FERRET: Framework for Expansion Reliant Red Teaming

arXiv cs.AI / 3/12/2026

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Key Points

FERRET (Framework for Expansion Reliant Red Teaming) is introduced as a multi-modal automated red-teaming framework designed to generate adversarial conversations that test and break target models.
It defines horizontal expansion to enable self-improvement of the red team model, vertical expansion to turn starter conversations into multi-modal dialogues, and meta expansion to discover new attack strategies during a conversation.
The authors compare FERRET with existing automated red-teaming approaches and report superior performance in generating effective adversarial conversations.
The work highlights implications for AI safety and model robustness and suggests directions for future automated red-teaming research.

Abstract

We introduce a multi-faceted automated red teaming framework in which the goal is to generate multi-modal adversarial conversations that would break a target model and introduce various expansions that would result in more effective and efficient adversarial conversations. The introduced expansions include: 1. Horizontal expansion in which the goal is for the red team model to self-improve and generate more effective conversation starters that would shape a conversation. 2. Vertical expansion in which the goal is to take these conversation starters that are discovered in the horizontal expansion phase and expand them into effective multi-modal conversations and 3. Meta expansion in which the goal is for the red team model to discover more effective multi-modal attack strategies during the course of a conversation. We call our framework FERRET (Framework for Expansion Reliant Red Teaming) and compare it with various existing automated red teaming approaches. In our experiments, we demonstrate the effectiveness of FERRET in generating effective multi-modal adversarial conversations and its superior performance against existing state of the art approaches.