Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security
A new survey examines the trustworthiness of agentic AI systems, focusing on safety, robustness, privacy, and system security. The authors highlight the risks associated with these systems and propose strategies for mitigation. They also discuss the importance of consistent evaluation metrics for high-stakes deployments.
- ▪Agentic AI systems can execute complex tasks autonomously but introduce new failure modes that challenge trustworthiness.
- ▪The survey clarifies key concepts and identifies risks along the agent workflow, summarizing targeted mitigation strategies.
- ▪Open challenges include self-evolving agents, runtime monitoring, and the trust-utility trade-off.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23989 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
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| Summary source text | contentText |
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| Publisher visit | Yes — open original |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Artificial Intelligence arXiv:2605.23989 (cs) [Submitted on 17 May 2026] Title:Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security Authors:Jinhu Qi, Muzhi Li, Jiahong Liu, Yuqin Shu, Dianzhi Yu, Shicheng Ma, Wenqian Cui, Yiyang Zhao, Yiyi Chen, Ruoxi Jiang, Irwin King, Zenglin Xu View a PDF of the paper titled Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security, by Jinhu Qi and 11 other authors View PDF HTML (experimental) Abstract:Agentic AI systems -- Large Language Models (LLMs) augmented with planning, tool use, memory, and long-horizon interactions -- can execute complex tasks autonomously, but their multi-step trajectories introduce new failure modes that…
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