AI chatbots and assistants are now front-line software: they talk to customers, read internal documents, call APIs and increasingly take actions on a user's behalf. That makes them an attractive target. Unlike a normal web form, a chatbot can be talked into misbehaving — leaking data, ignoring its rules, or driving a connected system to do something it shouldn't. Red-teaming is how you find these weaknesses before an attacker does.

This guide walks through how to red-team an AI chatbot in practice — what to test, the techniques that work, and how to turn findings into fixes. It is aligned to the OWASP Top 10 for LLM Applications and the MITRE ATLAS adversarial-ML knowledge base.

AI red-teaming is the practice of adversarially testing an AI system to make it fail — bypassing its guardrails, extracting data it should protect, or manipulating it into unauthorised actions. Where traditional penetration testing targets code and infrastructure, AI red-teaming targets the model's behaviour: the prompts, the context it reads, and the tools it can use. The two are complementary, and AI products generally need both.

Try to override the chatbot's instructions directly — for example, asking it to ignore previous rules, reveal its system prompt, or adopt a new persona without restrictions. The goal is to see whether user input can outrank the system's own instructions.

Plant malicious instructions in content the chatbot will later read — a document, a web page, a support ticket, an email or an API response. When the model ingests that content (common in RAG and agent workflows), the hidden instructions can hijack its behaviour. This is one of the most dangerous and overlooked attack paths.

Use role-play, hypothetical framing, encoding tricks, or multi-step conversations to coax the model past its safety guardrails into producing prohibited or harmful output. Test whether guardrails hold across long conversations, not just single messages.

Probe for data leakage — system prompts, API keys, other users' data, or confidential records the model can reach through its context or tools. In RAG systems, test whether you can retrieve documents you shouldn't have access to (a broken tenant- or access-control boundary).

If the chatbot can call tools or take actions — send emails, run queries, make changes — test whether you can manipulate it into doing so without authorisation, or beyond what the current user should be allowed. This is where AI risk becomes real-world impact.

Test resource abuse: can an attacker drive expensive inference at scale (denial-of-wallet), or extract the model's behaviour through systematic querying? Check for rate limits, quotas and monitoring.

AI systems change constantly — new prompts, new tools, new models and new data. Red-team before launching a customer-facing chatbot, after any significant change to its capabilities or integrations, and on a regular cadence thereafter. Pair it with traditional VAPT so both the model and the software around it are covered.

Red-teaming an AI chatbot means thinking like an attacker who speaks the model's language — using prompts, poisoned context and connected tools to make it misbehave. Cover the core techniques, map findings to the OWASP LLM Top 10, fix the root causes, and re-test. Done well, red-teaming lets you ship AI assistants your customers can trust.