AI red teaming is adversarial testing that uses human red teams with automated tooling to attack AI systems (via jailbreaks, prompt injection, data poisoning, model extraction and agentic misuse) and reveal failures before bad actors do. Red teaming AI grew out of cybersecurity red teaming and adapts classic penetration testing to the failure modes of large language models (LLMs), multimodal AI systems and autonomous AI agents.
AI red teams attempt to break your models in every way possible: tricking the model into harmful outputs, leaking private data and bypassing safety guardrails.
Frameworks guiding AI red teaming practices include:
Attacks are already happening: 1 in 4 malicious breaches is now AI-enabled (up 56% year over year) and breaches involving AI cost an average of $6 million against a $4.99 million global average. More than 20% of organizations reported breaches targeting their AI models or applications. (IBM Cost of a Data Breach Report 2026, covering 602 breached organizations studied between March 2025 and February 2026, conducted by Ponemon Institute.)
Microsoft established an internal AI Red Team that, between 2024 and 2025, attacked over 100 generative AI products. They concluded prompt/script attacks combined with fuzzing are more effective than ML evasion attacks at breaking AI systems, meaning every organization deploying AI should have a red-teaming program. In July 2026 Microsoft extended that program worldwide, launching the External Red Team Alliance (EXTRA) with unrestricted gifts to 18 university labs across six continents.
This 2026 guide covers how AI red teaming works for generative AI and agentic systems, the methods and process red teams use, the full tool landscape (enterprise platforms, open-source scanners and government frameworks) and real-world case studies from OpenAI, Microsoft, Anthropic, Meta and Google. Additionally, this guide also shows how to operationalize continuous adversarial testing inside your own AI stack.
AI red teaming aims to replicate attacks and stress-test how the model behaves in realistic conditions. It’s much more involved than your traditional pentesting process. Red teamers think like adversaries to discover vulnerabilities.
Based in cybersecurity and adversarial robustness, red teaming expands upon standard AppSec testing to emulate real-world attack scenarios that are constantly evolving.
As AI technology becomes more prevalent across critical systems like finance, healthcare, hi-tech, automotive and critical infrastructure, red teaming AI becomes increasingly important. Red teaming practices, objectives, and deliverables can vary greatly between industry, university and government organizations.
However, red teaming always involves orchestrating security specialists and providing them with the tools needed to think and act like an adversary to build out attack scenarios and surface vulnerabilities for use in your risk assessments.
“Good governance starts with support for open access to data and models, to enable independent research and audit of AI systems."
- Dr. Rumman Chowdhury, Founder and CEO of Humane Intelligence (Source)
The red team researches potential attacks against the AI model. Red teaming involves using adversarial prompts, injection attacks, jailbreaks, multimodal exploits (basically anything a malicious actor could use to attack the model) to find weaknesses. Red teamers think like attackers. Their goal is to break your model before someone else does.
The blue team is responsible for defending the model. This includes building and maintaining the controls that keep your model secure (writing input validation and output filtering, configuring guardrails, monitoring pipelines, etc.). In the case of AI systems, blue teams must also triage and respond to red team findings: they must remediate vulnerabilities and then re-test to confirm the defenses are effective.
Purple teams are a partnership of blue and red. Purple teaming is a practice where your red and blue teams operate in tandem: red teams disclose techniques and findings throughout the testing process, while blue teams ingest that information and use it to improve defenses. Purple teaming is especially important for AI. Because every time you update the model, you also expand the attack surface.
AI is extremely powerful, yet many companies aren’t able to use it because of potential vulnerabilities. That’s why there are so many AI red teaming use cases: it allows you to increase security while improving your models.
How do you know where you can improve without knowing your weaknesses? Here are just a few examples of how you can use AI red teaming.
Do you (or anyone in your organization) use AI or create AI models? If so, you should know where your models could be vulnerable. AI models pose many different security risks if not thoroughly examined.
Using red teaming, you can discover where your models have gaps and prevent issues before they occur. Red teams can attack your system to find vulnerabilities you didn’t know existed and show you how to prevent them.
Imagine if one of your adversaries got their hands on your AI system. Would it be able to handle different attack scenarios? Improving an AI system’s resiliency allows it to better handle multiple types of attacks.
Through red teaming, you can expose your model to different adversarial attacks like data poisoning, model evasion, and more. You can then test to make sure your model can operate as intended even when under attack.
Do your AI systems align with industry regulations and ethical guidelines? Regulatory standards and compliance requirements around AI are quickly developing.
AI red teaming can help you test that your AI models follow legal, ethical, and safety requirements and recommended standards. This helps mitigate risk and fosters trust within your employees and customers.
Do your AI models operate without unintended bias? The inputs used to train AI models or the decision-making process itself can unintentionally create bias, resulting in unfair decisions.
Red teams can run tests using different inputs to determine if there are inequities in your models.
Under what conditions will your AI system break? Red teams can try to overload your AI solution with high data volumes or conflicting inputs in order to measure how it will perform when under stress.
These tests can help reveal how your organization’s AI will perform if something unforeseen occurs.
From customer PII to private company information, AI systems can access a lot of sensitive information. Ensure that your AI applications, such as generative AI platforms and other AI solutions, properly protect any private data it may come in contact with.
Red teams can help you identify weaknesses with how your AI stores, shares, and accesses personal/private data. You can also check that your AI technology complies with privacy regulations such as GDPR or CCPA.
Malicious actors (or even honest users) may supply your AI system with input that leads to dangerous or deceptive output. AI red teaming exercises test for human-AI interactions to assess potential dangers like mis/disinformation, harmful guidance, or a confusing user experience. AI red teaming helps create safer and more transparent human-AI experiences.
A generic testing model won’t cut it. AI red teaming is unique in that it takes into consideration the specifics of your company and your industry. How will your intended deployment create unique attack vectors?
Red teams will perform threat modeling that’s specific to your industry. If you work in finance, we’ll think about how AI can be manipulated to engage in financial fraud. If you’re in healthcare, we’ll look at ways that AI can make life or death mistakes.
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.
Adversarial machine learning attacks target the model itself rather than the systems around it. NIST AI 100-2 E2025 classifies them into evasion (inputs crafted to force wrong outputs), poisoning (corrupting training or fine-tuning data), extraction (stealing model weights or training data) and inference attacks (working out whether specific records were in the training set).
AML defense testing runs each attack class against your model and measures whether accuracy, privacy and output controls hold. It is the red-team check that your model-level defenses (adversarial training, input sanitization, differential privacy) actually work under attack, not just on paper.
Interest around AI red teaming has surged as AI systems move into production. The AI red teaming services market grew from $1.75 billion in 2025 to $2.26 billion in 2026 (a 28.8% jump) and is projected to reach $6.17 billion by 2030 (The Business Research Company, 2026). While the entire cybersecurity market exceeds $200 billion, AI red teaming services remain one of its fastest-growing segments.
Research from Meta, Google, OpenAI, Anthropic, MITRE and others fuels continuous advancements in the field. Published research from these organizations makes it easier than ever to assess your AI risk with custom frameworks and tooling to identify and mitigate the risks posed by your AI systems across industries.
Eight major trends are fueling AI red teaming growth.
AI growth can be measured by looking at metrics like how many AI models exist today compared to previous years. There are 2.96 million public model repositories on the Hugging Face Hub as of August 2026, up from 2.43 million in January (Hugging Face, State of Open Models: Summer 2026). The platform’s acceleration was first documented in “Anatomy of a Machine Learning Ecosystem” (arXiv 2508.06811): the second million uploads took 335 days against over 1,000 days for the first. Along with a boom in AI models came an increase in developers working with AI.
Early AI use cases were limited to generating images and basic writing tasks, but today’s solutions do far more complex work. Organizations trust AI to handle critical data and functions within industries such as healthcare.
When AI is put into high-risk production scenarios, like self-driving cars or healthcare software, it needs to be thoroughly tested to ensure it can’t cause any harm.
AI adoption continues to grow exponentially across industries as does the number of attacks against AI models. Additionally, attacks are becoming more sophisticated. Adversarial attacks like data poisoning and model evasion are on the rise.
Companies understand the importance of testing their AI systems before an attack occurs. This awareness is driving demand for AI red teaming.
The EU AI Act is no longer pending. Obligations for general-purpose AI (GPAI) models applied on August 2, 2025. The bulk of high-risk system obligations began applying on August 2, 2026.
Article 55 requires providers of general-purpose AI models with systemic risk to perform model evaluations, including conducting and documenting adversarial testing such as red teaming. Penalties reach up to 35 million euros or 7% of global annual turnover for the most serious violations.
Organizations are getting ahead of enforcement by hardening their AI systems with AI red teaming before regulators or attackers find the gaps.
Organizations are red teaming AI technology to gain trust in their systems. There is particular demand for trustworthy AI that powers decisions affecting the public (generative AI) or uses personal data. Removing bias, improving ethical behavior, and proving transparency all improve public trust in AI.
AI red teaming tools need to improve as well to match both the improved quality of AI tech (generative AI tools, other software that uses AI) and increasingly sophisticated threat actors. As vulnerability detection and penetration testing tools become more advanced (look at Garak and PyRIT), it’s becoming easier to effectively red team AI.
Smaller businesses and enterprises across industries can now perform AI red teaming to improve their AI security posture even if they don’t have dedicated internal red teaming resources.
The cost of an AI breach is significant. Based on IBM’s Cost of a Data Breach Report 2026, breaches involving AI cost an average of $6 million, a full million above the $4.99 million global average.
One in four malicious breaches is now AI-enabled, a 56% increase year over year. More than 20% of organizations reported breaches targeting their AI models or applications.
Red teaming helps decrease that cost. Organizations using AI and automation in their security operations cut breach costs by $2 million on average through faster discovery, reduced downtime and limited exposure of sensitive data.
The dollar amount it will cost you to identify a weakness via a red team exercise prior to deployment is only a fraction of what remediation and disclosure will cost your organization post deployment if that weakness is exploited. And that’s without factoring in the reputational costs and regulatory risk.
A manual AI red teaming assessment is scoped and priced like a specialist penetration test: a defined testing window, a target list and a findings report. Labor is the main cost driver.
AI red teamers in the United States bill $59 to $84 per hour (ZipRecruiter, August 2026), so a two-to-six week manual engagement typically lands between the low five figures and the low six figures depending on team size and scope. Automated AI red teaming platforms compress that cost by running continuous attack suites against every model release; platform pricing varies by vendor and model count.
The investment is small against the downside: AI-enabled breaches cost an average of $6 million (IBM, 2026) and how you measure a red teaming assessment determines how much of that risk you actually retire.
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.
No matter which method is chosen, AI red teaming will generally follow a framework that consists of multiple steps and incorporates numerous tactics in order to assess an AI system.
When deciding how you will conduct your AI red teaming exercise, there are multiple options you can choose from. Many methodologies have been developed to help security professionals test and secure their AI implementations.
Manual Testing
Manual testing leverages human intelligence to create prompts and directly query AI models. Manual red teaming excels at performing adversarial attacks to identify potential threats.
Red team members will score results according to desired metrics. For example, you may rank by risk type or severity, effectiveness, deviation from baseline, etc.
Automated Testing
Automated approaches can utilize AI and predefined rules to create adversarial inputs at scale. Classification systems or other algorithms can be used to validate outputs as well.
The downside to this approach is that it could miss out on more creative malicious inputs from human attackers.
Human in the Loop Approaches
Hybrid approaches that incorporate manual testing along with automated testing (“human in the loop”) can also be valuable. For example, a red team might manually create a small set of adversarial prompts, then use automation to help expand those prompts into a larger dataset.
This approach balances the depth of manual insights with the efficiency of automated testing.
Below is a table outlining the strengths and weaknesses of manual, automated, and hybrid red teaming approaches.
Choosing manual, automated or a combination of both depends on your resources and what vulnerabilities you’re testing. Whatever your poison may be, you will need the appropriate skillset within your red team to carry out these strategies.
Through proper red team training, you can ensure your employees have the skills needed to conduct realistic emulation of sophisticated attacker campaigns and detect exploitable weaknesses within your AI systems. There are full red team training courses that can give your team hands-on practice with different attack methods and testing tools.
Next, you’ll want to scope the red teaming exercise based on your chosen methodology. During this phase, your AI red team will:
When scoping and planning your red teaming activities, consider how they fit into your existing AI Security Posture Management (AI-SPM) efforts. Maybe your posture management will inventory all your AI assets so you know what to red team. Perhaps you’ll use it to define risk thresholds or risk scoring, and track policies. Aligning your red teaming to your AI-SPM can ensure you’re attacking your most critical pieces of AI, that it fits into your larger risk prioritization, and that you track findings from red teaming efforts for the long term.
Learn more about the critical tasks for each phase of your red teaming efforts with our Complete Red Teaming Checklist.
After scoping and planning, the team will develop threat scenarios that are representative of real-world attacks such as:
Red teaming can sometimes overlap with other forms of testing such as Breach and Attack Simulation (BAS) testing. However, red teaming allows for more customization around your specific set of threats.
BAS testing is typically used for predefined, repeatable attacks. Red teaming can simulate adaptive adversaries.
Just as important is verifying that defensive controls work. After red teams learn some of the methods attackers will use to jailbreak an LLM, they need to test to make sure a system’s defenses actually hold up when faced with an attack. Common controls include:
Each of these controls should ideally be tested independently by red teams. But red teams can learn a lot by testing how these controls work together as well. For example, having a hardened system prompt won’t do you much good if your output filtering can be bypassed with creative encoding.
With an agreed upon plan of attack in place, the red team now moves on to testing. At this phase they will carry out their planned scenarios through periodic penetration testing, continuous AI pentesting, attack simulation and sandboxing.
The red team will then observe how your system behaves when under attack in order to identify its robustness and response.
You may also want to consider special tools and procedures that help secure certain types of AI like chatbots.
AI red teaming exercises can take weeks or even months depending on the scope created in step two. Once complete, the red team will provide a report of findings, which will include:
Knowing how to measure your red teaming assessment will help you identify where you were successful and where you can improve. There are many different metrics you should look at such as how many vulnerabilities were found. Also, identifying the severity of the threats found and how they can impact your system.
Some red teams will find risk and provide recommendations on how to remediate them. Others will assist the organization with remediating discovered issues. With this step, the red team may also perform retests to validate the fixes.
"An AI incident can manifest as a reliability issue, a safety concern, or a complex combination of both. In AI incident response, recognizing these subtle yet important differences is useful. The goal is not just to respond to incidents, but to build AI systems that are both dependably performant and fundamentally safe across diverse operational environments."
- Heather Frase, PhD, Senior Fellow at Georgetown’s Center for Security and Emerging Technology (CSET) (Source)
The OWASP Top 10 for LLM Applications (2025) is currently the most popular framework for identifying areas where AI will fail when under attack from an adversary. In the table below, we’ve mapped each risk to the red teaming technique used to identify it and the coverage provided by Mindgard.
OWASP risks will often be chained together in practice. A user might deceive an LLM into prompt injection (LLM01) to make it perform an unauthorized tool call (LLM06), revealing a sensitive system prompt (LLM07) in the same turn of conversation. Tests should attempt to facilitate chaining attacks together, not just identify individual attack vectors.
To red team AI agents and agentic systems, you attack what the agent can do, not just what it says. AI agents call tools, browse the web, read files and send messages, so agent red teaming tests for unauthorized tool calls, indirect prompt injection attacks planted in the content an agent reads, memory poisoning, multi-agent collusion and excessive agency (OWASP LLM06). The attack surface is measurable: the UK AISI and Gray Swan agent red teaming challenge surfaced more than 60,000 policy violations across frontier agent systems.
Start by mapping every tool and data source your agent can reach, then test each one as an injection point.
The AI red teaming tool landscape has evolved rapidly over the past few years. Broadly, today’s AI red teaming tools can be categorized into three tiers: Enterprise platforms designed for continuous production use, open-source libraries designed for flexibility and customizability, and industry-wide frameworks and standards which all testing should be mapped against.
Commercial platforms like Mindgard, HiddenLayer, Protect AI Recon, Lakera Red, Robust Intelligence, and CalypsoAI are designed for teams that need to operate red teaming at scale. Tools at this tier, like Mindgard’s Runtime Threat Detection & Response offering, perform holistic, real-time testing of LLMs at runtime rather than as a point-in-time exercise.
HiddenLayer’s AISec platform specializes in purpose-built ML model protection and threat intel. Protect AI Recon focuses on model scanning, MBOMs (bill of materials for ML), and governance. Most of these solutions are built with enterprise-class model inventory management in mind and focus on tight integration to existing security workflows, processes around audit ready outputs, and automated coverage as models are updated or replaced.
Open source resources are great options if you are a red teamer who needs flexibility around attacking certain vector categories, or if you simply need to focus on budgetary constraints. NVIDIA has an open source LLM vulnerability scanner called Garak which allows for coverage across many attack categories via programmatic, systematic vulnerability scanning. Libraries like PromptFoo (acquired by OpenAI) and DeepEval are great options if you’re looking for LLM evaluation tools that can plug into developers’ CI/CD pipelines.
PyRIT is Microsoft’s Python-based orchestration tool that’s designed specifically for AI risk identification. If you’re also concerned with traditional ML attacks or working with pre-LLM models, libraries like TextAttack or IBM’s Adversarial Robustness Toolbox cover these older attack categories. The biggest downside to these tools is the added cost of maintenance and integration. Commercial platforms have built these proprietary integrations into their product. Open source tools will not, and will require extra config work.
The third category is frameworks and standards. These resources don’t actually help you run red team exercises, but rather provide taxonomies and benchmarks that your testing should strive to cover. OWASP’s LLM Top 10 (2025), which identifies prompt injection as the top threat to LLM implementations, is the standard vulnerability classification scheme for LLM red teaming. Organizations like MITRE have expanded on this work with their ATLAS™ framework, which helps organizations map adversarial threats to AI and ML infrastructure just as ATT&CK® does for traditional OS and network infrastructure.
The National Institute of Standards and Technology (NIST) also released an updated attack taxonomy for ML via their AI 100-2 E2025 (March 2025) publication. As industries move toward standardization around emerging risks, NIST has established the GenAI profile of their AI Risk Management Framework via AI 600-1. Humane Intelligence allows the community to perform public red team exercises and share findings. The AI Risk and Vulnerability Alliance’s AI Vulnerability Database acts as a community-driven registry for vulnerabilities. Lastly, there are red teaming competitions like DEF CON GRT / AISI that help organizations learn and test their skills while contributing to the greater red teaming knowledge base.
AI RED TEAMING TOOL LANDSCAPE · Q3 2026
Commercial platforms, open-source scanners and industry frameworks for adversarial testing of AI systems. Filter by tier, search any tool.
| TOOL | TIER | BEST FOR | KEY STRENGTH | OSS | PRICING |
|---|
Sources: vendor product pages; OWASP LLM Top 10 (2025); NIST AI 100-2 E2025 (Mar 2025); NIST AI 600-1 (Jul 2024); UK AISI x Gray Swan challenge results. Compiled Aug 2026 for mindgard.ai.
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.
Our list of top pentesting service providers can help you find the right partner to help secure your AI.
All AI models have different capabilities, but no organization leveraging AI will ever be free from malicious threats. Here are some examples of AI red teaming that demonstrate how important red teaming will be during this age of AI-first attacks.
OpenAI's red team discovered that you can trick their generative AI model into producing biased or harmful outputs by prompting it about extremely charged social/political topics. Originally, OpenAI worked around this by putting content warnings on information they feared may be harmful before sending it to the user.
OpenAI has since changed this practice, removing some content warnings that users found to be annoying when discussing certain topics. OpenAI has been working to circumvent bias in AI by continuing to catch potentially harmful or biased information that the model may produce and fix the way the model detects such information without inconveniencing the user.
The OpenAI Red Teaming Network includes over 200 external experts from a variety of fields such as chemistry, biosecurity, cybersecurity, law and multilingual testers who joined since 2023. The external reviewers contribute to model pre-release testing, most recently with GPT-5 and their o-series of reasoning models.
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.
Microsoft’s AI Red Team published “Lessons from Red Teaming 100 Generative AI Products” in January 2025 (arXiv 2501.07238). The team’s top finding: attackers “don’t have to compute gradients to break an AI system” and instead lean on simple, practical methods like hand-crafted prompts and fuzzing.
Microsoft frames external testing as the only way to cover the contexts an internal team cannot:
“Managing frontier AI risk requires more than internal safeguards. It requires continuous engagement with experts who understand how these systems behave across different technical, linguistic, and cultural contexts.”
- (Natasha Crampton, Chief Responsible AI Officer, Microsoft. Microsoft Security Blog, July 2026)
That is the same logic behind crowd-powered testing programs like DEF CON GRT and the UK AISI challenges covered below.
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.
Anthropic’s red team language work is included in its 2024 blog post, “Challenges in Red Teaming AI Systems,” as well as its policy-level red team work (e.g., the CBRN uplift evaluations published with UK AISI in 2025), which covers gaps in Claude’s cultural and linguistic knowledge, including prompts in languages other than English.
Meta’s AI red teaming helped identify a previously unknown deserialization vulnerability, CVE-2024-50050 (CVSS 9.3, Snyk 2024), in Llama Stack that was patched within weeks of discovery. Meta’s red teaming efforts also helped shape the Llama 4 pre-release test pilot program in 2025 to run red teaming tests such as prompt injection, tool-use abuse, and policy evasion attempts before any models are released to the public.
Google’s red team found that they were able to trick the model into making incorrect predictions or biased outputs with adversarial examples under certain training conditions.
By adding additional defenses like adversarial training techniques, Google was able to improve the overall resilience of the model to these attacks. Finding vulnerabilities like this and stopping them before production is critical to scaling trustworthy AI, and red teaming helps ensure that happens.
Google's AI Red Team published its “Secure AI Framework (SAIF) Red Team Guide” in 2024. Google maintains continuous red teaming practices across both Gemini and Vertex AI deployments. Release notes from Gemini 2.0 and 2.5 cite adversarial red teaming against multimodal prompt injection as a blocking pre-release gate.
Of course, while AI red teaming can offer organizations many advantages, it is not without hurdles. Below are some common challenges associated with red teaming AI that companies should prepare to address if they want to realize a return on their red teaming investments.
Physical security is one aspect of AI systems red teams will need to start thinking about more. As AI technology develops and continues to integrate software with hardware and real-world environments, there will be more instances where simply hacking an AI system through digital means won’t be the only security concern. If someone can gain physical access to an AI system, they may be able to tamper with the underlying model or steal information from hardware vulnerabilities.
Physical red teaming, which tests for these types of physical vulnerabilities, is likely to become a part of many organizations’ AI security roadmaps as they begin to consider protections against physical threats to AI models and their environment.
Currently there are no agreed-upon standardized methodologies for performing AI red teaming. Different organizations (and academic researchers) have taken different approaches to the problem, making it nearly impossible to establish any kind of meaningful baseline around AI safety.
The one standard applicable cross-industry is OWASP’s LLM Top 10 (2025 edition, released November 2024) which defines prompt injection as risk LLM01:2025 (#1). Risk methodology is anchored by NIST’s AI 100-2 E2025 (released March 2025) which provides the adversarial-ML taxonomy. Vendor specific methodologies exist outside of these two standards.
Large language models and multimodal systems are typically “black boxes” with complex designs. Their decision-making processes are not easily interpretable, so finding ways to test these systems requires significant expertise, resources, and specialized tools that can identify model-level and system-level weaknesses.
The good news is that you don’t have to have that expertise in-house to test sophisticated AI models. By working with experts like Mindgard, you can enhance your AI’s safety profile without building your own red team.
Attack methodologies aren’t static. Red teams need to evolve along with new attack methods like data poisoning, adversarial perturbations, model evasion attacks, etc.
The expert effort required to jailbreak frontier models increased 40x across two model generations released six months apart in 2025, but universal jailbreaks “have been found in every system tested.” Model success rates on cyber apprentice-level tasks jumped from ~10% in early 2024 to 50% in 2025 per UK AISI.
There are also frontier risks that need to be red teamed creatively like autonomous misuse and synthetic content creation.
Prompt injection is the Top Threat in the OWASP Top 10 for LLM Applications (LLM01: 2025), and for good reason. Prompt injection is not a traditional software bug. It is a fundamental property of the language modeling paradigm: Since LLMs are instructed to follow natural-language prompts wherever they appear in their context window, an embedded instruction, if carefully placed anywhere in that stream of input, can take control of the model.
Red teams need to test all three attack surfaces:
Separate tooling is required to test each of these three surfaces, and they each have unique criteria for determining successful vulnerability discovery. The attack surface only grows from there when you account for agentic AI systems that can manipulate the world around them. Essentially, any webpage, file, or database the system can access is a potential indirect injection point.
Manual red teaming allows you to identify more nuanced vulnerabilities, but it doesn’t scale very well and requires a lot of resources. Automated red teaming is easily scalable but may not catch some nuanced vulnerabilities that manual review would catch.
Manually testing systems is difficult to scale. Attacker simulation software can find many vulnerabilities, but subtle vulnerabilities may be missed by an automated red team.
This is why many teams need a hybrid approach.
Balancing scalability and resource intensiveness is made even more difficult by there not being enough skilled professionals around. Red teams require specialized knowledge to execute correctly.
AI technology, and the threats surrounding it, are evolving quickly. Red teams need people with security knowledge as well as knowledge of new attack patterns.
Just ~1.5% of respondents from a global company survey stated they believe they have sufficient personnel to keep up with AI safety and security at their rate of adoption (Microsoft Digital Defense Report 2025). Additionally, red teaming for AI needs to be happening constantly, so companies can’t rely on manpower to manually test everything. Automated AI red teaming is a must.
The talent gap is now being addressed at the industry level too: Microsoft’s External Red Team Alliance funds 18 university labs across six continents to grow the pool of qualified AI security researchers.
Teams can look to trusted solution partners like Mindgard to help. We can help you provide scalable, automated red teaming while also giving you access to experts who can help you stay covered without draining your resources.
Learn about some of the top minds in AI security & AI red teaming here.
AI models operate across different modalities, such as image, text, or audio. As a result, evaluating their behavior over these inputs is expected to grow more complicated.
Testing AI over multimodal inputs will require red teamers to combine domain expertise with specialized tooling to identify vulnerabilities arising from interactions between modalities.
Although multimodal security concerns can pose roadblocks, they aren’t necessarily dealbreakers. For example, there are many challenges associated with AI red teaming that can slow progress. However, with collaboration between academia, industry, and policy makers, we can build standardized approaches to evaluating AI as well as create scalable tooling and an ecosystem for AI red teaming.
Academic researchers make the same argument for why that collaboration matters:
“Academic research is critical to understanding the cyber security landscape and finding solutions that work for all of society.”
- (Nicolas Papernot, Professor, University of Toronto. Microsoft Security Blog, July 2026)
Standardized, independently tested methods are how the field gets past today’s vendor-by-vendor patchwork.
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.
Our experts at Mindgard understand how important red teaming is to securing your organization’s AI systems. Mindgard’s Offensive Security for AI is security designed to protect AI systems from emergent threats only identifiable in an instantiated model and not found by traditional application security testing tools.
Reach out to Mindgard today to see how our AI red teaming services can help you secure your AI systems.
An AI red team is a team of security researchers, domain experts, and automated tools that aggressively attack (through prompt injection, jailbreaks, data poisoning, model extraction, output manipulation) an AI system to identify safety, security, and regulatory gaps before malicious actors find them. Advanced red teams use a mix of internal teams (Microsoft's AI Red Team), external specialists (CSET, Humane Intelligence), and crowd powered adversarial testing (DEF CON GRT, UK AISI × Gray Swan).
LLMs are red-teamed because they inherit all the security risks of classical software - and introduce novel failure modes like prompt injection, hallucinated output, training-data leakage, jailbreaks, and agentic misuse. Prompt injections were listed on OWASP LLM Top 10 (2025) alongside sensitive-information disclosure, supply-chain vulnerabilities, improper output handling, harmful levels of agency, and system-prompt leakage. Attacks with maliciously crafted prompts have also been shown to evade traditional ML defenses better than other evasion techniques according to Microsoft’s research into 100 products in 2025. That’s why red teaming needs to happen against each model generation.
Though there aren't many certifications specifically for AI red teaming, there are some legitimate options. For instance, SANS' SEC598: AI and Security Automation for Red, Blue, and Purple Teams recently went through an overhaul in 2025 to include generative AI and agentic automation with hands-on LLM labs built into red/blue/purple team workflows. Upon completing this course, students are qualified to obtain the GIAC Offensive AI Analyst (GOAA) certification.
HTB Academy's AI Red Teamer Job Role Path teaches students how to understand and implement red teaming AI workflows. Developed in collaboration with Google and based off of Google's Secure AI Framework (SAIF), this hands-on job role path walks learners through how to weaponize prompt injection, model privacy attacks, adversarial AI, AI supply chain risks, and AI deployment risks. This path culminates with the HTB Certified Offensive AI Expert (COAE) certification, which tasks students with performing industry-level red team exercises against realistic AI infrastructure including adversarial ML, data poisoning, evasion attacks, and LLM output manipulations.
NIST does not legally require AI red teaming, but two NIST publications define what good AI red teaming looks like. NIST AI 600-1 (July 2024), the Generative AI Profile of the NIST AI Risk Management Framework, recommends red teaming before and after deployment across 12 generative AI risk categories. NIST AI 100-2 E2025 (March 2025) supplies the adversarial machine learning attack taxonomy red teams use to classify jailbreaks, poisoning, evasion and extraction attacks.
The guide defines 12 risk areas:
From there, they map out recommended practices to adhere to for each risk area. Notice how the mapping explicitly calls out both pre-deployment red teaming/testing as well as responsible disclosure of security incidents.
If you dig into how to satisfy just the RMF’s Measure function, NIST explicitly mentions internal testing/red teaming to validate your model against hallucinations, bias, and privacy leaks.
This is all not required by law, but if you’re in a regulated space or part of the federal supply chain, you can probably expect it as a baseline.
AI red teamers in the United States earn between $59.62 and $78.37 per hour, with an average of $67.60 per hour, according to ZipRecruiter data from August 2026. Senior AI red team roles at frontier labs and security vendors pay well into six figures.
Demand outstrips supply: red teaming AI systems requires both classic offensive security skills and model-specific knowledge of jailbreaks, prompt injection and adversarial machine learning.
When red teaming is applied to intelligence, independent teams are used to think critically about weak spots, assumptions, and potential threats. Military, cyber, and AI programs commonly use red teams to analyze possible vulnerabilities.
Mindgard helps organizations discover, assess and defend their AI systems. Spun out of more than a decade of AI security research at Lancaster University in the UK and headquartered in Boston and London, Mindgard operationalizes the expertise of AI researchers and offensive security practitioners through a Security Platform that performs Shadow AI discovery, AI red teaming, and run-time AI protection to assess and mitigate risk across models, agents, and applications.
The expert-level checklist for operationalizing NIST AI RMF, ISO/IEC 42001 and the EU AI Act. 190+ interactive items and a board-ready maturity scorecard. Built for CISOs, AI governance leads and ML engineering teams.
