Survey Maps Safety and Privacy Risks in Agentic AI Systems
A detailed study released on arXiv (2605.23989) investigates the reliability of agentic AI systems—large language models (LLMs) that incorporate planning, tool utilization, memory, and long-term interactions. This research highlights two essential aspects for high-risk applications: Safety and Robustness, along with Privacy and System Security. It defines important terms, pinpoints risk areas within the agent workflow, and suggests targeted strategies for risk mitigation at various stages. Additionally, topics such as value alignment, transparency, fairness, and accountability are explored for context. The authors create a centralized hub for metrics and benchmarks, focusing on both process and outcome indicators.
Key facts
- arXiv paper ID: 2605.23989
- Focuses on agentic AI systems: LLMs with planning, tool use, memory, and long-horizon interactions
- Examines two core dimensions: Safety and Robustness, and Privacy and System Security
- Proposes stage-targeted mitigation strategies
- Consolidates evaluation into a unified metrics-and-benchmarks hub
- Discusses value alignment, transparency, fairness, and accountability as context
- Targets high-risk deployments
- Emphasizes both outcome and process signals
Entities
Institutions
- arXiv