Introducing GOAT—Promptfoo's Latest Strategy

Promptfoo has introduced a new strategy, GOAT, designed to jailbreak multi-turn conversations in AI models, inspired by Meta's research on agentic red teaming systems. Unlike traditional single-turn attacks, GOAT uses a multi-turn approach where an attacker language model (LLM) engages in ongoing dialogue with a target model, utilizing a structured three-step process: observation, thought, and strategy. This iterative process allows the attacker LLM to dynamically adapt its techniques, simulating human-like adversarial interactions to uncover vulnerabilities in AI models over extended conversations. The GOAT strategy leverages a customizable toolbox of red teaming techniques, such as priming responses, hypotheticals, and persona modifications, to effectively bypass safety mechanisms and expose weaknesses that static methods may miss. By simulating real adversarial behavior and adapting strategies throughout the interaction, GOAT provides a more effective way to test the resilience of LLMs, particularly in conversational AI applications like chatbots and agentic systems.