AI systems are becoming part of everyday life in business, healthcare, finance, and many other areas. As these systems handle more important tasks, the security risks they face grow larger. AI red teaming tools help organizations test their AI systems by simulating attacks and finding weaknesses before real threats can exploit them.
These tools work by challenging AI models in different ways to see how they respond under pressure. They look for problems like biased outputs, data leaks, prompt injections, and other vulnerabilities that could harm your organization or users. Red teaming has moved from being an optional practice to a necessary part of building safe AI systems.
This article explores the leading AI red teaming tools available today and what makes each one useful for different needs. You’ll also learn about the core concepts that drive these tools and the ethical considerations you should keep in mind when testing your AI systems.
1. Mindgard
The Mindgard AI Red Teaming Tool is an automated AI red teaming platform that tests AI systems for security vulnerabilities. The platform was developed by UK scientists with over six years of testing experience in AI security.
You can use Mindgard to test various AI applications, including large language models, image models, audio models, and multi-modal systems. The platform focuses on finding runtime vulnerabilities that appear when your AI systems are actually running. This includes risks like prompt injection, model extraction, data poisoning, and evasion tactics.
The tool uses Continuous Automated Red Teaming to secure your AI systems throughout their entire lifecycle. It integrates with major MLOps and CI/CD platforms so you can test your models during development and deployment.
Unlike traditional application security tools, Mindgard addresses vulnerabilities specific to AI technology. It runs automated adversarial testing to find exploitation paths that attackers might use.
Mindgard also offers AI Security Labs, a free online tool for engineers who want to evaluate cyber risks in their AI systems. This allows you to perform basic red teaming tests before committing to the full platform.
2. Promptfoo
Promptfoo is an open-source tool designed to help you test and secure your LLM applications. You can use it to find vulnerabilities in your AI systems before they become problems.
The platform focuses on red teaming, which means it actively searches for weaknesses in your prompts, agents, and RAG systems. It looks for issues like prompt injections, jailbreaks, and data leaks that could harm your application.
You get access to automated testing that works with your existing development workflow. The tool integrates with command line interfaces and CI/CD pipelines, making it practical for regular use.
Promptfoo lets you compare different LLM providers including GPT, Claude, Gemini, and Llama. This helps you choose the best model for your needs based on actual performance data.
The tool uses declarative configuration files, which means you set up your tests without writing complex code. You can create adaptive attacks that target your specific application rather than running generic tests.
Over 30,000 developers currently use Promptfoo for their LLM security testing. The platform helps you catch security issues early in development when they’re easier and cheaper to fix.
3. PyRIT
PyRIT stands for Python Risk Identification Tool for generative AI. Microsoft developed this open-source framework to help you find security and safety issues in AI systems.
You can use PyRIT to test generative AI models for harmful outputs and potential vulnerabilities. The tool works with different types of AI models and platforms, which means you’re not locked into one specific system.
Microsoft created PyRIT in 2022 for its own internal red teaming work. The company used it to test systems like Copilot before releasing it publicly on GitHub. Now security professionals and machine learning engineers can access the same framework.
The tool helps you organize and structure your red teaming efforts. You can probe AI systems for novel harms, risks, and jailbreaks. PyRIT supports testing across multimodal generative AI models, not just text-based systems.
The framework includes ways to link data sets to targets and score your results. You can run it in the cloud or use it with smaller language models based on your needs.
4. Cobalt AI Red Teaming Platform
Cobalt offers red team services that help you test your security controls and understand how ready your organization is to handle threats. The platform lets you simulate attacks to find weak points in your systems before real attackers do.
You can use Cobalt to run realistic security assessments that go beyond basic testing. The service focuses on helping you bridge what they call the AI readiness gap as organizations adopt new AI technologies.
Cobalt’s approach involves working with experienced security professionals who act as adversaries. They test your defences using real-world attack methods. This gives you practical insights into how your security measures actually perform under pressure.
The platform aims to help you understand where your vulnerabilities exist. You get detailed information about what worked and what didn’t during the simulated attacks. This allows you to make informed decisions about where to strengthen your security.
Cobalt’s red team services are designed for organizations that need to verify their security controls are working as intended. The testing helps you prepare your security operations centre for actual incidents.
5. Garak
Garak stands for Generative AI Red-teaming and Assessment Kit. It’s an open-source tool developed by NVIDIA that scans for vulnerabilities in large language models.
You can use Garak to test your AI systems for common security problems. The tool checks for issues like hallucination, data leakage, prompt injection, misinformation, and toxicity generation. It also probes for jailbreaks and other weaknesses.
If you’re familiar with network security tools like nmap or Metasploit Framework, Garak works in a similar way but focuses on LLMs instead. The tool is written entirely in Python and has built an active community around it.
Garak helps solve a key problem with traditional red teaming. Human experts are expensive and hard to find. Manual testing also doesn’t scale well when you need to check many different vulnerabilities. This tool automates the testing process so you can find security issues faster.
You need proper authorization before using Garak on any AI system. Testing applications without permission is illegal.
6. FuzzyAI
FuzzyAI is an open source tool built for automated LLM fuzzing. It helps you find jailbreaks and security vulnerabilities in your LLM APIs before they become problems.
The tool was developed by CyberArk. It works by automatically generating adversarial prompts to test how your AI system responds under different conditions. This saves you time compared to manual testing.
FuzzyAI focuses on identifying weak points where users might bypass your safety controls. When you run the fuzzer, it simulates various attack scenarios against your language model. The tool then reports back on any successful jailbreaks or unexpected behaviours it discovers.
This makes FuzzyAI useful for developers and security researchers who need to validate their AI systems. You can integrate it into your development workflow to catch issues early.
The automated nature of FuzzyAI means you can test a large number of potential vulnerabilities quickly. This is important because manually testing every possible attack vector would take too long. The tool helps you prioritize which security gaps need attention first.
7. Bishop Fox AI Red Teaming Suite
Bishop Fox brings its proven red teaming methods to AI security testing. The company offers a modular approach that adapts to your specific needs rather than forcing you into a standard package.
Their AI red teaming service works by understanding your goals first. Then they build a custom engagement that matches your organization’s requirements. This means you get testing that focuses on your actual risks.
Bishop Fox combines traditional security expertise with AI-specific challenges. Their team simulates real-world attacks against AI systems to find weak points before attackers do. They test not just the technology but also how your processes and people respond.
The service helps you meet regulatory expectations while protecting your operations. Bishop Fox has experience working with financial institutions and other regulated industries. Their assessments show you where your AI systems might fail under pressure.
You can use Bishop Fox if you need a tailored security assessment. Their building block method lets you choose which parts of your AI infrastructure to test. This flexibility works well for organizations with complex AI deployments or specific compliance requirements.
8. Microsoft AI Red Teaming Agent
Microsoft AI Red Teaming Agent helps you find safety risks in generative AI systems during development. It works directly in Azure AI Foundry and automates the testing process that typically requires specialized experts.
The tool connects with PyRIT, which stands for Python Risk Identification Tool. This is an open-source framework from Microsoft’s AI Red Team. You can use it to test your AI models and applications without building custom testing tools from scratch.
The agent scans your systems across different risk categories. These include violence, hate and unfairness, and self-harm. It uses attack strategies of varying complexity levels to probe your AI defences.
When you run automated scans, the tool evaluates how successful different attacks are against your system. It generates detailed scorecards that show where vulnerabilities exist. You can use these reports to guide your risk management decisions.
The tool makes red teaming expertise accessible to developers and security engineers. You can run scans on your models and application endpoints either locally or in the cloud. This allows you to continuously test your AI systems for potential safety issues.
9. Mend.io AI Red Teaming Tool
Mend.io offers an AI red teaming solution built into its AppSec Platform. You get a single dashboard where you can run security tests designed specifically for AI applications.
The tool simulates real attacks on your AI systems to find weaknesses before they become problems. You can test how your AI actually behaves in different scenarios, not just how it should work in theory.
Mend.io focuses on conversational AI security. You can test your systems like a real user would and identify risks specific to your domain. The platform shows you dynamic AI insights that help you understand what’s happening with your security.
The red teaming features integrate directly with your existing AppSec strategy. You don’t need to use a separate tool or switch between different platforms. This makes it easier to include AI security testing in your regular workflow.
Only 13% of organizations feel ready to keep their AI systems safe, even though 72% use AI in their business. Mend.io aims to close this gap by making red teaming more accessible and practical for development teams.
10. SafeStack Red Teaming Tool
SafeStack offers a red teaming platform designed to help you test your AI systems for security vulnerabilities. The tool focuses on identifying weaknesses in your AI models through simulated attacks and threat scenarios.
You can use SafeStack to evaluate how your AI systems respond to various security challenges. The platform provides testing capabilities that target common AI vulnerabilities and helps you understand where your defences need improvement.
SafeStack’s approach involves running automated tests against your AI models to uncover potential security gaps. You get detailed feedback on how your systems handle different attack patterns. This information helps you make informed decisions about strengthening your AI security measures.
The tool supports testing workflows that fit into your existing development process. You can run assessments at different stages of your AI system development to catch vulnerabilities early. SafeStack aims to make red teaming more accessible for teams that need to validate their AI security posture but may not have extensive security expertise in-house.
Core concepts behind AI red teaming tools
AI red teaming tools operate on three fundamental principles: launching controlled attacks against AI systems, identifying weak points before real threats emerge, and maintaining ongoing security checks throughout an AI system’s lifecycle.
Simulated adversarial attacks
These tools launch controlled attacks against your AI systems to test their defences. You can use them to send malicious prompts, inject harmful data, or manipulate inputs in ways that might trick your AI into producing unsafe outputs.
The attacks mirror real-world threat patterns. Your red teaming tools might try to bypass content filters, extract sensitive training data, or cause your model to generate biased responses. They test scenarios like prompt injection, jailbreaking attempts, and data poisoning.
Common attack types include:
- Prompt manipulation – Crafting inputs to bypass safety guidelines
- Model extraction – Attempting to steal model weights or architecture
- Data poisoning – Injecting corrupted training examples
- Evasion attacks – Finding blind spots in your model’s decision boundaries
You run these simulations in a safe environment where failures help you improve rather than cause real damage.
Vulnerability discovery in AI systems
Red teaming tools scan for specific weaknesses in your AI applications. They identify where your model produces harmful content, leaks private information, or makes unfair decisions based on protected characteristics.
Your tools test against known vulnerability categories. These include hallucinations where your AI invents false information, bias issues that produce discriminatory outputs, and security gaps that expose sensitive data. The tools document each vulnerability with severity ratings and reproduction steps.
You receive detailed reports showing exactly which inputs triggered problems. This lets your team patch vulnerabilities before deployment or add guardrails to prevent exploitation in production systems.
Continuous security assessment
Your AI systems need ongoing testing because new vulnerabilities emerge as models interact with users. Red teaming tools automate repeat testing so you catch issues throughout your development and deployment cycles.
You can schedule automated scans that run daily or weekly. The tools track changes over time and alert you when new vulnerabilities appear after model updates or retraining. This continuous approach catches problems that only surface after your AI processes thousands of real-world interactions.
Your security posture improves through iteration. Each testing cycle builds on previous findings to explore deeper attack vectors and edge cases your team hadn’t considered.
Ethical and compliance implications
AI red teaming tools must operate within clear ethical boundaries while meeting regulatory requirements. Organizations need to balance aggressive security testing with respect for privacy, legal constraints, and industry standards.
Responsible testing practices
You need to establish clear rules of engagement before starting any red teaming activity. This means defining what systems you can test, what methods are acceptable, and when to stop if you discover sensitive information.
Your testing should never compromise real user data or system stability. Set up isolated environments that mirror production systems without exposing actual customer information. Document all testing activities so you can prove compliance if regulators ask questions later.
Key ethical principles include:
- Obtaining proper authorization before testing any system
- Avoiding tests that could harm individuals or communities
- Stopping immediately when you find critical vulnerabilities
- Reporting findings through appropriate channels
You must also consider the potential for bias in your testing approach. Red teams should include diverse perspectives to identify harms that might affect different user groups.
Alignment with industry standards
Your AI red teaming practices should align with frameworks like ISO 42001, which provides guidance for AI management systems. These standards help you demonstrate due diligence to regulators and customers.
Different industries have specific requirements you need to follow. Healthcare AI systems must comply with HIPAA regulations. Financial services need to meet requirements from banking regulators. Government contractors must follow federal security standards.
You should document how your red teaming processes map to relevant standards. This creates an audit trail showing your commitment to responsible AI development. Many organizations also pursue third-party certifications to validate their testing practices meet industry benchmarks.
Privacy considerations
Red teaming activities can expose sensitive training data or reveal personal information in model outputs. You must handle any discovered data according to privacy laws like GDPR or CCPA.
Your team needs data minimization protocols that limit what information gets collected during testing. Redact or anonymize any personal data in test reports. Store findings in secure systems with strict access controls.
Test specifically for data leakage vulnerabilities where models might reveal training data through carefully crafted prompts. This includes checking if the AI can be manipulated to expose proprietary information, trade secrets, or confidential user details. Your red teaming tools should flag these risks before deployment.
