$S^3$: Improving Agent Safety through Multi-Stage Defense
Large Language Model (LLM) agents rely on multi-stage agentic workflows, with stages such as memory, planning, and tool execution, to accomplish complex tasks. However, risks may emerge at different stages, propagate across steps, and become difficult to detect and mitigate. Existing safety methods protect only isolated stages and are difficult to integrate, leaving agents without comprehensive protection throughout the workflow. To address these limitations, we introduce Stage-Specific Safety Skills, a unified abstraction that represents heterogeneous safety designs as reusable and composable components with explicit stage semantics. We further develop an automated transformation pipelin...
| Authors | Zibo Xiao, Haoyu Wang, Jun Sun |
|---|---|
| Primary category | cs.CR |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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