AI Red Teaming: Testing Your Defences Against Intelligent Attacks
How do you test defences against attacks that think, learn, and evolve? The red teaming methodology for AI threats.
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Red Teaming
Red teaming stress-tests an AI system the way an attacker would. These articles cover the methods, what to probe for, and how findings feed back into real controls.
All Red Teaming Posts (19)
Google's Red Team "Mandible": Lessons for Enterprise AI Security
What can enterprise security teams learn from Google's secretive AI red team operations? Inside intelligence reveals advanced testing methodologies.
Red Teaming for AI Systems: Adversarial Testing for Social Services Security
What hidden vulnerabilities lurk in your AI systems? Use adversarial testing methodologies to identify biases and security risks before they impact vulnerable populations.
100 Red Team Methodology Examples with Use Cases
VerityAI's Red Teaming Methodology. Our Top Secret Red Teaming Methodology - Shared, because we sharing is caring, and transparent.
Cultural & Regional AI Vulnerabilities: Ensuring Global Safety and Relevance
AI must work for everyone, everywhere. Explore testing approaches that identify cultural blind spots and ensure your AI performs fairly across diverse global contexts.
AI Content Generation Risks: Testing for Harmful Outputs
AI content generation opens new doors—and new dangers. Learn how to test for harmful outputs, misinformation risks, and subtle bias normalization in your generative AI systems.
AI Fairness & Bias Red Teaming: Uncovering Hidden Disparities Before Deployment
Uncover hidden biases that create unequal experiences across user groups. Master systematic testing approaches to identify and mitigate fairness vulnerabilities before they damage your reputation.
Technical Exploitation
Dive deep into the technical vulnerabilities hiding in AI systems' foundations. From tokenization tricks to character encoding exploits, learn how attackers target the infrastructure itself.
Social Engineering Against AI Systems: The Human Side of AI Manipulation
Beyond technical exploits lies a more dangerous threat: psychological manipulation. Explore how attackers use trust, urgency, and authority to bypass AI safeguards.
Prompt Injection & Jailbreaking: How Attackers Manipulate AI Systems
Discover how attackers manipulate AI systems through cleverly crafted prompts. Learn to identify and defend against the most common exploits before they compromise your AI's safety boundaries.
Temporal & Evolving AI Attack Testing: Identifying Threats That Develop Over Time
The most sophisticated threats evolve over time. Discover how to test for attacks that develop across multiple sessions and interactions, bypassing point-in-time security measures.
AI Model Extraction & IP Protection Testing: Safeguarding Your AI Investments
Your AI models represent valuable IP. Learn how attackers steal model capabilities through API interaction and discover robust protection strategies for your AI investments.
Multimodal AI Vulnerability Testing: Securing Systems Across Input Types
Multiple input types create multiple attack vectors. Learn how text, image, audio, and video interact to create novel security challenges in multimodal AI systems.
Physical AI Safety: The Board's Guide to Assurance
When AI acts in the physical world, a software failure becomes a collision, a recall, or a fine, and the board owns the safety case.
Specialized AI Application Testing: Domain-Specific Risk Assessment
Generic testing fails in critical domains. Discover domain-specific red teaming approaches for healthcare, finance, legal, and other high-stakes AI applications.
AI Legal & Compliance Risk Testing: Navigating the Regulatory Landscape
Navigate the complex regulatory landscape with confidence. Transform AI compliance from a roadblock into a competitive advantage with systematic testing strategies.
Emergent AI Risks: Testing for Behaviour That Only Shows at Scale
Emergent risks are AI behaviours that show up only at scale, and documented cases of alignment faking, scheming, and hidden backdoors prove ordinary QA won't catch them.
AI System Limitations Boundary Testing: Finding Failure Points Before Users Do
Every AI has breaking points. Learn systematic approaches to find your system's boundaries and failure modes before users encounter them in production.
AI Data Extraction & Privacy Vulnerabilities: Protecting Sensitive Information
Your AI might be leaking more than insights. Discover how attackers extract training data, private information, and intellectual property through intelligent querying techniques.
Frequently asked questions
What is red-teaming for AI?
AI red-teaming is deliberately attacking your own AI system to find how it fails, including jailbreaks, prompt injection, data leakage and unsafe outputs, before an attacker or a customer does. It's adversarial by design, so it catches behaviour that ordinary testing and benchmarks miss. NIST's Generative AI profile for the AI RMF explicitly calls for this kind of structured adversarial testing.
How is red-teaming different from a standard security penetration test?
A penetration test targets the infrastructure and code around the model; AI red-teaming targets the model's behaviour, trying to make it say or do things it shouldn't through the inputs themselves. Jailbreaks and prompt injection don't show up in a normal pen test because they exploit the model, not the network. You need both, and they need different skills.
How often should you red-team an AI system?
Red-team before launch and then repeatedly, because models get updated, prompts change and new attack techniques appear constantly. Treating it as a one-off gives you a false sense of safety, since a jailbreak that failed last month may work after the next model update. The NIST AI RMF frames adversarial testing as an ongoing lifecycle activity for exactly this reason.