AI Red Teaming Tools: PyRIT vs Garak vs Promptfoo (2026)

Choosing the right AI red teaming tools is one of the more consequential decisions a security team makes when hardening an LLM-based system. The market in 2026 offers four credible open-source frameworks, several commercial platforms, and a growing number of managed service providers, each making legitimate claims about what they test. This guide compares them without vendor bias, based on what they actually cover, and gives you a decision framework based on your team's size, engineering capacity, and compliance requirements.

We run AI red team engagements for a living. In practice, the tool selection comes down to five questions: which attack surface are you testing, what is your engineering capacity, do you need compliance reporting, how often does testing need to run, and how much agentic or RAG coverage matters to you.

What to Look For: Five Coverage Layers

Before comparing tools, you need a framework for what "coverage" means. A production LLM deployment has at least five distinct attack surface layers, and most tools address only some of them.

Layer 1: Input and output. Classic prompt injection, jailbreaks, toxicity generation, PII exfiltration, and system prompt extraction tested against what the model receives and returns. All four open-source tools cover this layer.

Layer 2: Retrieval. For RAG systems, the attack surface includes document poisoning, retrieval manipulation, and indirect prompt injection embedded in retrieved content. This layer is partially addressed by Promptfoo and Garak, but comprehensive RAG attack coverage requires custom configuration or specialized tooling.

Layer 3: Agentic. For agent pipelines that invoke tools, APIs, or other agents, the relevant attacks include tool call hijacking, unauthorized action escalation across permission boundaries, memory poisoning, and multi-agent trust boundary violations. This layer has the weakest open-source coverage in 2026.

Layer 4: Model. Adversarial inputs that attack the model artifact itself: extraction attacks, fine-tuning backdoors, embedding space manipulation, and deserialization exploits in serialized model formats. This is Garak's home territory at the probe level and the core focus of HiddenLayer at the artifact level.

Layer 5: Infrastructure. Token flooding, cost amplification via max-token exploitation, API key sprawl, and supply chain risks in model serving pipelines. Almost no red teaming tool covers this layer; it requires separate infrastructure security review alongside your AI red team engagement.

Keep this map in mind as you read the tool comparisons. No single tool covers all five layers. The right answer for most teams is a combination, not a replacement.

Open-Source AI Red Teaming Tools: A Deep Dive

Promptfoo

Promptfoo is the most accessible open-source AI red teaming tool for engineering teams. It runs from a YAML config file, integrates natively with GitHub Actions, GitLab CI, and Jenkins, and combines red teaming with general-purpose LLM evaluation in a single CLI workflow. With 18,000+ GitHub stars and an MIT license, it has the largest community of the four open-source tools.

Coverage focuses on Layers 1 and 2: prompt injection, jailbreaks, PII leakage, system prompt extraction, and application-layer RAG attacks. Critically, Promptfoo maps findings to the OWASP Top 10 for LLM Applications, NIST AI RMF, MITRE ATLAS, and the EU AI Act, producing reports that non-technical stakeholders and auditors can read. That compliance mapping is rare in open-source tooling and one of the main reasons Promptfoo became dominant in the engineering-team segment.

In March 2026, OpenAI acquired Promptfoo for approximately $86 million, a clear signal that application-layer LLM security testing has become infrastructure-grade. OpenAI confirmed the MIT license remains in effect, and the project continues to accept community contributions.

Best for: Engineering teams who want security testing embedded in their CI/CD pipeline with low configuration overhead and OWASP-mapped reporting.

Gaps: Limited agentic attack coverage out of the box. Multi-agent pipeline testing requires custom plugin configuration. Weaker on model-level and infrastructure attacks.

PyRIT

PyRIT (Python Risk Identification Tool) is Microsoft's open-source red teaming framework, MIT-licensed and battle-tested across more than 100 internal Microsoft products, including Copilot. It has 3,800+ GitHub stars, 129 contributors, and a published academic paper. The PyRIT documentation is extensive and actively maintained.

The critical distinction from other tools: PyRIT is an orchestration framework, not a scanner. You write Python scripts that define attack campaigns using three composable primitives: orchestrators (which direct the attack flow), converters (which transform prompts), and scorers (which evaluate model responses). This gives security researchers precise control over attack logic at the cost of higher engineering overhead compared to YAML-driven tools.

PyRIT's multi-turn capabilities are its strongest differentiator. The Crescendo attack gradually guides a model toward generating harmful content through a sequence of small, seemingly innocuous steps, using an attacker LLM to manage the escalation dynamically. Microsoft researchers published this technique in 2024 and demonstrated it bypasses safety alignment in GPT-4 and Gemini Pro. The Tree of Attacks with Pruning (TAP) technique explores multiple attack branches in parallel, pruning dead ends based on scorer feedback. The original TAP paper reports over 80 percent success against GPT-4 Turbo and GPT-4o.

PyRIT also ships more than 70 prompt converters: Base64, ROT13, Leetspeak, Unicode confusables, LLM-based rephrasing, translation, and multimodal injection. Converters stack, so you can translate a prompt, then Base64-encode it, then embed it in an image. For teams testing models that have been hardened against common jailbreaks, the converter pipeline is a practical way to test the depth of that hardening.

Best for: Security researchers and red team professionals who need full programmatic control over attack logic, especially for multi-turn and custom agentic attack scenarios.

Gaps: No built-in dashboard or compliance reporting. Requires Python proficiency. Not CI/CD-native without custom wrapping.

Garak

Garak is NVIDIA's open-source LLM vulnerability scanner, designed to test model-level weaknesses rather than application-layer behavior. Its modular architecture uses probes (which generate test inputs), detectors (which analyze model outputs), evaluators (which compile results), and buffs (which modify probe behavior).

With 120+ probe modules across categories including prompt injection, DAN jailbreaks, encoding bypasses, data leakage, package hallucination, malware generation, toxicity, and XSS, Garak provides the broadest automated coverage of base model vulnerabilities in any open-source tool. It supports most major model providers: OpenAI, Hugging Face, AWS Bedrock, Azure OpenAI, NVIDIA NIM, Cohere, Groq, and any REST-accessible endpoint. You can run a full scan against a model endpoint with a single CLI command, which makes it practical for DevSecOps teams with no red teaming background.

Garak's gap is the application layer. It tests model endpoints, not full application stacks. RAG coverage is limited to a subset of indirect prompt injection probes. Agentic attack coverage exists but is early-stage. If your question is "is this base model safe to deploy," Garak answers that well. If your question is "is this deployed application exploitable," you need Promptfoo or PyRIT alongside it.

Best for: Baseline model safety scanning before deployment, comparing models during evaluation, and scheduled nightly regression tests when foundation models are updated.

Gaps: Not designed to test full application stacks, complex RAG pipelines, or multi-agent workflows. CLI-first with limited reporting output.

DeepTeam

DeepTeam by Confident AI is the newest of the four open-source tools and the lowest-friction entry point for teams new to AI red teaming. Install via pip, point it at an LLM endpoint, and run scans covering 40+ vulnerability types drawn directly from the OWASP LLM Top 10 and the OWASP Top 10 for Agentic Applications.

The tool's explicit OWASP alignment is its main value proposition. Every vulnerability type maps to a specific OWASP code, and DeepTeam supports NIST AI RMF alignment as well. For teams producing compliance evidence, having findings that directly reference OWASP categories saves the manual mapping step that Garak and PyRIT require.

DeepTeam runs scans asynchronously, which improves throughput against high-latency endpoints. It works against any LLM endpoint with an API, and supports 10+ adversarial attack methods including multi-turn attacks, persona simulation, and jailbreak injection. The project has 1,277 GitHub stars and is Apache 2.0 licensed.

The trade-off for simplicity is depth. DeepTeam's probe library is smaller than Garak's, its multi-turn orchestration is less sophisticated than PyRIT's, and its CI/CD integration is less mature than Promptfoo's. It does not yet have native multimodal attack support. But for a team that needs OWASP-mapped baseline coverage in under an hour of setup, it is currently the fastest path.

Best for: Teams that need quick OWASP LLM Top 10-mapped baseline coverage with minimal configuration, particularly for compliance-driven use cases.

Gaps: Smaller probe library than Garak. Simpler multi-turn orchestration than PyRIT. Less mature CI/CD integration than Promptfoo.

Commercial AI Red Teaming Platforms

Mindgard

Mindgard positions itself as a continuous AI red teaming platform rather than a point-in-time scanner. It runs automated adversarial campaigns against LLMs, NLP models, and multi-modal systems on a schedule, with OWASP, NIST AI RMF, MITRE ATLAS, and EU AI Act reporting built into the dashboard. Mindgard was named in the 2026 Gartner Emerging Tech report for AI trust, risk, and security management.

The platform's strength is the combination of breadth and reporting quality. Automated reconnaissance, adversarial testing, and chained attack scenarios run against diverse model types, with output that compliance auditors and executive stakeholders can read without translation. For enterprises running large AI estates with diverse model deployments, that management layer has real value.

The coverage gap to know: Mindgard's testing model was built around single-model evaluation. It does not natively test multi-agent pipelines, MCP tool poisoning, or agentic orchestration frameworks like LangGraph, CrewAI, or AutoGen. For teams deploying complex agent architectures in 2026, that gap matters and will require supplemental tooling or a managed engagement.

HiddenLayer

HiddenLayer focuses on the model artifact layer: protecting deployed models from adversarial threats at the serialized model level. Its ModelScanner checks more than 35 model formats for deserialization attacks, architectural backdoors, and malicious code injections, filling a gap that application-level red teaming tools ignore entirely.

This is a distinct and complementary capability. Most open-source red teaming tools test what goes into a model and what comes out. HiddenLayer tests the model file itself, which matters for organizations that consume models from third-party sources, use open-weight models from Hugging Face, or maintain model registries that could be compromised through supply chain attacks. Following the Check Point acquisition of Lakera in November 2025, HiddenLayer has sharpened its positioning around model integrity and supply chain security as a distinct category from prompt-level red teaming.

Lakera (now Check Point AI Security)

Lakera built its reputation on Gandalf, the widely-used prompt injection challenge, and on guardrail tooling for detecting prompt injection and sensitive data exposure at the application layer. In November 2025, Check Point Software Technologies acquired Lakera for $300 million and integrated it as the foundation of Check Point's AI Security product line.

Lakera today is best understood as an enterprise guardrail product within the Check Point ecosystem rather than a standalone red teaming platform. Teams already embedded in the Check Point environment gain AI security features through existing relationships. Teams evaluating standalone AI red teaming tooling should evaluate Mindgard, HiddenLayer, or the open-source options.

Novee AI

Novee AI is an emerging autonomous red teaming platform that entered the market in early 2026, focused specifically on agent-native attack scenarios: testing multi-agent pipelines, tool-call hijacking, and memory poisoning at the orchestration layer. It is one of the few commercial platforms with explicit coverage of MCP tool poisoning and multi-agent trust boundary violations.

Compared with Mindgard, Novee's coverage of agentic attack surfaces is deeper. Compared with HiddenLayer, its model-artifact coverage is thinner. It has less maturity in compliance reporting than Mindgard. For enterprises deploying complex multi-agent systems and finding that other platforms leave agentic attack surfaces uncovered, Novee is worth evaluating as a supplement.

AI Red Teaming Tool Comparison Matrix

| Capability | PyRIT | Garak | Promptfoo | DeepTeam | Mindgard | HiddenLayer | |---|---|---|---|---|---|---| | Prompt injection / jailbreaks | Yes | Yes | Yes | Yes | Yes | Partial | | RAG / retrieval attacks | Partial (custom) | Partial | Yes | Partial | Yes | No | | Agentic pipeline attacks | Yes (custom) | Limited | Limited | Partial | No | No | | Model artifact / supply chain | No | Partial | No | No | Partial | Yes | | CI/CD integration | Manual | Manual | Native | Manual | API | API | | OWASP / NIST reporting | No | No | Yes | Yes | Yes | No | | Multi-turn attacks | Yes (Crescendo / TAP) | Limited | Limited | Partial | Yes | No | | License / cost | MIT / Free | Apache 2.0 / Free | MIT / Free | Apache 2.0 / Free | Commercial | Commercial | | Engineering overhead to operate | High | Low | Low to medium | Low | Low | Low |

Build vs. Buy: A Realistic Cost Analysis

The license cost is not the deciding variable. The engineering capacity required to operate and maintain the stack is.

An open-source red teaming stack built on Promptfoo or PyRIT requires a security engineer to configure attack campaigns, update probe libraries as new techniques emerge, generate and format compliance reports, and triage findings. A senior security engineer in 2026 costs $150,000 to $200,000 per year in fully-loaded salary and benefits. If 30 to 40 percent of their time goes to red teaming infrastructure, the true annual cost is $45,000 to $80,000 in personnel alone, before accounting for compute costs for attacker LLM usage in techniques like Crescendo and TAP, or the time cost of building custom agentic test scenarios from scratch.

Commercial platforms that replace or reduce that engineering overhead start at $50,000 to $150,000 annually for most mid-market buyers. The decision point is not "free vs. paid" but "which approach delivers the coverage I need at the speed and compliance posture my business requires."

A practical framework by company stage:

Early-stage startups without regulated data: Start with Promptfoo in CI/CD and run Garak against any foundation models you deploy. Set a bi-weekly schedule for Garak scans and integrate Promptfoo into your pull request workflow. Cost: engineering time only.

Growth-stage companies facing PCI DSS, HIPAA, or SOC 2 audits: Add DeepTeam for OWASP LLM Top 10-mapped compliance evidence. Consider a point-in-time managed AI penetration test before your audit to cover agentic and RAG attack surfaces that automated tools miss. Cost: low tooling overhead plus a one-time engagement fee.

Mid-market and enterprise: A commercial platform for continuous automated monitoring plus a managed AI penetration test annually. The compliance reporting quality and agentic coverage justify the platform spend. Budget for supplemental tooling if you run complex multi-agent pipelines.

One thing to check before choosing build: the hidden cost of attacker LLM usage. Multi-turn techniques like Crescendo and TAP require an attacker LLM to orchestrate the attack dynamically. At scale, that API cost adds up fast. Commercial platforms absorb this infrastructure cost in their pricing. Self-managed stacks carry it directly.

How BeyondScale Combines These Tools in Engagements

No single tool covers the full attack surface of a production AI system. In our AI penetration testing engagements, we select and combine tools based on the target architecture.

For standard LLM chat or generation features, we integrate Promptfoo into the engagement workflow during the initial scan phase, using its application-layer attack library as a baseline. We then layer in custom PyRIT orchestrators for multi-turn scenarios, specifically Crescendo and TAP, to find vulnerabilities that single-shot scanners miss. In practice, multi-turn attacks routinely surface issues that pass automated single-shot scans, particularly in models that have been fine-tuned with safety alignment.

For systems with RAG pipelines, we extend coverage to the retrieval layer: testing document poisoning, retrieval manipulation, and indirect prompt injection embedded in retrieved documents. These attack scenarios are not fully covered by any current open-source tool out of the box and require custom test development aligned to the specific retrieval architecture.

For agentic systems using multi-agent orchestration, tool-call integration, or MCP servers, open-source tooling provides partial coverage at best. We run custom attack suites that test tool-call hijacking, permission escalation across agent boundaries, memory poisoning, and multi-agent trust violations. These scenarios require custom orchestration that goes beyond the current capabilities of Promptfoo, DeepTeam, or Garak.

Our AI red teaming methodology guide covers the full attack surface framework and explains how we scope engagements. For teams building a continuous red teaming program, our continuous LLM red teaming guide walks through CI/CD integration patterns for Promptfoo and PyRIT. The enterprise AI security testing guide covers both tool selection and engagement scoping for complex environments.

Matching Tool to Use Case

If you are a security engineer who wants red teaming integrated into pull requests with OWASP-mapped reports and minimal configuration: start with Promptfoo.

If you are an AI red team researcher who needs to build custom multi-turn attack campaigns and wants full programmatic control over attack logic: use PyRIT.

If you are a DevSecOps team running nightly regression tests against a foundation model to catch safety regressions after model updates: use Garak.

If you are a compliance-focused team that needs the fastest path to OWASP LLM Top 10 evidence without significant engineering overhead: use DeepTeam.

If you are a CISO responsible for a large AI estate with diverse deployments, multiple models, and a compliance audit on the calendar: evaluate Mindgard for continuous coverage and plan a managed AI penetration test to cover agentic attack surfaces the platform does not reach.

Conclusion

The AI red teaming tools landscape in 2026 is more mature than it was eighteen months ago, but no single tool covers the full attack surface of a production AI system. Promptfoo is the right default for engineering teams who want CI/CD-integrated application testing. PyRIT is the right choice for researchers who need multi-turn attack orchestration. Garak handles model-level scanning with the widest probe coverage. DeepTeam gives compliance teams the cleanest OWASP mapping for audit evidence. Commercial platforms like Mindgard add the continuous monitoring, agentic coverage, and formal compliance reporting layer that open-source tools still lack.

For most production deployments, the answer is a combination: one open-source tool running in CI/CD for continuous baseline coverage, plus periodic managed AI penetration testing to cover the attack surfaces that automated scanning cannot reach, particularly agentic pipeline attacks and custom multi-turn exploitation of hardened models.

If you want an expert assessment of your current AI red teaming coverage, or need a structured engagement before a compliance audit, book a call with the BeyondScale team. Our Securetom scanner can also run an automated baseline assessment of your AI deployment in under ten minutes.