AI Model Red Teaming & Safety
FERRET: Framework for Expansion Reliant Red Teaming
Introducing FERRET, a multi-faceted automated red teaming framework designed to generate multi-modal adversarial conversations. It aims to identify and exploit vulnerabilities in target models through novel horizontal, vertical, and meta expansion strategies, integrating text and image attacks for comprehensive model safety assessment.
Key Performance Indicators
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Attack Success Rate (ASR)
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Diversity Score
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Conversations Analyzed
Deep Analysis & Enterprise Applications
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Horizontal Expansion
Vertical Expansion
Meta Expansion
Horizontal Expansion: Discovering Conversation Starters
In horizontal expansion, the red team model leverages policy descriptions, attack strategies, and feedback from previous trials to discover effective conversation starters. These prompts form the first turn of a conversation, aiming to generate initial violations. The model continuously self-improves by learning from successful and unsuccessful examples logged in the horizontal memory.
This process is crucial for identifying novel attack vectors without predefined goals, making the red teaming process more autonomous and adaptable.
Vertical Expansion: Building Multi-Turn Conversations
Vertical expansion takes the conversation starters discovered during horizontal expansion and expands them into full, multi-turn adversarial conversations. It involves stacking various attack and jailbreaking strategies, including the fusion of text and image modalities to create intertwined multi-modal attacks. The red team model decides the optimal strategy and modality combination to deepen the attack.
Each turn of the conversation is logged, providing a detailed history for further analysis and refinement.
Meta Expansion: Evolving Attack Strategies
Meta expansion focuses on discovering new attack or jailbreaking techniques, drawing inspiration from existing strategies for text and image modalities. The red team model is encouraged to build upon these examples to generate novel and more effective adversarial approaches. This continuous evolution of attack strategies ensures that FERRET remains at the forefront of identifying emerging vulnerabilities in target models.
By constantly innovating its attack taxonomy, FERRET can uncover vulnerabilities that static red teaming approaches might miss.
Horizontal Expansion Process Flow
Attack Model
→
Transformation Toolkit
→
Target Model
→
Judge Model
→
Horizontal Feedback Logs
Vertical Expansion Process Flow
Attack Model
→
Transformation Toolkit
→
Target Model
→
Judge Model
→
Conversation History
3.6%
ASR Improvement vs. SOTA Baselines on Llama Maverick
| Target Model | Metric | FLIRT | GOAT | FERRET (Ours) |
|---|---|---|---|---|
| Llama Maverick | Attack Success Rate | 12.8% | 18.1% | 21.7% |
| Llama Maverick | Diversity | 0.266 | 0.226 | 0.252 |
FERRET consistently demonstrates superior performance across both Attack Success Rate and Diversity metrics when compared to existing state-of-the-art baselines like FLIRT and GOAT. This highlights FERRET's ability to generate both more effective and more diverse adversarial conversations.
Validating FERRET with Human Judgement
Our human studies revealed an attack success rate of 27.4% for FERRET in multi-turn scenarios, validating its effectiveness in identifying policy violations. In single-turn comparisons, FERRET consistently outperformed baselines (6% ASR for FERRET vs. 4.8% for FLIRT), affirming its superior capability in both multi-turn and single-turn adversarial generation. These results underscore the framework's practical impact in real-world AI safety assessments.
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FERRET Integration Roadmap
A phased approach to integrate advanced red teaming into your AI development lifecycle.
Horizontal Expansion Setup
Configure policies and initial attack strategies. FERRET autonomously discovers effective conversation starters by learning from feedback logs.
Vertical Expansion & Multi-Modal Attacks
Expand discovered prompts into full multi-turn conversations, integrating text and image attacks using a transformation toolkit.
Meta Expansion & Strategy Evolution
FERRET dynamically generates new attack and jailbreaking techniques, continuously adapting to enhance adversarial effectiveness.
Continuous Monitoring & Refinement
Integrate feedback loops for ongoing model assessment, ensuring long-term safety and robustness against emerging threats.
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