AI Red Teaming: Core Techniques and How It Differs

AI red teaming is an authorized, goal-based attack simulation against an AI system, such as an LLM app, AI agent or RAG pipeline, to find security flaws and unsafe behavior before real attackers do. At Pentestica it runs alongside AI penetration testing, quoted in tester-days after a scoping call.

The idea comes from classic red teaming, but the target is the model’s behavior and the tools and data it can reach, not only servers and networks.

Why AI red teaming differs from traditional red teaming

• Focus: A traditional red team engagement works toward an objective across your perimeter, cloud and internal network. AI red teaming aims at model behavior, prompt manipulation, data leakage and the misuse of tools an agent can call.
• Nature: Most software gives the same answer to the same request. A model can answer the same prompt differently across attempts, so testers repeat attacks and keep the exact prompts and responses as evidence.
• Team: The work combines offensive security testing with an understanding of how models, prompts and their data sources behave, because many findings sit in the gap between the two.

Core AI red teaming attack techniques

• Jailbreaking: Crafting prompts that get the model past its built-in safety rules and policy restrictions.
• Prompt injection: Hiding instructions in user input or in retrieved content, such as a document in a RAG source, so the model follows the attacker instead of the system prompt.
• Data poisoning and extraction: Planting content that changes model behavior, or probing the model until it reveals data it shouldn’t. NIST notes that common AI security concerns “relate to adversarial examples, data poisoning, and the exfiltration of models…”
• Agentic tool abuse: Getting an AI agent to use its access to APIs, databases or other tools for an action the user was never allowed to take.

Key AI red teaming frameworks and resources

• OWASP Top 10 for LLM Applications: Our test cases are aligned with the OWASP Top 10 for LLM Applications (2025).
• MITRE ATLAS: Our attack scenarios reference MITRE ATLAS.
• NIST AI Risk Management Framework: NIST describes it as “intended to be voluntary, rights-preserving, non-sector-specific, and use-case agnostic.” It gives context for which risks matter to you; it isn’t a testing method.
• Evidence you can act on: You get an Executive Summary and a Technical Report with the prompts, responses and fixes, and a retest is available after fixes.

AI red teaming or an AI penetration test: which one do you need?

Choose an AI penetration test when you need broad coverage of a defined scope; choose AI red teaming when you want to know whether a specific objective is reachable. An AI penetration test checks a defined scope (the chatbot, agent or RAG search plus the web app and API around it) for as many weaknesses as the tester-days allow. AI red teaming is the wider, goal-based version: the team picks an objective, such as reading another customer’s data through the assistant, and tries every route to it. If you want to compare the two approaches outside AI, see red team vs penetration testing, or how purple teaming brings attackers and defenders together. Our penetration testing services list every test type.

Agents often call the same APIs as your users, so old API flaws still count. In API tests of B2B systems, we’ve found administrative endpoints that were hidden in the user interface but still reachable through a direct call. An account with an ordinary user role could perform an operation reserved for administrators. The fix was to move the permission check from the frontend to the backend.

Sources: OWASP Top 10 for Large Language Model Applications; NIST AI 100-1, AI Risk Management Framework 1.0.