auditing OpenAI models specifically, vendor independence is now a fair question to raise.

Here is the analytical read, labeled as our view. Promptfoo's credibility rested partly on neutrality: one tool testing every provider's models. Under OpenAI ownership, using it to audit OpenAI systems means the auditing tool and the audited vendor share an owner. OpenAI has pledged continued open-source development, and more than 350,000 developers have used the project, so abandonment looks unlikely. But regulated teams with strict independence requirements may prefer Garak or PyRIT for provider-neutral evidence, or add a second tool for cross-validation. The deal also confirms the category's direction: red teaming is becoming a built-in platform feature, not an add-on. Expect the other labs to follow.

Your Action Plan

1. Book the sprint. Two weeks, one engineer, zero tooling budget. The plan above is the ticket.

2. Start the wide sweep today. Garak against your staging endpoint takes an afternoon to set up.

3. Pick your orchestrator by stack. PyRIT for agents and Azure, Giskard for RAG-heavy apps, DeepTeam if DeepEval already runs in your pipeline.

4. Put guardrails in before findings pile up. An open-source guard deployed mid-sprint turns findings into fixes in the same log.

5. Archive the before-and-after. Date it, sign it, store it with your AI inventory. That single artifact answers buyers, auditors, and the December 2027 documentation clock at once.

Next milestone to watch: Garak's agentic-AI release and the integration of Promptfoo into OpenAI's enterprise platform will both reshape the open-source field this year. Browse all 4 red-teaming tools, and the guardrails to pair with them, in the directory.

FAQs

What is the best free tool to red team an LLM?

Garak (NVIDIA) for the widest sweep of known vulnerabilities, and PyRIT (Microsoft) for building targeted multi-turn attacks. Most teams get the best first-sprint results running both: Garak for breadth, PyRIT for depth.

Is automated LLM red teaming enough for compliance?

No, but it is the right floor. Automated scans produce dated, repeatable evidence for ISO 42001 monitoring and future EU AI Act documentation, yet novel exploits and business-logic flaws still require human red teamers. Combine both.

Who owns Promptfoo now?

OpenAI announced its acquisition of Promptfoo on 9 March 2026 and plans to integrate it into its enterprise agent platform. OpenAI has committed to continuing the open-source project.