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 past conversations and stateful backends that maintain session context internally.

AI Red Teaming supports all major LLM providers including OpenAI, Gemini, Bedrock, and Hugging Face 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.