Best AI Red Team Tools 2025: A practical guide to features and functions

Why do you need to test AI agents? 

Creating agents is easy, but do you really know yours are safe?. Whether you are working in banking, healthcare or retail, AI systems handle sensitive information, provide guidance, and increasingly make autonomous decisions that directly affect your company and its stakeholders. This makes them extremely useful, but this also positions them as vulnerabilities, exposing your company to security, safety and business risks. So, how do we turn these agents into trustworthy enterprise-ready applications? That’s right, by thoroughly testing them!

From AI risk detection to AI risk prevention

While observation tools and basic evaluation offer a strong foundation for testing, they only identify harm after it has occurred. By the time you’re reviewing logs and spotting an issue, the damage is already done. To proactively prevent such incidents, we need a more advanced approach, AI red teaming.

AI Red teaming is stress-testing AI agents to uncover vulnerabilities before malicious users find ways to exploit them or benign users accidentally encounter them. It involves simulating adversarial attacks to identify, and mitigate potential weaknesses in your AI agents. And trust us, there always are weaknesses!.

To assist you in finding the right tool for red teaming AI models, we've curated a list of the top 7 AI red teaming tools available in 2025.  This list should help you understand what each of the red teaming tools offers, and, more importantly, what they do not offer

Features, pros and cons for each of the top red teaming tools

1. Giskard, best for attack coverage, collaboration and automation (🇪🇺, France) 

Giskard offers an advanced automated red-teaming platform for LLM agents- including chatbots, RAG pipelines, and virtual assistants. Unlike tools limited to static single-turn prompts, Giskard performs dynamic multi-turn stress tests that simulate real conversations to uncover context-dependent vulnerabilities: hallucinations, omissions, prompt injections, data leakage, inappropriate denials, and more. It includes 50+ specialized probes (e.g., Crescendo, GOAT, SimpleQuestionRAGET) and an adaptive red-teaming engine that escalates attacks intelligently to probe grey zones where defences typically fail. Giskard also generates realistic attack sequences and minimizes false positives, providing higher-confidence results. Discovered vulnerabilities are mapped to OWASP for enterprise-grade traceability.

Pros:

• Detects 50+ specialized probes mapped to OWASP LLM Top 10 vulnerabilities.
• Uncovers weaknesses through dynamic, multi-turn, and adaptive attack strategies that evolve with context.
• Integrates human feedback to refine and validate results, reducing false positives and improving reliability.
• Supports security and business alignment testing within a collaboration-ready platform featuring a business-friendly UI, Python SDK, and has an open-source for data scientists

Cons:

• Limited to text-based AI.
• No remediation suggestions.

2. Confident AI, best for Python code integration and foundational evals (🇺🇸, USA)

DeepTeam is an open-source LLM red-teaming framework for stress-testing AI agents such as RAG pipelines, chatbots, and autonomous LLM systems. It implements 40+ vulnerability classes (prompt injection, PII leakage, hallucinations, robustness failures) and 10+ adversarial attack strategies (multi-turn jailbreaks, encoding obfuscations, adaptive pivots). Results are scored with built-in metrics aligned to OWASP LLM Top 10 and NIST AI RMF, enabling reproducible, standards-driven security evaluation with full local deployment support.

Pros:

• Detects a wide range of 40+ vulnerabilities with 10+ adversarial attack strategies for realistic red-teaming.
• Aligns with OWASP LLM Top 10 and NIST AI RMF, ensuring standards-based risk assessment and governance.
• Extensible architecture allows rapid addition of custom attack modules, datasets, or evaluation metrics.

Cons:

• Limited to text-based LLM applications.
• Requires technical expertise to configure and operate effectively.

3. Deepchecks, best for predict ML/LLM evaluations and monitoring(🇮🇱, Israel)

Deepchecks is an LLM evaluation and monitoring platform for LLM agents including chatbots, RAG pipelines, and virtual assistants. Unlike tools limited to testing phases alone, Deepchecks combines systematic evaluation with continuous production monitoring to uncover vulnerabilities: hallucinations, data leakage, reasoning failures, and robustness issues. It includes automated scoring mechanisms, version comparison analytics, and vulnerability detection mapped to OWASP and NIST AI RMF standards. Deepchecks generates evaluation reports with end-to-end traceability from development through production deployment.

Pros:

• Evaluates agentic systems at runtime, combining testing with continuous production monitoring.
• Support traditional ML evaluation and testing too
• Detects vulnerabilities through automated evaluation with reduced noise and higher-confidence results.
• Seamless CI/CD integration with flexible deployment options (on-premise, AWS GovCloud, hybrid).

Cons:

• Limited to text-based LLM applications.
• Not specialized in adversarial attack generation like pure red-teaming frameworks.
• Steeper learning curve than lightweight alternatives.
• Positioned as evaluation-first rather than attack-first platform.

4. Splx AI, best for multi-modal AI red teaming (🇺🇸, USA)

Splx AI is a commercial, end-to-end platform for red teaming and securing conversational AI agents, including chatbots and virtual assistants. It runs thousands of automated adversarial scenarios such as prompt injection, social engineering, hallucinations, and off-topic responses, helping teams uncover vulnerabilities quickly. The platform integrates directly into CI/CD pipelines and offers real-time protection features.

Pros:

• Covers a wide range of adversarial scenarios, including social engineering.
• Provides real-time protection as well as testing.
• Strong enterprise integration with CI/CD pipelines.

Cons:

• Proprietary and closed-source.
• Less customizable compared to alternatives.
• No clear human feedback pattern.
• Credit-based pricing.

5. Promptfoo, good for multi-modal coverage and remediations (🇺🇸, USA)

An open-source, developer-friendly CLI and library for red teaming LLM-based agents like chatbots, virtual assistants, and RAG systems. It automatically scans for 40+ vulnerability types and compliance issues mapped to OWASP/NIST standards. It generates tailored adversarial attacks, integrates seamlessly into CI/CD workflows, and runs locally without exposing your data.

Pros:

• Developer-friendly and easy to integrate into CI/CD pipelines.
• Broad vulnerability coverage aligned with industry standards.
• An entirely local operation prevents data exposure.

Cons:

• Primarily targeted at developers rather than enterprise teams.
• Command-line focus, less accessible for non-technical/business users.
• No alerting notifications.

6. Mindgard, great for hands-on assistance and end-to-end security (🇺🇸, USA)

Mindgard’s DAST-AI platform automates red teaming at every stage of the AI lifecycle, supporting end-to-end security. Thanks to its continuous security testing and automated AI red teaming for multiple modalities, it does not just stop at text. For more hands-on assistance, Mindgard also offers AI red teaming services and artefact scanning. 

Pros:

• Extends testing beyond text models to include the broader ecosystem.
• Strong emphasis on compliance and regulatory requirements.
• Offer remediation strategies

Cons:

• Available only as an enterprise solution.
• It likely requires significant resources for implementation.
• No alerting notifications.
• No demo tier.

7. Lasso, for code scanning and MCP testing  (🇺🇸, USA)

A commercial, automated GenAI red-teaming platform that stress-tests chatbots and LLM-powered apps by simulating real-world adversarial attacks, including system prompt weaknesses and hallucinations. It delivers actionable remediation, detailed model-card insights, and continuous security monitoring. They recently launched their open source MCP Gateway, the first security-centric solution for Model Context Protocol (MCP), designed explicitly with agentic workflows. 

Pros:

• Provides continuous monitoring alongside remediation guidance.
• First to offer a security solution tailored for the Model Context Protocol.

Cons:

• Focused primarily on LLM-powered applications.
• Focuses on a wide range of scenarios. 
• No alerting notifications.
• No demo tier

Comparison table of top AI red teaming companies

Each table below gives an objective overview of the state of AI red teaming companies and tools as of 2025. “ ✅” and “ ❌” indicate whether a feature is supported or not, respectively. Lastly, some boxes contain written descriptions for categorical features. Note, that the information was obtained at the time of writing.

Conclusion

The tools highlighted in this article represent the best-in-class options for AI red teaming in 2025, offering solutions for everything from detection to prevention of key vulnerabilities.

Creating an agent is easy, but making it trustworthy is very difficult. For this exact reason, implementing AI red teaming as part of your development and deployment pipelines is required if your team wants to enjoy a stress-free life when deploying public-facing AI agents. By proactively identifying and mitigating vulnerabilities, you can protect your users, uphold regulatory standards, and build AI systems that are safe, reliable, and resilient.

For teams looking to take the next step, Giskard offers a free AI Red Teaming Assessment to detect security and business compliance issues in your public chatbots. Apply here.