Organizations need to deploy safe, reliable, and trustworthy AI models. However, malicious attacks and model misconfigurations can lead to harmful outputs, data exfiltration, bias, and more. While businesses may already have processes to test their networks, apps, and other digital assets, they also need to stress-test these AI models.
That’s where AI red teaming comes in. AI red teaming is a systematic process of employing expert teams — or "red teams"— to identify novel risks and vulnerabilities, test the limits, and enhance the security of artificial intelligence (AI) systems.
While red teaming is incredibly effective at spotting weaknesses in a model, organizations must still use the right tools and follow proper processes to maximize their security posture.
In this guide, we’ll explain how AI red teaming works for generative AI platforms and other AI systems, why it’s so valuable, and how to effortlessly integrate AI red teaming into your existing cyber security processes.
AI red teaming simulates adversarial attacks and stress-tests the model’s functionality under real-world conditions, going far beyond traditional penetration testing methods. Red teamers adopt the perspective of potential adversaries, probing for weaknesses that they could exploit.
Rooted in the principles of cybersecurity and adversarial resilience, this approach goes beyond traditional AppSec testing by mimicking dynamic, real-world threat scenarios.
AI red teaming is especially vital as AI systems become more integrated into high-stakes environments, such as financial systems, healthcare, hi-tech, autonomous vehicles, and critical infrastructure. Across industry, academia, and the public sector, red teaming methods, goals, and outputs vary widely, reflecting the diverse challenges posed by modern AI technologies.
But at its core, red teaming is about enlisting experts and empowering them with tools to simulate adversarial scenarios, uncover vulnerabilities, and inform comprehensive risk assessments.
AI is a powerful tool, but many organizations are unable to use it to its full potential because of security risks. Fortunately, there are many use cases for AI red teaming, which allows organizations to boost security and improve the quality of their models.
Does anyone in your organization use or build an AI model? Understanding the potential weaknesses of AI systems is crucial for ensuring their security and effectiveness.
AI red teaming helps organizations detect gaps and vulnerabilities in their models by simulating adversarial attacks and stress-testing system performance. This proactive approach not only uncovers hidden flaws but also provides actionable ways to mitigate risks before they can be exploited.
What would happen if an adversary targeted your AI system? AI red teaming enhances the ability of AI systems to withstand a variety of adversarial attacks, including data poisoning, model evasion, and system exploitation.
By exposing AI models to simulated threats, organizations can strengthen their defenses and improve their robustness under real-world conditions. This ensures their systems remain operational and reliable, even in hostile environments.
Are your AI systems compliant with industry standards and ethical guidelines? As regulatory frameworks for AI continue to evolve, organizations must ensure that their systems meet compliance requirements.
AI red teaming provides a structured way to evaluate whether AI models adhere to legal, ethical, and safety standards. This not only reduces the risk of non-compliance but also builds trust with employees and customers.
Are your AI models free from unintended biases? AI red teaming can uncover biases within training data or decision-making processes that may lead to unfair outcomes.
By simulating scenarios with diverse inputs, red teams can identify and address inequities, ensuring that AI systems operate in a fair and inclusive manner.
How does your AI system perform under extreme conditions? Red teams can simulate high-stress environments, such as unexpected surges in data volume or conflicting inputs, to test the system's performance limits. This testing approach helps organizations ensure their AI remains operational even during crises.
AI systems have access to a bevy of sensitive information that must remain under lock and key. Red teams can explore how AI systems, such as generative AI platforms and other AI solutions, handle personal or confidential data, identifying vulnerabilities in data handling, storage, and access. This also ensures compliance with privacy laws like GDPR or CCPA.
Intentional or not, malicious inputs could create misleading or harmful outputs from your AI system. AI red teaming tests scenarios where human users interact with AI, evaluating risks such as misinformation, harmful advice, or unintuitive interfaces. This ensures safer, more transparent interactions.
AI red teaming is a superior testing model because it accounts for the nuances of your organization and industry. What unique threats does your deployment environment pose?
Red teams customize threat models based on industry-specific risks, such as financial fraud in banking AI or life-critical errors in healthcare AI.
AI systems must be secure at all connection points to prevent unauthorized access. However, AI systems rarely operate in isolation, which could open your model up to security issues.
Red teams test the security of integrations with APIs, databases, and third-party software to identify vulnerabilities that could compromise the entire system.
Can adversaries manipulate your AI model? Red teams simulate adversarial attacks, such as perturbation-based evasion or poisoning, to test and strengthen defenses against AML threats.
These ten use cases show just how flexible and valuable AI red teaming can be. This process addresses emerging challenges in various industries and use cases, so regardless of how you use AI, this testing approach will work for your organization. By proactively identifying risks, organizations can build AI systems that are not only functional but also secure, ethical, and reliable.
The table below provides an overview of the various red teaming use cases and their benefits.
Interest in AI red teaming has expanded rapidly, driven by the increasing adoption of AI systems. According to Business Research and Insights, the global cybersecurity, red teaming, and penetration testing market was $149.50 billion in 2023 and is projected to reach $423.67 billion by 2032, at a CAGR of 12.27% during the forecast period.
Research from Meta, Google, OpenAI, Anthropic, MITRE, and others contribute to the growing body of knowledge on AI red teaming, making it easier to implement than ever before. These organizations offer frameworks and helpful insights to guide all industries in identifying and mitigating the risks associated with AI systems.
It’s no wonder why AI red teaming is expected to become a significant portion of the cyber security market, which was valued at around $22.4 billion in 2023 and is expected to grow rapidly with a CAGR of 21.9%. This growth is largely driven by several key trends.
The rapid expansion of AI is evident in the significant growth of AI models and the developer community. Research shows that the number of foundational models has doubled yearly since 2022.
This surge is paralleled by the increasing number of developers engaging with AI platforms. For instance, the Hugging Face Hub hosts hundreds of thousands of model repositories.
Initial AI use cases were for simple tasks like image generation and content creation, but today’s AI solutions are far more complex. Businesses in sensitive industries now use AI to manage mission-critical information and tasks.
As organizations increasingly deploy AI in high-stakes environments like healthcare, autonomous vehicles, and financial systems, there’s a greater need for rigorous testing to ensure safety and reliability.
The use of AI is on the rise, as well as the number of attacks targeting AI models. Not only that, but adversarial attacks, such as data poisoning and model evasion, are becoming more sophisticated.
Organizations recognize the importance of preemptive testing to safeguard their AI systems against such threats, fueling investment in AI red teaming.
Governments and regulatory bodies are increasingly introducing frameworks and guidelines, such as the European AI Act, for AI security, fairness, and transparency. While AI safety may have been a best practice in the past, it will now be a regulatory requirement with increasingly steep penalties.
Forward-thinking organizations are investing in AI red teaming today to strengthen their models before regulations come into play.
The need to build trust in AI systems, particularly those that influence public decisions (such as generative AI) or handle sensitive data, drives organizations to adopt red teaming. Organizations can enhance their credibility by addressing biases, ensuring ethical behavior, and demonstrating transparency.
AI red teaming tools are also evolving to keep up with the quality of AI technology—including generative AI platforms and other AI systems—and the sophistication of modern threats. Innovations in vulnerability detection and penetration testing tools, such as Garak and PyRIT, are lowering the barriers to entry and enhancing the effectiveness of AI red teaming.
This advancement makes AI red teaming accessible to businesses of all sizes and industries, helping organizations tighten up AI security without internal red teaming resources.
With the rapid evolution of threat landscapes, such as generative adversarial networks (GANs) and large-scale AI model manipulation, organizations are investing in proactive defenses. Red teaming enables them to stay ahead of emerging risks.
While AI red teaming tools are more advanced today than ever, many organizations are turning to external red teaming experts for unbiased evaluations of their AI systems. This trend is particularly pronounced in industries with high accountability, such as defense and finance.
Like in other areas of cybersecurity, we recognize that there is a shortage of AI security talent, and we’re here to fill that gap. Mindgard’s Offensive Security solution delivers continuous security testing and automated AI red teaming across the AI lifecycle, saving our customers time and money and providing empirical evidence of AI risk to the business for reporting and compliance purposes.
Regardless of the approach, AI red teaming involves a structured process comprising multiple stages and various techniques to evaluate an AI system’s security and resilience.
There are several methodologies to choose from when planning an AI red teaming exercise. Several methods have emerged to address the complexities of testing and securing AI systems.
This method uses human expertise to craft prompts and interact directly with AI models. Manual testing is great at conducting adversarial scenarios to uncover risks.
Analysts evaluate outputs based on specific criteria, such as risk type, severity, effectiveness, or deviations from baseline behavior. This hands-on approach is particularly effective for identifying more nuanced vulnerabilities.
Automated systems leverage AI and pre-defined rules to generate adversarial inputs, simulating attacks at scale. Classifiers or other evaluation algorithms are often employed to assess outputs against predefined benchmarks.
The downside to this approach is that it could miss out on more creative malicious inputs from human attackers.
Combining manual and automated methods—”human in the loop” approaches—can provide a more comprehensive testing framework. For instance, a red team might manually develop an initial set of adversarial prompts and then use automation to scale these into larger datasets.
This approach balances the depth of manual insights with the efficiency of automated testing.
The table below compares manual, automated, and hybrid red teaming methodologies and their advantages and disadvantages.
The choice between manual, automated, or hybrid approaches depends on the resources available and the specific vulnerabilities you want to test. Regardless of the method, having a well-trained red team is essential to executing these strategies effectively.
Proper red team training ensures that your team members are equipped with the right skills to simulate advanced adversarial attacks and identify vulnerabilities in AI systems. Organizations looking to strengthen their red team capabilities may benefit from comprehensive training programs that provide hands-on experience with various attack techniques and testing tools.
After selecting a methodology, determine the scope of the red teaming exercise. During this step, the AI red team will:
For a comprehensive framework to guide your red teaming efforts, check out our Complete Red Teaming Checklist. This interactive checklist offers a structured approach to ensure all critical aspects are thoroughly covered.
Next, the team will create scenarios that mimic real-world adversarial behavior, such as:
Red teaming often overlaps with other testing methodologies, such as Breach and Attack Simulation (BAS), but differs in the level of customization and the specific threats it aims to address.
While BAS focuses on automated, repeatable attacks to test specific vulnerabilities, red teaming delves deeper into simulating complex, evolving adversarial behaviors.
For example, BAS tools can sometimes complement red teaming exercises by providing real-time simulations of common attack vectors, making them an essential part of a broader cybersecurity strategy.
After deciding on a plan of attack, the red team begins the testing phase. During this step, they execute the predefined scenarios using techniques such as periodic penetration testing, continuous AI pentesting, attack simulation, and sandbox environments.
The red team will monitor the system’s behavior under adversarial stress to measure robustness and response effectiveness.
To assist in enhancing your AI security, you may also want to explore specialized tools and processes that focus on securing specific types of AI systems, such as chatbots.
Some AI red teaming exercises last weeks or months, depending on the scope outlined in step two. After the exercise, the red team will document their findings, including:
Understanding how to effectively measure the results of a red teaming assessment is essential for evaluating its success and identifying areas for improvement. Several metrics, such as the number of vulnerabilities discovered, the severity of threats, and the potential impact on the system, can help organizations assess the value of the exercise.
Some red teams simply identify risks and offer suggestions for mitigating them, while others will help the organization fix the identified issues. With this step, the red team might also conduct follow-up tests to ensure the effectiveness of these fixes.
Manual AI red teaming offers the benefit of human creativity, while automated tools make it possible to red team at scale. Regardless of your chosen approach, the right tools make all the difference.
In fact, most AI red teaming tools support manual, automated, and hybrid options, allowing you to red team however you see fit.
Some of the most popular AI red teaming tools are:
The table below highlights some of the most popular tools used in red teaming, their functionality, and use cases.
Red teaming tools can’t replace the expertise of human red teamers, but they help organizations speed up, streamline, and maximize the value of the process. These tools cover every aspect of the process, from reconnaissance to common exploits to bypassing tools.
For a comprehensive overview of leading penetration testing service providers that can assist in enhancing your AI security measures, refer to our guide on the top pentesting service providers. This guide offers detailed insights into various providers and can help you select the most suitable partner for your organization’s needs.
Every AI model is different, but malicious threats are a constant for any organization investing in AI. These AI red teaming examples show just how valuable red teaming can be in the new era of AI-first attacks.
OpenAI's red team identified that their generative AI model could be misled into generating biased or harmful content when prompted with highly charged social or political issues. In response, OpenAI initially implemented content warnings to flag potentially harmful responses before they were delivered to users.
However, OpenAI has recently revised this approach, removing certain content warnings that users found frustrating, especially when dealing with nuanced topics. Despite this change, OpenAI continues to monitor and mitigate potential biases by refining their model's ability to identify harmful or biased content without hindering the user experience.
Microsoft’s AI red teaming crew tested a vision language model (VLM), which was crucial for ensuring their generative AI model wouldn’t create illegal or harmful images. Microsoft’s red team soon realized that image inputs were much more vulnerable to jailbreaks than typical text-based inputs.
As a result, Microsoft switched to system-level attacks to better mimic real adversaries that would have no issue using other GenAI inputs, like images, to jailbreak the model.
Nefarious parties use creative methods to manipulate AI models. Anthropic shows just how important it is to think like an attacker, explaining that its AI red team also tests in multiple languages and cultural contexts.
Instead of relying on translations, Anthropic works with on-the-ground experts to fix the understanding of its AI, Claude, of non-US contexts.
Meta's red teaming processes have been instrumental in detecting and addressing critical vulnerabilities. For example, a significant flaw, designated as CVE-2024-50050, was discovered in the Llama framework, which could have allowed remote code execution. Upon identification, Meta promptly patched the vulnerability and released updated versions to safeguard users and maintain the integrity of its AI systems.
Google’s red team discovered that their model could be easily manipulated through adversarial examples in specific training scenarios, which could lead to incorrect predictions or biased outputs.
To mitigate this risk, Google implemented new defenses, such as adversarial training techniques, to strengthen the model’s robustness against such attacks. This continuous testing process highlights the importance of red teaming in maintaining the trustworthiness and security of AI systems as they scale.
AI red teaming comes with many benefits but isn’t without challenges. Organizations should plan for these common obstacles to see value from their investment in red teaming.
Addressing the physical security aspect of AI systems is a growing concern, especially as AI becomes integrated with hardware and physical environments. In some cases, AI systems aren't just vulnerable in the digital realm; physical access can also present risks, such as tampering with AI models or exploiting hardware vulnerabilities.
As AI security strategies evolve, physical red teaming is emerging as a crucial aspect of testing for physical vulnerabilities, complementing traditional cybersecurity assessments. These physical security measures ensure that AI models and their surrounding environments are protected against more direct, physical attacks.
One of the most significant challenges is the absence of standardized methodologies for AI red teaming. Organizations and researchers often employ divergent approaches, making it difficult to compare results or establish benchmarks for AI safety.
The lack of universally accepted frameworks also hinders collaboration and knowledge sharing across the industry. However, organizations can overcome this issue by following frameworks established by leaders in AI red teaming, like the Cybersecurity and Infrastructure Security Agency (CISA).
Modern AI systems, particularly large language models and multimodal systems, operate as “black boxes,” with intricate architectures and opaque decision-making processes. Understanding and effectively testing these systems requires specialized expertise, substantial resources, and innovative tools capable of uncovering vulnerabilities at both the model and system levels.
Fortunately, organizations don’t need internal resources to test complex AI models. Outsourcing to experts like Mindgard makes it possible to improve AI safety without investing in an internal red team.
The rapid evolution of adversarial attack techniques poses a persistent challenge. New methods, such as data poisoning, adversarial perturbations, and model evasion attacks, are continually emerging, requiring red teams to stay ahead of malicious actors.
Additionally, frontier risks, such as autonomous misuse and synthetic content generation, demand novel red teaming strategies.
While manual red teaming is effective for nuanced vulnerabilities, it is resource-intensive and lacks scalability. Automated red teaming provides scalability but may miss subtle issues that human testers can identify.
A hybrid approach is often necessary to strike the right balance between automation and human expertise.
However, this balance is further complicated by the shortage of skilled professionals with the specialized knowledge needed to conduct effective red teaming exercises. The rapidly evolving landscape of AI threats demands experts who are not only well-versed in AI security but also proficient in emerging attack techniques.
Organizations facing these challenges can benefit from partnering with experienced solution providers like Mindgard, which can offer both scalable, automated red teaming and access to skilled professionals. By leveraging Mindgard’s expertise, organizations can ensure they are effectively identifying vulnerabilities without overextending their resources.
To learn more about key figures in the AI security community, check out our article on people to know in AI security and AI red teaming, where we highlight the top experts driving innovation in this field.
AI systems process multiple data modalities, including images, text, and audio. Because of this, testing their behavior across these inputs is becoming more complex.
Multimodal red teaming requires integrating domain-specific expertise and advanced tools to uncover vulnerabilities unique to cross-modal interactions.
While these AI red teaming challenges can hinder progress, they aren’t impossible to overcome. Addressing these challenges requires collaboration across academia, industry, and policymakers to develop standardized methodologies, scalable tools, and a robust ecosystem for AI red teaming.
AI red teaming is a must for ensuring the safe, ethical, and compliant use of generative AI and other AI systems in your organization. While it’s possible to red team internally, it requires time and resources that growing businesses might not have. That’s why organizations lean on Mindgard for specialized testing.
At Mindgard, we specialize in helping organizations secure their AI systems through advanced red teaming practices. Our Offensive Security for AI protects AI systems from new threats that can only be detected in an instantiated model and that traditional application security tools cannot address.
Contact us today to learn about our AI red teaming services and how we can help safeguard your AI systems.
A red team is a group of human experts who simulate adversarial attacks against AI models. The goal is to identify vulnerabilities and potential security risks that hackers and other malicious parties might use to exfiltrate data or cause harm.
Regular AI red teaming ensures AI systems are safe and ethical long before deployment.
Large language models need red teaming to assess their security, fairness, and reliability. LLMs create a lot of content for many uses, which makes them particularly susceptible to malicious attacks and biases.
LLM developers thoroughly red team them before deployment to improve safety and prevent bad actors from exploiting any weaknesses in real-world applications.
The White House’s AI Executive Order (EO 14110) mandates red teaming—adversarial testing to uncover vulnerabilities—as a core requirement for high-risk AI systems. It specifically targets dual-use foundation models (e.g., advanced LLMs), requiring developers to share red-team safety results with the government before deployment.
The order also tasks NIST with standardizing red-teaming practices, including risk evaluation for cybersecurity, bias, and misuse, while federal agencies must conduct tests on AI used in critical infrastructure and national security.
In intelligence, red teaming is the practice of using independent groups to challenge assumptions, identify weaknesses, and simulate threats. This approach is widely used in military, cybersecurity, and AI development to enhance decision-making and security.
