10 Best AI Red Teaming Tools for AI Security and Model Testing

Artificial Intelligence is accelerating across all industries. Along with these advancements, there will be increasing risks. The more sophisticated AI systems become, the more important it will be for organizations to evaluate AI systems for safety and compliance. AI Red Teaming will help challenge and test AI systems.

These systems can help identify potential weaknesses. Through challenges and tests, trust in AI can be developed. Several tools and frameworks are available. Some of these tools are free and available to anyone. Others are proprietary frameworks offered by companies. Both types of tools help validate and defend AI systems.

What Is AI Red Teaming?

AI Red Teaming is a method of testing AI systems that involves simulating an adversarial condition on an AI system to identify potential risks and weaknesses. This can include identifying biases or observing hallucinations.

The method utilizes a combination of scoping, threat modeling, and design thinking to develop various attack scenarios and define potential risks.

The goal is to identify risks and validate the trust and reliability of the AI system. Because the risks may not necessarily be attack vectors, AI Red Teaming differs from other threat modeling efforts.

How to Choose an AI Red Teaming Tool?

• Deployment Fit: The tool should be able to be deployed via your preferred environment (i.e. local, cloud or hybrid) and not require significant changes to be integrated to your environment.
• Supported Models: The tool should be able to be integrated with the LLMs you want to test (i.e. OpenAI, Anthropic, Hugging Face, etc.).
• Attack Coverage: The tool should be able to identify prompt injections, jailbreaks and other attacks.
• Agent & RAG Testing: The tool should be able to perform testing via agents and/or RAG.
• Multi-Modality and Multi-Turn: The tool should be able to perform testing via different modalities (i.e. text, images, etc.) and/or turns.
• CI/CD: The tool should be able to be integrated to and perform testing via the CI/CD pipeline.
• Reporting: The tool should be able to generate reports and tests identifying vulnerabilities, and assign prioritization based on risk.
• Cost: Consider the price, features, and support offered by the vendor.

Key Points

Tool	Key Point / Best Use
NVIDIA Garak	Broad static scanning with 120+ probe modules; ideal for pre-integration vulnerability checks.
Microsoft PyRIT	Multi-turn, multi-modal adversarial campaigns; deep exploitation of conversational systems.
Promptfoo	CI/CD-native regression testing; integrates red teaming into developer pipelines.
Giskard	Python-first suite combining security + quality testing; strong EU compliance options.
DeepTeam	40+ vulnerability probes; best for teams already using DeepEval for evaluation.
Mindgard	Continuous automated scanning with compliance-mapped reporting (OWASP, NIST, EU AI Act).
HiddenLayer	Focuses on model security and supply chain risk; protects against poisoned model artefacts.
Lakera Red	Runtime defense layer; closes the loop between red teaming and live guardrails.
SPLX	Structured reporting and framework mapping; enterprise-grade adversarial testing.
Enkrypt	Security-first platform with strong monitoring and exploit chain detection.

1. NVIDIA Garak

Launched in 2023 by the GPUs giant, NVIDIA, Garak is an open-source framework, available for free and licensed under the Apache 2.0 license. Initially released as an LLM scanner, Garak focuses on static vulnerability testing.

It features over 120 probe modules and is continuously improving. In an ever-changing world of LLMs, it identifies prompt injection and jailbreaks as well as hostile LLM outputs.

While it does not adhere to a traditional commercial model, Garak is freely available and can be integrated in CI/CD processes. It offers API automation, and structured reporting, and defines the identified vulnerabilities in relation to the compliance frameworks, NIST and OWASP. Altogether, it is a valuable resource for various use cases and can even perform basic RAG testing.

Best For

Garak provides the widest range of static scanning and can be quite effective for identifying potential vulnerabilities.

Pros

• Static scanning with over 120 templates.
• Free and open source.
• Easy CI/CD integration.
• Good mapping for compliance (NIST, OWASP).

Cons

• No agent-based testing.
• No dashboards.
• No defense in depth.
• Less polished reporting.

2. Microsoft PyRIT

PyRIT is an open-source project released in 2023 by Microsoft. Licensed under MIT, it is freely available and can be deployed either locally or in the cloud. It focuses on large-scale, multi-modal adversarial attacks, and is particularly useful in testing conversational AI. It offers provider and LLM flexibility.

It incorporates prompt injection and jailbreak attacks and supports attack chaining. It performs agent testing and RAG evaluation and is capable of advanced modal probing. Like Garak, it can perform advanced testing, and is useful in enterprise processes and R&D.

It defines vulnerabilities in relation to compliance frameworks of NIST and the EU AI Act. It offers automation and SDKs and is easily integrated in the CI/CD process. Detailed reports are generated by the tool.

Best For

Almost all combinations of different conversations (multi-turn) with differing modes of inputs (text and/or images) are covered with this tool.

Pros

• Can model conversations with differing turns.
• Supports text and images for input.
• Free and open source.
• Good compliance mapping (EU AI Act and NIST).

Cons

• Takes some development and/or ops experience for deployment.
• No defense in depth.
• Less polished reporting.
• CI/CD integration is less straight-forward when compared to PromptFoo.

3. Promptfoo

As an MIT-licensed product by OpenAI, Promptfoo provides a regression testing framework to the software development industry. Given its open-source and CI/CD structure and functionality, developer work pipelines are easily embedded with Promptfoo.

Its major focus lies in evaluation of prompts and testing in an adversarial manner. Because of this, it supports and integrates a range of LLMs.

In regards to attack vectors, Promptfoo is strong in determining jailbreak mechanisms and prompt injection. While it can perform regression testing and validate software pipelines, it can not perform multi-modal or multi-turn tests.

It should be noted that integration with CI/CD is its competitive edge. It provides APIs, SDKs (primarily for Python), performs compliance testing and reports (primarily for software), integrates with OWASP and NIST frameworks and standards.

Best For

PromptFoo is focused on integrating with and testing CI/CD tools and workflows.

Pros

• Provides templates for CI/CD tools and workflows.
• Free and open source.
• Can test for prompt safety.
• Easy to integrate with APIs and SDKs.

Cons

• Limited agent-based testing and RAG testing.
• No support for multi-turn conversations and/or multi-modal input.
• Focus is on prompt safety and regression testing.
• Reporting and mapping are more geared to safety.

4. Giskard

Launched in 2022, Giskard is a free and open-source python framework (Apache License 2.0) for software testing. An enterprise version is available. Given its framework, cloud and edge deployment is supported. It integrates several LLMs and service providers and is designed for quality and security software testing while incorporating and embodying EU laws and frameworks.

It performs tests for prompt bias and injection, and unsafe outputs. Agent and RAG testing are performed by Giskard. Multi-turn and multi-modal tests are planned features. It performs CI/CD integrations and provides SDKs, primarily for Python. It performs regulatory compliance tests and reports, integrates and embodies NIST, ISO and the EU AI Act.

Best For

If your focus is in the EU and EU compliance, and you prefer Python, then Giskard is the tool for you.

Pros

• Integrates security and quality testing.
• Ranges from agent and RAG testing.
• Good mapping for the EU AI Act.
• Enterprise support is available.
• Free and open source.

Cons

• Multi-modal inputs and testing are yet to be fully implemented.
• Less polished reporting.
• Requires knowledge of Python.
• No defense in depth when compared to Lakera Red.

5. DeepTeam

DeepTeam was released in 2024 by the DeepEval team, and is fully open source and available on GitHub. DeepTeam is licensed under the Apache 2.0 license, meaning it can be freely used and deployed on any platform. Currently, DeepTeam contains over 40 vulnerability probes and is interoperable with numerous LLMs and providers.

DeepTeam provides probes for various attacks such as prompt injection and jailbreaks. It provides support for RAG testing and agent-based testing. DeepTeam is limited for use with other types of testing. DeepTeam is designed to work within CI/CD pipelines and offers SDKs and REST APIs. It is designed with research and academics in mind and is strong for compliance mapping.

Best For

• DeepEval users can benefit from DeepTeam’s probes of vulnerability.

Pros

• 40 plus probes.
• Free and open source (Apache 2.0).
• Libraries for agent and RAG testing.

Cons

• Support for neither multimodal nor multi-turn.
• Not enterprise ready reporting.
• No attack defense in depth.
• Less developed community (when compared to PyRIT and Garak).

6. Mindgard

Mindgard was released in 2023 and focuses on providing commercial AI red teaming services. Like DeepTeam, Mindgard focuses on report generation to assist in compliance with various frameworks including the NIST, OWASP, and the recently passed EU AI Act.

Attack vectors supported by Mindgard include prompt injection and jailbreaks. It supports RAG and agent based testing, as well as, multi-turn and multi-modal interactions. Mindgard offers integrations with CI/CD tools and generates compliance dashboards. It is best suited for use in the enterprise.

Best For

Best for prolonged automated scanning and dashboarding for compliance.

Pros

• Reporting for the enterprise (e.g. OWASP, NIST, EU AI Act).
• RAG, multi-modal, and multi-turn testing.
• CI/CD and CD integration.
• Continuous monitoring.

Cons

• Cost and complexity exceed open source alternatives.
• Proprietary Software.
• Cloud only.

7. HiddenLayer

HiddenLayer, launched in 2022, is an AI security company. Information on pricing and license models is not publicly available. HiddenLayer’s AI models are cloud-based and address model security and supply chain risks. HiddenLayer identifies and protects against model artifacts and risks posed by adversarial attacks.

HiddenLayer identifies and protects against various attack types, including data poisoning and prompt injection. It provides guards against agent and RAG testing. While HiddenLayer supports various types of adversarial attacks, including multimodal and multi-turn attacks, these features are currently in beta.

HiddenLayer provides integration with CI/CD and SDKs and generates compliance reports. HiddenLayer is especially beneficial in addressing model risks in the software supply chain.

Best For

• Best for securing models and supply chains.

Pros

• Enterprise reporting and integrations for compliance.
• Agent and RAG testing.
• CI/CD and SDK integrations.

Cons

• Cost and complexity exceed open source alternatives.
• Proprietary Software.
• Cloud only.

8. Lakera Red

Lakera AI, in 2023, released Lakera Red as a commercial defense-in-depth runtime platform. Lakera Red is cloud-native and can be fully integrated with production systems. Lakera Red provides runtime guards and guardrails against attacks for the supported LLMs and providers.

Lakera Red addresses prompt injection and jailbreak attacks and protects against exploit chaining. Lakera Red supports RAG and other types of adversarial testing. Like HiddenLayer, Lakera Red provides protection against various types of adversarial attacks including multi-turn and other advanced types of adversarial attacks.

Lakera Red reports compliance status and generates compliance reports. The novel approach by Lakera of integrating a runtime protection system with a red teaming system enhances the overall protection offered by the platform.

Best For

This tool can help you monitor your systems and orient your attack surface defense in real-time.

Pros

• Lakera combines protection and defense in a single solution.
• Supports agent, RAG and multimodal interaction.
• Provides real-time enterprise dashboards and guardrails.
• Integrates CI/CD and APIs/SDKs.

Cons

• Proprietary pricing.
• Single-platform (cloud).
• Expensive and more difficult to use than competing solutions.

9. SPLX

SPLX is a company that is based in the United States and was founded in 2023. The company offers commercial versions of AI red teaming services. As of now, the company does not have many clients and charges customers based on the company’s proprietary licensing.

SPLX’s services are cloud based and offer types of adversarial testing. The company focuses on larger organizations and provides training for the threat frameworks and methodologies.

The company provides services for testing AI systems in a way that prompts and instructs the AI system to perform attacks, jailbreaks, and other exploits. Other testing offered by SPLX includes RAG testing, agent based testing, and other types of adversarial testing.

SPLX offers services and integrations with CI/CD pipelines and provides APIs and SDKs to its clients. The company provides training for clients on how to perform adversarial testing in a structured manner, and aligns its offerings with the EU AI Act, OWASP and NIST.

Best For

Structured adversarial testing with compliance mapping.

Pros

• Provides structured, enterprise level reporting.
• Supports agent, RAG, and multimodal interactions.
• Integrates CI/CD.
• Maps compliance to NIST, OWASP, and the EU AI Act.

Cons

• Proprietary pricing.
• Single-platform (cloud).
• Expensive and less developer centered compared to competitors.

10. Enkrypt

Enkrypt was founded in 2024 and is also based in the United States. Similar to SPLX, Enkrypt offers a commercial version of an AI security platform. Enkrypt charges its customers based on a subscription. Enkrypt focuses on exploit chains and detecting attacks.

Similar to SPLX, Enkrypt offers types of adversarial testing and integrates with CI/CD platforms. The company provides APIs and SDKs, structured reporting, and aligns its offerings with NIST and ISO.

Best For

Security‑first monitoring and exploit chain detection.

Pros

• Powerful exploit chain detection.
• Supports RAG and multimodal interaction.
• Provides enterprise dashboards and guardrails.
• Integrates CI/CD and APIs/SDKs.

Cons

• Proprietary pricing.
• Single-platform (cloud).
• More features and therefore complexity than needed for most teams.

Why Do Businesses Need AI Red Teaming?

• Risk Management: Our AI-based testing solutions help enterprises analyze the risks associated with various AI failures and biases and protect themselves against potential attacks, thus preserving their reputation and avoiding legal issues.
• Legal Protection: AI systems are incorporated in almost all business processes. Our solution helps enterprises evaluate their AI systems against standards and frameworks such as the NIST and the EU’s Artificial Intelligence Act to prevent legal issues.
• Peace of Mind: Our solution helps enterprises identify various AI failures, develop guardrails and protect their customers’ information against failures to enhance their AI systems, build trust and peace of mind.
• Defend and Protect: Our solution helps enterprises safeguard their customers’ information from various Failures and breaches, build customer trust and differentiate their offerings in the market.
• Understand and Modify: Our solution helps enterprises identify various Failures and guardrails.
• Safeguard Customers: Our solution helps enterprises build guardrails and protect their customers from various failures.
• Additional Features: Our solution provides a competitive edge to enterprises by helping them safeguard their customers’ information and differentiate themselves from other enterprises in their industry.
• Improve Profitability: Our solution helps enterprises prevent failures, reduce costs and boost profitability.

Conclusion

Companies must take various precautions when releasing large language models and multimodal models. Several tools and services have been created for red teaming. Some tools are NVIDIA Garak, Microsoft PyRIT, PromptFool, and Giskard. Deep team, Mindgard, HiddenLayer, Lakera Red, SPLX, and Enkrypt, provide similar services for enterprises.

All the tools identified cover different types of risks, from prompt injection to more complex attacks. The main difference from one tool to another is the level of control provided to the customer and the type of threat model covered.

Whichever the case, all the tools described strengthen how a company defends itself against the different types of adversarial attacks against its artificial intelligence systems.

FAQ