MaskClaw: Edge-Side Personalized Privacy Arbitration for GUI Agents with Behavior-Driven Skill Evolution
arXiv:2605.28646v2 Announce Type: replace-cross Abstract: GUI agents rely on screenshots to infer intent and operate across applications, but these screenshots often contain private messages, medical records, payment credentials, and workplace-specific workflows. Privacy decisions in this setting depend on task, recipient, application state, and user role, yet static PII detectors miss these boundaries and cloud-side VLM reasoning can upload the raw screen before deciding what should be protected. We present MaskClaw, an edge-side privacy arbitrator for GUI agents. MaskClaw extracts local visu
The rapid advancement of GUI agents and their reliance on visual data necessitates real-time, on-device privacy solutions as concerns about data security and PII exposure escalate.
This development addresses a critical vulnerability in AI agent deployment, enabling wider adoption by mitigating privacy risks inherent in screen-based interaction, especially for sensitive enterprise or personal data.
The shift to edge-side privacy arbitration changes how AI agents process visual information, fundamentally enhancing local data protection and reducing reliance on cloud-based PII filtering.
- · AI agent developers
- · Enterprise users
- · Privacy-focused tech companies
- · Edge AI hardware manufacturers
- · Cloud-based PII detection services
- · Malicious data exfiltrators
- · Applications with weak privacy controls
GUI agents can now operate more securely in sensitive environments without transmitting raw visual data to the cloud.
This increases enterprise adoption of AI agents, accelerating automation of white-collar tasks.
The enhanced trust in AI agents could lead to new regulatory frameworks for on-device AI privacy and data handling standards.
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Read at arXiv cs.CL