PrivacySkills: How Privacy Guidance Shapes Source Selection in LLM Agents
cs.CR, cs.AI, cs.HC
Submitted: 2026-09-28
Updated: 2026-09-28
License: http://creativecommons.org/licenses/by/4.0/
The gist: While prior work has documented privacy failures in LLM agents, it remains unclear how the presentation of privacy guidance influences their choice of information sources.
Terminology
Abstract
While prior work has documented privacy failures in LLM agents, it remains unclear how the presentation of privacy guidance influences their choice of information sources. We introduce PrivacySkills, a controlled framework for evaluating how agents choose among acquisition pathways that provide the same task-relevant value: consulting publicly available personal information, accessing confidential sources, or interacting with the user. The evaluation framework comprises 55 synthetic tasks spanning 11 categories of personal information, with 169 associated skills that describe the available acquisition pathways. We consider privacy guidance through system-level instructions, skill-level metadata labels, or both. Separately, we vary user availability and urgency framing. With users available and no privacy guidance, agents access confidential sources in 30% of valid runs on average across five open-weight models, despite sufficient alternatives. This rate increases to 45% when users are unavailable, whereas urgency framing has no detectable effect. System-level privacy instructions alone have limited effects on confidential access, while skill-level intrusiveness labels produce a modest reduction (24% on average), but combining the two roughly halves confidential access. Our findings motivate incorporating privacy annotations into skill specifications and evaluating their effectiveness alongside system-level instructions.
Sources
- BiasBusters: Uncovering and Mitigating Tool Selection Bias in Large Language Models
- CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data
- Securing AI Agents with Information-Flow Control
- Tool Preferences in Agentic LLMs are Unreliable
- What Affects the Stability of Tool Learning? An Empirical Study on the Robustness of Tool Learning Frameworks
- IDP-Bench: Benchmarking ability of LLMs to protect personal information in interdependent privacy contexts
- SoK: Agentic Skills -- Beyond Tool Use in LLM Agents
- ToolMinimize: Auditing and Rewriting LLM Agent Tool Calls to Minimize Privacy Exposure
- Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory
- gpt-oss-120b & gpt-oss-20b Model Card
- Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation
- Progent: Securing AI Agents with Privilege Control
- PrivacyAlign: Contextual Privacy Alignment for LLM Agents
- Contextualized Privacy Defense for LLM Agents
- Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward
- When Lower Privileges Suffice: Investigating Over-Privileged Tool Selection in LLM Agents
- Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation
- PrivacyPeek: Auditing What LLM-Based Agents Acquire, Not Just What They Say
- AgentDAM: Privacy Leakage Evaluation for Autonomous Web Agents
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