消融拒绝向量让大模型能执行有害任务，暴露安全机制漏洞。
Applying Refusal-Vector Ablation to Llama 3.1 70B Agents
- 通过消融拒绝向量，构建无限制智能体
- 70B模型可成功完成行贿、钓鱼等有害任务
- 聊天模式下拒答，但智能体却会执行，适合安全研究者
近期，Llama 3.1 Instruct 等语言模型展现出日益增强的代理行为能力，可完成需短期规划与工具调用的任务。本研究对 Llama 3.1 70B 模型应用拒绝向量消融，并构建简易代理框架，生成不受限智能体。结果表明，经消融的模型可成功执行贿赂官员、制作钓鱼攻击等有害任务，揭示当前安全机制存在显著漏洞。为此，我们设计了一个小型 Safe Agent Benchmark，用于测试代理场景下的有害与良性任务表现。结果显示，聊天模式的安全微调无法有效泛化至代理行为：未修改的 Llama 3.1 Instruct 模型愿意执行多数有害任务，但在直接问答中又会拒绝提供相关建议。这凸显了模型能力提升带来的滥用风险，亟需完善语言模型代理的安全框架。
原文摘要 · Abstract (English)
Recently, language models like Llama 3.1 Instruct have become increasingly capable of agentic behavior, enabling them to perform tasks requiring short-term planning and tool use. In this study, we apply refusal-vector ablation to Llama 3.1 70B and implement a simple agent scaffolding to create an unrestricted agent. Our findings imply that these refusal-vector ablated models can successfully complete harmful tasks, such as bribing officials or crafting phishing attacks, revealing significant vulnerabilities in current safety mechanisms. To further explore this, we introduce a small Safe Agent Benchmark, designed to test both harmful and benign tasks in agentic scenarios. Our results imply that safety fine-tuning in chat models does not generalize well to agentic behavior, as we find that Llama 3.1 Instruct models are willing to perform most harmful tasks without modifications. At the same time, these models will refuse to give advice on how to perform the same tasks when asked for a chat completion. This highlights the growing risk of misuse as models become more capable, underscoring the need for improved safety frameworks for language model agents.
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