Toward a Unified Security Framework for AI Agents: Trust, Risk, and Liability
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911203510452224 |
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| author | Mo, Jiayun Kang, Xin Li, Tieyan Lei, Zhongding |
| author_facet | Mo, Jiayun Kang, Xin Li, Tieyan Lei, Zhongding |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09620 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward a Unified Security Framework for AI Agents: Trust, Risk, and Liability Mo, Jiayun Kang, Xin Li, Tieyan Lei, Zhongding Cryptography and Security Artificial Intelligence 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. |
| title | Toward a Unified Security Framework for AI Agents: Trust, Risk, and Liability |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2510.09620 |