实时验证大模型行动可信度，防患于未然
Real-Time Trust Verification for Safe Agentic Actions using TrustBench
- 在动作执行前调用安全验证工具，提前拦截有害行为
- 多领域插件使有害行为减少87%，特定领域效果更优
- 延迟低于200毫秒，适合真实场景的智能体应用
随着大语言模型从对话助手演变为自主智能体，确保可信性需从事后评估转向实时行动验证。现有框架如AgentBench仅评估任务完成情况，TrustLLM和HELM则在生成后评估输出质量，均无法阻止执行中的有害行为。本文提出TrustBench，一种双模式框架：(1) 通过传统指标与大模型作为裁判（LLM-as-a-Judge）评估多维度信任度；(2) 提供智能体在执行前调用的安全验证工具包。该框架在智能体制定行动后、执行前介入，针对医疗、金融、技术等领域的专用插件编码安全规则。在多个智能体任务中，TrustBench将有害行为减少87%；领域专用插件比通用验证提升35%的防护效果。系统延迟低于200毫秒，支持实际部署的实时可信验证。
原文摘要 · Abstract (English)
As large language models evolve from conversational assistants to autonomous agents, ensuring trustworthiness requires a fundamental shift from post-hoc evaluation to real-time action verification. Current frameworks like AgentBench evaluate task completion, while TrustLLM and HELM assess output quality after generation. However, none of these prevent harmful actions during agent execution. We present TrustBench, a dual-mode framework that (1) benchmarks trust across multiple dimensions using both traditional metrics and LLM-as-a-Judge evaluations, and (2) provides a toolkit agents invoke before taking actions to verify safety and reliability. Unlike existing approaches, TrustBench intervenes at the critical decision point: after an agent formulates an action but before execution. Domain-specific plugins encode specialized safety requirements for healthcare, finance, and technical domains. Across multiple agentic tasks, TrustBench reduced harmful actions by 87%. Domain-specific plugins outperformed generic verification, achieving 35% greater harm reduction. With sub-200ms latency, TrustBench enables practical real-time trust verification for autonomous agents.
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