TL;DR: Mindgard covers automated LLM vulnerability testing but has a significant gap in agentic AI attack surfaces: it does not test multi-agent pipelines, MCP tool poisoning, or agentic orchestration frameworks. This post covers five like-for-like red teaming alternatives, what each one actually tests, and where each one stops.

Why teams look beyond Mindgard

Mindgard is a UK-based AI security platform that automates red teaming of LLMs and ML models. It runs attack simulations against deployed models and surfaces vulnerabilities across a defined set of risk categories.

The gap most security teams hit is agentic coverage. AI applications in 2026 are increasingly multi-agent systems connected via MCP servers, external tools, and API integrations. Mindgard's testing model was built around single-model evaluation. It does not natively test MCP tool poisoning, agent-to-agent prompt injection, or the attack surfaces introduced by agentic orchestration frameworks like LangGraph, CrewAI, or AutoGen.

A second gap is depth on RAG pipelines. Mindgard tests model-level behavior but does not have dedicated coverage for retrieval pipeline poisoning, context contamination via injected documents, or the indirect prompt injection paths that open up when an LLM pulls from an external knowledge base.

A third gap is framework completeness. MITRE ATLAS documents over 80 AI-specific attack techniques across 14 tactic categories. Mindgard's coverage maps primarily to OWASP LLM Top 10. Teams that need to demonstrate coverage against NIST AI RMF or MITRE ATLAS for compliance or audit purposes will find gaps in Mindgard's reporting output.

The five alternatives

1. Repello AI (ARTEMIS)

ARTEMIS is Repello AI's automated red teaming engine. On the testing dimension, it is the closest commercial match to Mindgard while covering the attack surfaces Mindgard does not.

ARTEMIS runs context-specific attack simulations tailored to the application under test, drawing from 15M+ evolving attack patterns across OWASP LLM Top 10, NIST AI RMF, and MITRE ATLAS. Coverage is multimodal: text, images, voice, and documents across 100+ languages. Where Mindgard evaluates single-model behavior, ARTEMIS tests the full AI application: RAG pipelines, multi-agent orchestrations, MCP server integrations, and agentic workflows built on LangGraph, CrewAI, and AutoGen. Attack patterns are context-specific to the application, not generic probes run against an endpoint. Output is compliance-mapped reports with prioritized remediation steps tied to framework controls.

For teams that need runtime protection beyond testing, Repello also offers ARGUS, and AI Inventory for asset discovery. For this comparison, the like-for-like layer is ARTEMIS. Book a demo to see coverage against your specific stack.

2. Garak

Garak is an open source LLM vulnerability scanner originally developed by Leon Derczynski and now maintained with support from NVIDIA. It runs probes across a defined taxonomy of LLM failure modes: prompt injection, jailbreaking, hallucination, data leakage, and toxicity generation, among others.

Garak is the right tool for teams that want free, extensible, community-maintained red teaming at the model level. The probe library is large and actively updated. Integration into CI/CD pipelines is straightforward via Python.

The limits are real. Garak tests models, not AI applications. It does not test RAG pipelines, agentic workflows, or MCP integrations. It has no runtime protection component. It requires engineering time to configure, interpret, and act on results. For teams without a dedicated AI security engineer, the gap between running Garak and having an actionable remediation plan is significant.

3. PyRIT

PyRIT (Python Risk Identification Toolkit) is Microsoft's open source framework for red teaming generative AI systems. It provides a programmatic interface for running multi-turn adversarial conversations, automated attack orchestration, and scoring of model responses across safety dimensions.

PyRIT is well-suited for teams building on Azure AI infrastructure and for engineers who want to write custom attack scenarios in Python. The framework is flexible and the Azure integration is tight. Microsoft uses it internally for red teaming its own AI products.

The gaps: PyRIT is a framework, not a product. There is no dashboard, no compliance reporting, and no out-of-the-box attack coverage that works without configuration. Like Garak, it tests at the model or application prompt level and does not extend to runtime protection or asset discovery. It also requires meaningful Python engineering to operate effectively.

4. promptfoo

promptfoo is an open source LLM testing and evaluation tool that covers both quality evaluation (output correctness, consistency) and security testing (prompt injection, jailbreaking, PII leakage). It has a web-based UI and integrates into CI/CD pipelines via CLI or GitHub Actions.

promptfoo is particularly strong for teams that need to combine safety testing with output quality evaluation in a single workflow. It supports a wide range of model providers and has a growing library of security-focused test cases drawn from OWASP LLM Top 10 categories.

The ceiling: promptfoo is a testing and evaluation tool. It does not cover runtime protection, agentic attack surfaces, or AI asset inventory. For teams that need a complete security posture, it covers one layer of a multi-layer problem.

5. Giskard

Giskard is an open source ML testing framework that covers both traditional ML models and LLM-based applications. Its LLM testing module includes prompt injection detection, hallucination testing, and a RAG evaluation component that tests retrieval pipeline behavior under adversarial inputs.

The RAG evaluation module is Giskard's strongest differentiator in this comparison. Teams running production RAG pipelines who need structured testing of retrieval behavior, context contamination, and adversarial document injection will find more relevant coverage in Giskard than in most other open source options.

Giskard does not cover runtime protection, agentic attack surfaces, or MCP security. Like the other open source tools here, it requires engineering time to configure and is a testing tool rather than a platform.

Comparison table

How to choose

If your primary need is pre-production red teaming of a single LLM application and you have engineering resources to operate an open source tool, Garak or PyRIT cover that use case at no cost. If you need RAG pipeline testing specifically, Giskard is worth evaluating.

If you need compliance reporting, structured attack coverage across OWASP and NIST frameworks, and results that non-engineers can act on, the open source tools are not the right fit. Both Mindgard and Repello AI cover that need at the testing layer.

Where ARTEMIS separates from Mindgard at the testing layer is agentic coverage and framework depth. If you are running multi-agent systems, MCP integrations, or agentic workflows, ARTEMIS tests those attack surfaces natively. Mindgard does not. If your compliance requirement extends to MITRE ATLAS or NIST AI RMF, ARTEMIS maps output to those frameworks. Mindgard's reporting maps primarily to OWASP LLM Top 10.

FAQ

Is Mindgard a good tool?

Mindgard covers automated LLM vulnerability testing and produces structured compliance reporting. For teams that need pre-production red teaming of LLM applications and clear output against standard frameworks, it is a capable tool. The gaps are in runtime protection, agentic attack surface coverage, and asset inventory. Whether those gaps matter depends on your deployment context.

What is the main difference between Mindgard and ARTEMIS?

Both are commercial AI red teaming platforms with compliance reporting. ARTEMIS tests the full AI application stack including RAG pipelines, multi-agent systems, and MCP integrations natively. Mindgard's testing model is built around single-model evaluation and does not natively cover agentic attack surfaces. ARTEMIS also maps output to OWASP LLM Top 10, NIST AI RMF, and MITRE ATLAS; Mindgard maps primarily to OWASP LLM Top 10.

Can open source tools replace a commercial AI red teaming platform?

For specific, narrow use cases with engineering resources to operate them, yes. Garak covers broad LLM vulnerability scanning. PyRIT covers programmatic adversarial testing. Giskard covers RAG evaluation. None of them produce compliance reports or work without significant configuration. Teams that need to satisfy audit requirements or operate without a dedicated AI security engineer will hit the ceiling of open source tooling quickly.

What attack surfaces should an AI red teaming platform cover in 2026?

Beyond standard prompt injection and jailbreaking, a complete platform should test indirect prompt injection via external data sources, RAG pipeline poisoning, multi-agent orchestration attack paths, MCP tool poisoning, multimodal inputs across text, images, and audio, and cross-lingual bypass techniques. ARTEMIS covers all of these. Most point tools cover a subset.