AI Red Teaming: How to Stress-Test Your AI Systems Before Attackers Do

If your business has deployed a chatbot, an AI-powered support tool, or any application that puts user input in front of a language model, it has an attack surface a traditional penetration test wasn’t built to find. AI red teaming — adversarial testing specifically designed for AI systems — has become the practice most security-mature organizations now use to find those gaps before attackers do, and it’s increasingly a documented requirement under frameworks like the EU AI Act and NIST’s AI Risk Management Framework, not just a best practice.

Why AI systems need their own testing approach

A traditional penetration test looks for vulnerabilities in code, configuration, and infrastructure — SQL injection, broken authentication, misconfigured cloud storage. Those vulnerabilities still matter for AI applications, but AI systems introduce an entirely different category sitting on top: ways to manipulate the model’s behavior through the inputs it receives, in ways that have no equivalent in traditional software.

OWASP’s Top 10 for LLM Applications has become the standard taxonomy for this category, and prompt injection has held the #1 spot for two consecutive years — reflecting how central it is to AI-specific risk. The broader category list red teams typically test against includes:

• Prompt injection — manipulating the model’s behavior through crafted input, directly or indirectly (via content the model reads, not just what the user types)
• Sensitive information disclosure — getting the model to reveal data it shouldn’t (training data artifacts, system prompts, other users’ information)
• Supply chain risk — vulnerabilities inherited from third-party models, plugins, or training data (covered in depth in our AI model supply chain security article)
• Data and model poisoning — manipulating training or fine-tuning data to implant harmful behavior
• Improper output handling — treating model output as safe to execute or display without validation, opening the door to injection attacks downstream
• Excessive agency — giving an AI system more autonomous capability (API access, tool use, action-taking) than a given task actually requires
• Unbounded consumption — resource exhaustion attacks that drive up costs or degrade availability through crafted, expensive-to-process inputs

A related, newer framework — OWASP’s Top 10 for Agentic Applications — covers risks specific to AI agents that take autonomous action, including goal hijacking and memory poisoning, which matters directly for any business deploying agentic AI tools (see our article on securing autonomous AI agents).

How AI red teaming actually works in practice

Rather than a single test-and-done exercise, current practice treats AI red teaming as a continuous loop:

1. Build a versioned test set mapped to the OWASP LLM Top 10 categories — current guidance suggests somewhere between 500 and 5,000 prompts depending on application complexity, covering prompt injection attempts, jailbreak techniques, data disclosure probes, and abuse scenarios specific to what the application actually does.
2. Run automated and manual testing against the live application, not just the underlying model in isolation — many vulnerabilities only appear in how the application wires the model to its data sources, tools, and output handling.
3. Repeat the cycle as the application changes — new prompts, new integrations, updated model versions, and new attack techniques discovered across the industry all mean a test that passed last quarter isn’t a permanent guarantee.
4. Map findings against recognized frameworks — OWASP LLM Top 10, MITRE ATLAS (an attack framework modeled on MITRE ATT&CK but specific to AI systems), and NIST’s AI Risk Management Framework, which increasingly matters for compliance documentation, not just internal tracking.

A sobering finding from recent testing

A March 2026 NIST-backed competition testing 13 frontier AI models in agentic scenarios found at least one successful attack against every single model tested — and notably, the most capable model in the competition was not the most resistant to attack. That finding matters for how businesses should think about AI security generally: model capability and security robustness are not the same axis, and a more advanced model isn’t automatically a safer one. Testing your specific application and its specific configuration matters more than trusting that a capable underlying model has resistance built in.

Where this fits with your existing security program

For most businesses, AI red teaming isn’t a replacement for traditional penetration testing — it’s an addition, covering a layer traditional testing wasn’t designed to reach. If your organization already runs periodic penetration tests on its web applications and infrastructure, AI red teaming extends that same discipline to the AI-specific surface those tests don’t cover: the prompts, the model behavior, the agentic actions, and the data flowing through them.

AI red teaming checklist

• Identify every AI-powered feature or application in your business, including third-party AI integrations
• Map your application against the OWASP Top 10 for LLM Applications (and Agentic Applications, if relevant)
• Build or commission a structured adversarial test set, not ad hoc manual poking
• Test the full application, not just the underlying model in isolation — most real vulnerabilities live in the integration layer
• Treat testing as a repeated cycle tied to application changes, not a one-time pre-launch exercise
• Document findings against recognized frameworks (OWASP, MITRE ATLAS, NIST AI RMF) for compliance and audit purposes
• Don’t assume a more capable underlying model means a more secure application — test your specific configuration

Key takeaways

AI applications carry a category of risk — prompt injection, data disclosure, excessive agency — that traditional penetration testing wasn’t built to find, and OWASP’s LLM Top 10 has become the standard framework for testing against it. A March 2026 NIST-backed competition found every one of 13 frontier models tested was successfully attacked in agentic scenarios, underscoring that this isn’t a theoretical risk category waiting for better models to solve it. If your business has deployed any AI-facing application, adversarial testing against it deserves the same seriousness as a penetration test against your web application — because it’s finding a genuinely different class of exploitable weakness.

Shahid Security’s Penetration Testing service includes AI-specific adversarial testing for businesses running AI-powered applications, mapped against OWASP’s LLM Top 10 framework.

Deployed an AI application and want it tested before an attacker finds the gaps? Book a free consultation.

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Sources: Kili Technology: LLM Red Teaming in 2026, Openlayer: Red-Team Your LLM — Attack Vectors & Evidence, Confident AI: Best AI Red Teaming Tools 2026.