arXiv AI
Loose Lips: Understanding Privacy Leakage in Agentic Dialogue
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The Interlocutor Effect: Why LLMs Leak More Personal Data to Agents Than Humans
arXiv:2606. 09844v1 Announce Type: cross Abstract: Large Language Models (LLMs) alter their privacy behavior based on the perceived identity of their interlocutor.
arXiv AI
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NeuroFilter: Activation-Based Guardrails for Privacy-Conscious LLM Agents
arXiv:2601. 14660v2 Announce Type: replace-cross Abstract: Agentic Large Language Models (LLMs) are models able to reason, plan, and execute tools over unstructured data.
arXiv AI
Sep 25
PrivDrift: Auditing User-Secret Leakage Under Topic Drift in Active LLM Conversations
PrivDrift is a benchmark that tests whether user‑disclosed secrets can still be recovered by large language models after the conversation shifts to unrelated topics. It includes 1,000 controlled multi‑turn dialogues with seeded secrets, topic‑drift turns, and standardized extraction probes. Experiments on three LLMs with extended context windows show that dialogue‑level leakage remains substantial—between 38.7% and 54.6%—and is influenced by model, secret type, and persuasion intensity, while additional topic drift does not reliably reduce leakage.
arXiv AI
Jun 26
Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents
arXiv:2606. 26627v1 Announce Type: cross Abstract: Large language model agents increasingly query databases, search document collections, call external APIs, remember past interactions, and act on a user's behalf.
arXiv Machine Learning
4d ago
Disclosure Begets Disclosure: A Self-Reinforcing Privacy Loop in LLM Agents with Persistent Memory
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arXiv AI
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Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation
arXiv:2512. 16310v3 Announce Type: replace-cross Abstract: LLM-based agents increasingly use multiple external tools to complete complex tasks.