构建三位一体的AI代理安全框架，统筹信任、风险与责任问题。
Toward a Unified Security Framework for AI Agents: Trust, Risk, and Liability
- 提出信任-风险-责任联动框架，系统化解决AI代理安全难题。
- 可适配各类应用场景，提供针对性安全措施建议。
- 适用于6G网络等前沿领域，推动可信可靠的AI发展。
AI代理的发展虽带来巨大机遇，但随之而来的信任危机、风险隐患以及责任归属困难等问题日益突出。现有解决方案多孤立应对各问题，未能认识到三者间的相互影响。本文提出的信任、风险与责任（TRL）框架，将三者关联起来，提供系统性方法以建立和增强信任、分析与缓解风险、分配与追溯责任。该框架可应用于任意AI代理应用场景，并根据上下文提出合适的应对措施。其潜在影响涵盖社会、经济、伦理等多个层面，有望为6G网络中实现可信、无风险、负责任的AI应用提供重要价值。
原文摘要 · Abstract (English)
The excitement brought by the development of AI agents came alongside arising problems. These concerns centered around users' trust issues towards AIs, the risks involved, and the difficulty of attributing responsibilities and liabilities. Current solutions only attempt to target each problem separately without acknowledging their inter-influential nature. The Trust, Risk and Liability (TRL) framework proposed in this paper, however, ties together the interdependent relationships of trust, risk, and liability to provide a systematic method of building and enhancing trust, analyzing and mitigating risks, and allocating and attributing liabilities. It can be applied to analyze any application scenarios of AI agents and suggest appropriate measures fitting to the context. The implications of the TRL framework lie in its potential societal impacts, economic impacts, ethical impacts, and more. It is expected to bring remarkable values to addressing potential challenges and promoting trustworthy, risk-free, and responsible usage of AI in 6G networks.
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