AI adoption is exploding across industries, with companies rushing to integrate large language models (LLMs) and generative AI into everything from customer service to code generation.
But while businesses focus on AI’s potential, they often overlook the risks lurking beneath the surface. AI systems can be manipulated, exploited, or behave in completely unexpected ways that traditional security testing simply can’t catch.
That’s where AI red teaming comes in, a specialised approach to stress-testing AI systems for vulnerabilities that could compromise security, spread misinformation, or cause ethical harm.
This guide breaks down what AI red teaming actually means, why it’s crucial for any organisation using AI, and how to get started with testing your own systems.
What is red teaming in AI?
AI red teaming is a specialised security testing approach where experts simulate adversarial attacks against AI systems to uncover vulnerabilities. AI red teamers use techniques to uncover hidden risks, including:
- Prompt injection (tricking the AI with cleverly crafted instructions)
- Jailbreaking (bypassing safety guardrails)
- Bias exploitation
The goal isn’t to break AI systems for the sake of it. It’s to identify vulnerabilities early on so they can be fixed before causing real-world harm.
Red teaming has its roots in military strategy and cybersecurity, where “red teams” simulate enemy attacks to test defences. An adversary aims to find weaknesses before real attackers do.
In AI red teaming, we apply this same adversarial mindset to artificial intelligence systems, particularly LLMs. Instead of testing network firewalls or code vulnerabilities, we’re probing how AI systems respond to malicious prompts, unexpected inputs, and edge cases that could cause them to fail catastrophically.
Think of it as a comprehensive stress test for AI. Just like you wouldn’t launch a bridge without testing its weight limits, you shouldn’t deploy AI systems without understanding how they behave under adversarial pressure.
Why is AI/LLM red teaming important?
AI systems introduce risks that traditional cybersecurity approaches weren’t designed to handle. Unlike conventional software, AI models can generate unpredictable outputs, hallucinate false information, and be manipulated through natural language rather than code exploits.
Security risks
Prompt injection attacks can trick AI systems into revealing sensitive training data, bypassing access controls, or executing unintended actions.
For example, an attacker might manipulate a customer AI to leak confidential customer information or authorise unauthorised transactions. These aren’t theoretical risks: they’re happening in production systems today.
Ethical and reputational concerns
AI models can perpetuate harmful biases, generate discriminatory content, or spread misinformation at scale.
A healthcare AI that exhibits racial bias or a hiring algorithm that discriminates against protected groups, for instance, can expose organisations to legal liability and devastating PR crises.
Regulatory pressure
The EU AI Act mandates risk assessments for high-risk AI systems, while NIST’s AI Risk Management Framework pushes organisations toward systematic AI governance. Regulators expect companies to demonstrate they’ve proactively tested their AI systems for potential harms.
Beyond the “happy path”
Most organisations only test AI systems on “happy path” scenarios: the intended use case where everything works as expected. However, attackers don’t follow the rules. They probe edge use cases, combine inputs in unexpected ways, and exploit the gap between how AI systems are supposed to work and how they actually behave under pressure.
Key methods in AI red teaming
AI red teaming combines human creativity with automated testing tools to uncover vulnerabilities that neither approach could have found on its own.
Human red teamers
Human red teamers bring domain experts and adversarial thinking that’s hard to automate. They craft sophisticated prompt attacks, explore edge cases, and think outside the box about how AI systems might be misused.
For instance, a skilled red teamer might discover they can trick a content moderation AI by embedding harmful instructions in seemingly innocent poetry or convince a code generation AI to produce malware by framing it as an ‘educational example.’
Automated tools
Automated tools take this testing beyond what humans can achieve manually. AI fuzzing tools generate thousands of adversarial prompts automatically, while LLM-based evaluators can assess outputs for bias, toxicity, or security risks at scale. Some platforms use adversarial AI models specifically trained to find weaknesses in other AI systems.
Continuous testing
Most importantly, AI red teaming isn’t a one-time activity. AI models evolve through retraining, fine-tuning, and updates, potentially introducing new vulnerabilities or changing how existing ones behave.
Continuous testing ensures you catch issues as your AI systems change, rather than discover them when attackers do.
AI red teaming vs traditional security testing
Traditional security focuses on infrastructure, networks, and code vulnerabilities. Penetration testers look for SQL injection flaws, misconfigured servers, and weak authentication mechanisms: technical weaknesses with well-defined attack vectors and fixes.
AI red teaming operates in a fundamentally different space. Instead of exploiting code vulnerabilities, attackers manipulate AI behaviour through natural language. Rather than gaining unauthorised access to systems, they trick AI models into producing harmful, biased, or incorrect outputs. The attack surface isn’t just technical: it’s linguistic, behavioural, and contextual.
The remediation approaches differ, too. Traditional vulnerabilities often have clear technical fixes: patch the software, update configurations, or implement access controls. AI vulnerabilities might require retraining models, adjusting safety guardrails, or redesigning how humans interact with AI systems.
However, these approaches complement rather than replace each other. A comprehensive security strategy needs both traditional security testing to protect the infrastructure running AI systems and AI red teaming to ensure the AI models themselves behave safely and securely.
Getting started with AI red teaming
Starting with AI red teaming doesn’t require a massive investment or specialised team. Even basic adversarial testing can uncover significant risks, and the insights you gain will inform more sophisticated testing as your AI security programme matures.
Here’s how to get going. Or, you can check out our in-depth tutorial on LLM red teaming: A practical guide for AI security.
Define goals
Begin by clearly defining what you’re trying to protect against: security breaches, regulatory compliance violations, reputational damage, or ethical harms.
Start small with prompt testing
Try adversarial inputs against your AI systems: can you make them reveal information they shouldn’t? Do they exhibit bias when discussing sensitive topics? Can you bypass their safety restrictions with creative prompting? Document what you find and how the system responds.
Use open-source red teaming tools and frameworks
Open source tools and frameworks like the Python Risk Identification Tool (PyRIT) for generative AI, the Adversarial Robustness Toolbox (ART), and CleverHans (one of the first AML libraries) provide structured approaches to AI red teaming without requiring custom development. These platforms offer pre-built attack scenarios, evaluation metrics, and reporting capabilities that can accelerate your testing programme.
Work with external experts if needed
For more sophisticated testing, consider working with outsider experts who specialise in AI security. Just as you might hire penetration testers for traditional security assessments, AI red teaming specialists bring experience across different AI systems and attack techniques that internal teams might overlook.
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