arXiv:2512.16310v3 Announce Type: replace-cross Abstract: LLM-based agents increasingly use multiple external tools to complete complex tasks. We study Tools Orchestration Privacy Risk (TOP-R): an agent may combine individually non-sensitive tool returns and disclose an unintended sensitive conclusion. We formalize TOP-R with three conditions: conclusion sensitivity, single-source non-inferability, and compositional inferability. We introduce LRSE (Library-Grounded Reverse-Inference Seed Expansion), a four-library reverse-construction pipeline grounded in privacy norms, reasoning chains, tool
The rapid advancement and deployment of LLM-based agents, particularly in complex task execution, necessitate immediate attention to emerging privacy and security vulnerabilities like those outlined in TOP-R.
This research highlights a critical, previously underexplored privacy risk in AI agents, impacting data security, trust, and regulatory frameworks for sophisticated AI systems.
The understanding of AI agent security shifts from individual tool vulnerabilities to the emergent risks from tool orchestration, requiring new assessment methodologies and mitigation strategies.
- · AI security researchers
- · Privacy-focused AI developers
- · Regulators and compliance bodies
- · Cybersecurity firms specializing in AI
- · AI developers lacking privacy-by-design expertise
- · Users of unsecure agent systems
- · Companies handling sensitive data with poorly designed AI agents
AI agent developers will need to integrate new privacy risk assessments and mitigation techniques into their design and deployment workflows.
Increased regulatory scrutiny and potential for new compliance standards specifically targeting AI agent privacy risks will emerge, impacting development cycles and costs.
Public perception of AI agent safety could be significantly swayed by high-profile privacy breaches, potentially slowing adoption or fostering a more cautious approach to AI integration in sensitive domains.
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Read at arXiv cs.CL