Best LLM Vulnerability Scanners 2026: Garak, PyRIT, Promptfoo, and Mindgard Compared
A practitioner's guide to the best LLM vulnerability scanners in 2026 — Garak v0.15.0, PyRIT, Promptfoo (now OpenAI), and Mindgard. OWASP LLM Top 10 coverage, CI/CD fit, and buyer profiles.
The best LLM vulnerability scanners in 2026 occupy a narrower category than most procurement guides acknowledge: tools that actively probe your model or application for exploitable weaknesses before an attacker does. This is distinct from runtime filters that stop attacks in flight and from general SAST tools that scan your Python rather than your prompts. If you’re evaluating pre-deployment scanning for an LLM application this year, four tools appear on serious shortlists — Garak, PyRIT, Promptfoo, and Mindgard — and the selection turns on deployment model, attack surface scope, and CI/CD integration depth.
What LLM Vulnerability Scanners Actually Test
The reference threat model is the OWASP Top 10 for LLM Applications 2025 ↗, which updated significantly from the 2023 version. Two new categories were added and several others substantially reworked. The risks most relevant to scanner coverage:
- LLM01:2025 Prompt Injection — crafted inputs hijack model behavior; the attack surface expanded considerably with agentic architectures where the model processes external data.
- LLM02:2025 Sensitive Information Disclosure — training-data extraction and context leakage from both the model and the application layer.
- LLM06:2025 Excessive Agency — an agent with tool access takes unauthorized or consequential actions beyond intended scope.
- LLM07:2025 System Prompt Leakage — extraction of confidential system instructions through adversarial prompting.
- LLM08:2025 Vector and Embedding Weaknesses — retrieval poisoning and adversarial manipulation of RAG pipeline inputs.
No single scanner covers all ten categories at equal depth. The practical question before any procurement is: which OWASP categories does your deployment actually expose? A chat interface sitting in front of a single model has a different threat surface than a multi-agent pipeline with web browsing, code execution, and CRM write access.
The Best LLM Vulnerability Scanners for 2026
Garak (NVIDIA)
Garak ↗ — generative AI red-teaming and assessment kit — is the broadest open-source scanner available and the closest analog to Nessus for LLM infrastructure. Version 0.15.0, released May 2026, added a multi-turn GOAT probe, an Agent-breaker probe for testing tools available to LLM agents, a system-prompt-extraction probe targeting LLM07, a ModernBERT refusal detector, and native NeMo Guardrails server support.
The tool ships with 50+ probe modules and 28 detector types. Probe categories cover prompt injection, jailbreaks, encoding bypasses (Base64, ROT-13, visual encoding), data leakage, package hallucination, and toxic content generation. It supports 23 generator backends: OpenAI, Anthropic, Hugging Face, AWS Bedrock, Replicate, Cohere, Groq, llama.cpp, and custom REST endpoints.
Trade-offs: CLI-first workflow requires scripting effort to integrate into CI/CD pipelines at scale. A full suite run takes hours; targeted probe subsets bring that to minutes and are the practical choice for every-commit gating. Garak is a pre-deployment scanner — it finds holes, it doesn’t seal them at runtime. For the complementary guardrail layer, guardml.io ↗ covers open-source runtime defenses in detail.
Garak fits teams that want maximum probe breadth and are comfortable building around a command-line tool. It does not fit teams expecting a turnkey enterprise dashboard.
Microsoft PyRIT
PyRIT (Python Risk Identification Toolkit) is Microsoft’s open-source automation framework ↗ for adversarial probing of generative AI systems. The framework ships with 53+ attack datasets — HarmBench, AdvBench, XSTest, AIRT, and others — and 20+ response scorers including LLM-as-judge, Azure AI Content Safety integration, Likert scales, and true/false classifiers.
The standout capability for agentic deployments is XPIAOrchestrator, which runs cross-domain prompt injection attacks by embedding malicious instructions in external data sources: document stores, email bodies, and web content retrieved by the agent. This directly targets LLM08 vector weaknesses and LLM01 indirect injection — the two attack classes hardest to catch with naive probe suites that only test direct user input.
PyRIT requires more configuration effort than Garak for comparable coverage, but the scoring infrastructure and dataset library are more mature for producing structured OWASP or NIST AI RMF evidence maps. Recommended for teams who need auditable test artifacts tied to specific control frameworks, and for any team running RAG-augmented or agentic systems. For a deeper look at the indirect injection mechanics PyRIT targets, aisec.blog ↗ covers the attack patterns in operational detail.
Promptfoo
Promptfoo’s red-team mode ↗ auto-generates adversarial prompts using 50+ attack plugins spanning prompt injection, jailbreaks, PII leakage, SSRF, SQL injection through tool calls, excessive agency, and hallucination. The platform ships OWASP LLM Top 10 and NIST AI RMF presets, and it has the cleanest native CI/CD integration of any tool in this category: a YAML config wraps your LLM endpoint, a GitHub Action gates the release, and failing builds block on configurable vulnerability thresholds.
In March 2026, OpenAI acquired Promptfoo ↗ for undisclosed terms, having built a user base of 350,000 developers and deployments at 25% of Fortune 500 companies. Per OpenAI’s announcement, the project remains open source under its current MIT license. The acquisition confirms the tool’s production credibility but introduces the standard governance questions for any open-source project absorbed by a commercial entity.
Promptfoo occupies the sweet spot for development teams who want automated security gates without dedicated red team staffing. Garak covers more attack categories in depth; Promptfoo integrates more cleanly into developer workflows from day one.
Mindgard
Mindgard is the enterprise option: annual subscription (five figures), managed adversarial testing, continuous scanning rather than point-in-time runs, and a reporting layer that maps findings directly to MITRE ATLAS and OWASP LLM categories. The web interface is designed for security teams rather than developers, and the output format is built for compliance artifact handoffs.
The trade-off is cost and integration overhead. Mindgard fits organizations with dedicated AppSec budget, a compliance requirement (SOC 2 AI addendum, EU AI Act high-risk classification), and a preference for vendor-managed tooling over maintained open-source stacks. It does not fit teams who want developer-led security gates or who are early enough in their LLM deployment to still be validating threat model assumptions.
Matching Tool to Role
No single tool covers the full OWASP LLM10 surface. The right stack depends on where you sit:
- Security engineers running quarterly red team assessments: Garak for breadth across direct attacks, PyRIT for agent and RAG-specific indirect injection chains.
- DevSecOps teams implementing CI/CD gates: Promptfoo has the lowest setup cost and surfaces high-severity issues fastest against a developer workflow.
- Enterprise AppSec with compliance mandates: Mindgard provides the audit trail; layer Garak underneath for probe depth the managed platform doesn’t reach.
- Teams that have not yet implemented pre-deployment scanning: start with Promptfoo — the OWASP preset configuration takes minutes and will find LLM01 and LLM06 exposures in most applications immediately.
The residual risk after any scanner is the attack surface the tool’s probe library does not yet cover. That gap expands with every new agentic deployment pattern — multi-agent handoffs, tool-augmented reasoning, retrieval over untrusted content. The correct response is layered scanning at build time and runtime filtering at the production edge, not faith in any single tool’s coverage claims.
Sources
- OWASP Top 10 for LLM Applications 2025 — https://genai.owasp.org/llm-top-10/ ↗. The canonical LLM threat taxonomy; the 2025 edition adds System Prompt Leakage (LLM07) and Vector and Embedding Weaknesses (LLM08).
- NVIDIA garak GitHub repository — https://github.com/NVIDIA/garak ↗. Version history, probe module list, and generator backend support documentation.
- Announcing Microsoft PyRIT — https://www.microsoft.com/en-us/security/blog/2024/02/22/announcing-microsofts-open-automation-framework-to-red-team-generative-ai-systems/ ↗. Microsoft Security Blog on PyRIT’s design goals and intended use cases.
- Promptfoo red-team documentation — https://www.promptfoo.dev/docs/red-team/ ↗. Attack plugin categories, CI/CD integration, and framework alignment details.
- OpenAI acquires Promptfoo — https://techcrunch.com/2026/03/09/openai-acquires-promptfoo-to-secure-its-ai-agents/ ↗. TechCrunch reporting on the March 9, 2026 acquisition and OpenAI’s stated plans for the open-source tool.
Sources
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