arXiv AI
When Agents Talk, Secrets Travel: Topology-Driven Leakage and Recipient-Conditioned Sanitization in Multi-Agent LLM Systems
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arXiv AI
Jun 16
AgentLeak: A Benchmark for Internal-Channel Privacy Leakage in Multi-Agent LLM Systems
arXiv:2602. 11510v3 Announce Type: replace Abstract: Multi-agent Large Language Model (LLM) systems create privacy risks that current output-only benchmarks cannot measure.
arXiv AI
2d ago
Judging Before Propagation: Task-Graph Contextual Integrity for Multi-Agent Privacy
The paper introduces Task-Graph Contextual Integrity (TG‑CI), a formalism that extends traditional Contextual Integrity to multi‑agent systems by modeling workflows as Multi‑Agent Task Graphs (MATGs). TG‑CI evaluates privacy not just at individual disclosures but across the entire downstream propagation, offering two enforcement policies—Full‑Scope and Auto‑Region—that balance privacy protection with task performance. A new benchmark, MAPLE‑Bench, demonstrates that TG‑CI can cut unauthorized disclosures by up to 94% while preserving over 94% of task accuracy compared to existing local‑history CI methods.
arXiv AI
Jun 2
PrivacyPeek: Auditing What LLM-Based Agents Acquire, Not Just What They Say
arXiv:2606. 00152v1 Announce Type: cross Abstract: LLM-based agents are rapidly advancing, autonomously invoking external tools to complete multi-step tasks for users.
arXiv AI
Sep 15
Confuse the Model, Control the Flow: Understanding and Mitigating Privacy Leakage from LLM Agents with Information Flow Control
arXiv:2609.14003v1 Announce Type: cross Abstract: Personal AI agents built on large language models (LLMs) are increasingly given access to a user's private data and communications in order to provid...
arXiv AI
Jun 4
Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs
arXiv:2512. 04668v4 Announce Type: replace-cross Abstract: Graph topology is a fundamental determinant of memory leakage in multi-agent LLM systems, yet its effects remain poorly quantified.
arXiv AI
6d ago
InferPriv: Evaluating Inferential Privacy Leakage in LLM Assistants
InferPriv is a new benchmark that evaluates inferential privacy leakage in large language model (LLM) assistants. It measures whether an honest-but-curious recipient can deduce a user’s sensitive attributes from an LLM’s reply during routine tasks, a type of leakage not captured by existing direct‑leakage benchmarks. Testing five frontier models, the study finds that inferential leakage rates (16.2–25.2%) far exceed direct leakage, even when direct leakage is near zero under privacy‑aware prompts.