Multi-turn attack support enables you to conduct sophisticated AI Red Teaming scenarios by maintaining conversation context across multiple interactions with your target language models, extending beyond basic single-turn testing to simulate realistic attack patterns where adversaries gradually manipulate model behavior through sequential prompts that mimic how actual users interact with AI systems in production environments.

When you configure multi-turn attacks, AI Red Teaming automatically manages conversation state, supporting both stateless APIs that use conversation history and stateful backends that maintain session context internally.

AI Red Teaming supports all major LLM providers including OpenAI, Gemini, Bedrock, Hugging Face, and Databricks, while also accommodating custom endpoints through flexible configuration options that auto-detect common message formats.

You can leverage multi-turn attacks to test sophisticated vulnerabilities that only emerge through extended conversations with AI agents, particularly when evaluating chatbots and virtual assistants that maintain memory across interactions to verify whether context from earlier turns can be exploited to bypass safety controls in later exchanges. If you are assessing custom API endpoints or self-hosted LLM backends, multi-turn testing reveals how these systems handle conversation state management and whether attackers could poison agent memory or create context confusion, while organizations migrating from one LLM provider to another can use multi-turn campaigns to ensure their new infrastructure maintains the same security posture across extended interactions.

Multi-turn support increases the realism and effectiveness of your AI Red Teaming efforts by testing how models behave under conditions that mirror actual user interactions rather than isolated prompts, reducing manual testing overhead by automating complex attack chains that would otherwise require crafting individual requests and manually managing conversation state across multiple API calls.

AI Red Teaming provides a complete audit trail of conversation histories ensuring compliance requirements are met while enabling reproducibility of security findings across your organization, and by detecting vulnerabilities that only manifest through accumulated context, you gain visibility into risks that single-turn testing cannot uncover, particularly those involving memory isolation failures and gradual manipulation of model behavior through carefully sequenced prompts.