Adversaries Can Misuse Combinations of Safe Models (2406.14595v2)

Abstract: Developers try to evaluate whether an AI system can be misused by adversaries before releasing it; for example, they might test whether a model enables cyberoffense, user manipulation, or bioterrorism. In this work, we show that individually testing models for misuse is inadequate; adversaries can misuse combinations of models even when each individual model is safe. The adversary accomplishes this by first decomposing tasks into subtasks, then solving each subtask with the best-suited model. For example, an adversary might solve challenging-but-benign subtasks with an aligned frontier model, and easy-but-malicious subtasks with a weaker misaligned model. We study two decomposition methods: manual decomposition where a human identifies a natural decomposition of a task, and automated decomposition where a weak model generates benign tasks for a frontier model to solve, then uses the solutions in-context to solve the original task. Using these decompositions, we empirically show that adversaries can create vulnerable code, explicit images, python scripts for hacking, and manipulative tweets at much higher rates with combinations of models than either individual model. Our work suggests that even perfectly-aligned frontier systems can enable misuse without ever producing malicious outputs, and that red-teaming efforts should extend beyond single models in isolation.

• The paper demonstrates that adversaries decompose complex tasks into subtasks using safe models, achieving a 43% success rate for vulnerable code generation.
• The study employs both manual and automated decomposition methods to reveal that combining models can dramatically increase misuse potential.
• The findings call for expanded red-teaming and policy revisions to address evolving threats as AI models increasingly interact.

Adversaries Can Misuse Combinations of Safe Models: An Analysis

The paper "Adversaries Can Misuse Combinations of Safe Models" by Erik Jones, Anca Dragan, and Jacob Steinhardt presents an empirical paper elucidating the insufficiency of evaluating AI models for misuse risks individually. The authors demonstrate that even models deemed "safe" when evaluated separately can be combined to execute malicious tasks. This insight has significant implications for the evaluation and deployment of AI models, particularly LLMs.

Core Findings

Key findings of the paper include:

1. Task Decomposition:

Adversaries can break down complex, malicious tasks into subtasks, utilizing different models to solve each part based on their specific capabilities. The paper explores two decomposition methods: - Manual Decomposition: A human identifies and splits tasks, leveraging the capabilities of a frontier model for complex but benign subtasks, and weak models for simpler, malicious subtasks. - Automated Decomposition: A weak model generates benign sub-tasks, which are then solved by a frontier model. Finally, these solutions are used in-context by the weak model to achieve the original malicious objective.

1. Empirical Results: The authors present concrete empirical evidence showing that model combinations significantly enhance the ability to misuse AI systems. For instance, combining Claude 3 Opus with Llama 2 70B resulted in a success rate of 43% for generating vulnerable code, compared to under 3% for either model individually.
2. Threat Model: The paper formalizes a threat model wherein adversaries have access to multiple models and sequentially query them to achieve a malicious goal. The efficiency of the adversaries in composing the models underscores the complexity of ensuring AI safety.
3. Scaling Behavior: The paper foresees that as both frontier and weak models improve, the potential for misuse via model combinations will escalate. The success of adversaries scales with the quality of the models involved, indicating a future where model combinations could become increasingly problematic.

Practical and Theoretical Implications

Red-Teaming and AI Safety

The research suggests an expansion of red-teaming efforts to include multi-model scenarios. Traditional approaches that focus on individual model misuse are inadequate in the face of novel misuse strategies that exploit combinations of models. Red-teaming needs to be a continuous process throughout model deployment, reassessing risks as new models are introduced.

Policy and Deployment Strategies

The findings call for a rethinking of policies around AI model deployment. Developers need to account for the broader ecosystem of models rather than individual model assessments. Extensions to current safety protocols are necessary to mitigate risks from model combinations. Additionally, this elevates the need for collaborative efforts across organizations to establish community-wide standards for AI deployment and safety evaluations.

Future Developments in AI

The paper highlights several avenues for future research and development in AI security:

• LLM Agents: Future adversaries might leverage LLM agents to adaptively exploit model combinations, potentially using techniques like reinforcement learning to fine-tune weak models against strong models.
• Tool and Information Access: New dimensions of model combinations could involve models with specialized tools or access to proprietary information, escalating the complexity of AI misuse.
• Emergent Capabilities: Research should focus on emergent capabilities arising from model interactions, such as collusion between AI agents, which could magnify the scale and impact of misuse.

Conclusion

The paper "Adversaries Can Misuse Combinations of Safe Models" fundamentally challenges the current understanding of AI safety. It underscores the necessity of evaluating AI systems within the context of their interactions with other models, rather than in isolation. The empirical evidence provided offers a compelling argument for revising and expanding red-teaming practices, policy frameworks, and continuous monitoring throughout the lifecycle of AI deployments. This research paves the way for more robust and comprehensive strategies to safeguard against the multifaceted nature of AI misuse.