Contextualized Privacy Defense for LLM Agents
LLM agents increasingly act on users' personal information, yet existing privacy defenses remain limited in both design and adaptability. Most prior approaches rely on static or passive defenses, such as prompting and guarding. These paradigms are insufficient for supporting contextual, proactive privacy decisions in multi-step agent execution. We propose Contextualized Defense Instructing (CDI), a new privacy defense paradigm in which an instructor model generates step-specific, context-aware privacy guidance during execution, proactively shaping actions rather than merely constraining or vetoing them. Crucially, CDI is paired with an experience-driven optimization framework that trains ...
| Authors | Yule Wen, Yanzhe Zhang, Jianxun Lian et al. |
|---|---|
| Primary category | cs.CR |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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