The paper investigates how tool-call traces from large language model agents can reveal user attributes. Using the PrivacyTrace benchmark, the authors show that an attacker can infer user profiles with a 26.25% success rate from tool-call data, outperforming prior guessing methods. The study highlights that tool arguments and results are key evidence, and linking traces across sessions increases profiling risk.
arXiv:2608. 10042v1 Announce Type: cross Abstract: Tool-use LLMs are increasingly asked to act on users' behalf, but existing benchmarks usually focus on profile recall, style imitation, generic tool use, or response-level personalization.
The paper "Log-To-Leak: Prompt Injection Attacks on Tool-Using LLM Agents via the Model Context Protocol" investigates how natural-language metadata in the Model Context Protocol (MCP) can be exploited to inject malicious post‑hoc logging calls into large language model agents. The authors introduce the Log-To-Leak attack, which adds an attacker‑controlled call after a legitimate tool invocation, targeting user queries, MCP server names, server responses, and agent responses. Experiments across five MCP servers and four LLM agents show high attack success rates (up to 100%) and significant logging accuracy, while existing defenses still leave substantial residual risk.
arXiv:2606. 28061v1 Announce Type: cross Abstract: Large language models (LLMs) have increasingly moved from standalone text generation systems to agents that invoke external tools, access environments, and execute multi-step tasks.
arXiv:2603. 07473v2 Announce Type: replace-cross Abstract: The Model Context Protocol (MCP) is an open and standardized interface that enables large language models (LLMs) to interact with external tools and services, and is increasingly adopted by AI agents.
The paper introduces ASLEval, a framework for measuring privacy exposure displacement in large language model (LLM) agent sessions. It highlights that traditional local proxies—such as inspecting a single action or final response—often miss unauthorized data leaks elsewhere in a multi-step session. ASLEval pre-registers hidden target sets, tracks all declared visible exits, and preserves internal traces for diagnosis, revealing that a single outlet view can overlook nearly 47% of exposure and that internal evidence typically precedes visible leaks. The study underscores the need for benchmarks that define complete visible boundaries, ground claims in pre-specified targets, and report privacy alongside task utility.
arXiv:2607. 25297v1 Announce Type: cross Abstract: The rapid development of large language model (LLM) agents has enabled their broad adoption across diverse real-world tasks.
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:2606. 00152v1 Announce Type: cross Abstract: LLM-based agents are rapidly advancing, autonomously invoking external tools to complete multi-step tasks for users.
RouteScan is a non‑intrusive auditing framework that detects harmful behavior in Mixture‑of‑Experts (MoE) large language models by analyzing expert‑routing telemetry captured from GPU execution. It uses the number of active GPU threads during the prefilling phase as a micro‑architectural fingerprint to isolate cross‑domain risk indicators and precisely identify malicious prompts. Evaluations on four open‑source MoE LLMs show strong generalization with AUROC > 0.91 on unseen harmful domains, while privacy tests indicate that full prompts cannot be reliably recovered from aggregated telemetry.
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.
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.
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.