📰 Originally published on SecurityElites — the canonical, fully-updated version of this article.
Has your organisation conducted an AI red team assessment?
No — we haven’t deployed any AI applications yet We’re deploying soon but haven’t assessed security yet We’ve done informal testing but no structured red team Yes — we have a formal AI red team process
AI Red Teaming Guide for 2026 :— Every organisation deploying an LLM application is deploying an attack surface that no traditional security control was designed to protect. Firewalls, WAFs, and vulnerability scanners have no visibility into whether a chatbot can be manipulated into leaking customer data, whether the RAG pipeline returns documents the user shouldn’t see, or whether the AI can be prompted to produce content that creates legal or reputational liability. The only way to know is to systematically try to make these things happen — before your users do, before journalists do, before attackers do. This is AI red teaming. This guide covers the complete methodology: what to test, how to test it, and what the most common findings look like in practice.
🎯 What You’ll Learn
The six core domains of an AI red team assessment and what each covers
How to structure test cases using the OWASP LLM Top 10 as a framework
Automated tools (Garak, PyRIT) and when manual testing is essential
What real enterprise AI red team findings look like and how to report them
How to build a continuous AI red team programme rather than a one-time assessment
⏱️ 35 min read · 3 exercises ### 📋 AI Red Teaming Guide 2026 — Contents 1. Why AI Security Testing Is Different 2. The Six Core Assessment Domains 3. OWASP LLM Top 10 as a Testing Framework 4. Red Team Tools — Automated and Manual 5. What Real Findings Look Like 6. Building a Continuous AI Red Team Programme ## Why AI Security Testing Is Different Traditional application security testing has a relatively stable target: code with deterministic behaviour. A SQL injection either works or it doesn’t. An authentication bypass either succeeds or fails. The vulnerability either exists in the code or it doesn’t. AI applications break this model. An LLM’s responses are probabilistic — the same input can produce different outputs across sessions. Vulnerabilities are often emergent from the interaction between the model, the system prompt, the retrieval pipeline, and the user input, not from any single component with a clear CVE. And the attack surface grows with every new capability added to the application.
The most significant difference is that AI red teaming must assess intended behaviour as well as unintended behaviour. A traditional penetration test succeeds when it finds something the application was never supposed to do. AI red teaming must also assess whether the application correctly handles what it was designed to do — whether it stays within scope, whether it applies its guidelines consistently, whether edge cases in legitimate usage produce harmful outputs. This dual mandate — finding both unexpected failures and intended-but-harmful behaviours — requires a different testing mindset.
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AI Red Team vs Traditional Pentest — Scope Comparison
Domain
Traditional Pentest
AI Red Team
Attack surface
Code, infrastructure, config
Model, prompts, RAG, tools, outputs
Reproducibility
Deterministic — same exploit, same result
Probabilistic — results vary across runs
Finding type
Unintended behaviours only
Unintended + intended-but-unsafe
Fix
Patch specific code/config
System prompt, model, guardrails, architecture
📸 AI red teaming vs traditional penetration testing scope comparison. The probabilistic nature of LLMs requires running each test case multiple times and rating findings by frequency rather than binary present/absent. An AI system that produces a harmful output 5% of the time is a vulnerability requiring remediation, even though 95% of interactions are safe. This statistical nature is the fundamental reason AI security testing requires a different methodology.
The Six Core Assessment Domains
Domain 1: Prompt injection and override. Testing whether adversarial inputs can override the system prompt’s instructions, bypass safety guidelines, or cause the model to behave in ways inconsistent with its defined role. This includes direct injection (user input directly attempts to override instructions), indirect injection (instructions embedded in retrieved documents, tool outputs, or external data sources), and role-based injection (convincing the model to adopt a persona that bypasses its guidelines).
Domain 2: Information disclosure. Testing whether the application leaks information it should not: system prompt content, information from other users’ sessions (in multi-user applications), data from RAG sources outside the user’s authorisation scope, training data memorisation (reproducing text from training data), and model configuration details that could inform further attacks.
Domain 3: Misuse and scope escape. Testing whether users can use the AI application for purposes outside its intended scope in ways that create harm or liability — generating content the application is not designed to produce, using the application as a proxy for purposes it should refuse, or exploiting the application’s capabilities for harmful downstream uses.
Domain 4: Unsafe output. Testing whether the application produces outputs that could cause harm if acted upon — factually incorrect information presented as authoritative, dangerous instructions, content that creates legal liability, or outputs that could harm users in specific contexts (medical advice, financial guidance, crisis situations).
📖 Read the complete guide on SecurityElites
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