most citedMemory Injection Attacks on LLM Agents via Query-Only Interaction

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

math-ph2026

Counterexamples to the total-current Hohenberg-Kohn conjecture

Andre Laestadius, Mihály A. Csirik

The total-current Hohenberg-Kohn conjecture asserts that the ground-state particle density and total current determine the scalar potential up to an additive constant and the vecto…

cs.AI2026

Agent MechSuits: Mechanistic Subspace Safety Steering for Multi-Turn CLI Agents

Weidi Luo, Qiming Zhang, Yihao Quan +5

Command-Line Interface (CLI) agents based on large language models (LLMs) demonstrate remarkable autonomous capabilities, but they also introduce significant safety and misuse risk…

cs.AI2026

Palette: A Modular, Controllable, and Efficient Framework for On-demand Authorized Safety Alignment Relaxation in LLMs

Qitao Tan, Xiaoying Song, Arman Akbari +7

Current safety alignment of foundation models largely follows a \emph{one-size-fits-all} paradigm, applying the same refusal policy across users and contexts. As a result, models m…

cs.CR2025

Comprehensive Vulnerability Analysis is Necessary for Trustworthy LLM-MAS

Pengfei He, Yue Xing, Juanhui Li +7

TThis paper argues that \textbf{a comprehensive vulnerability analysis is essential for building trustworthy Large Language Model-based Multi-Agent Systems (LLM-MAS)}. These system…

cs.LG2025★ 1 cited

Memory Injection Attacks on LLM Agents via Query-Only Interaction

Shen Dong, Shaochen Xu, Pengfei He +5

Agents powered by large language models (LLMs) have demonstrated strong capabilities in a wide range of complex, real-world applications. However, LLM agents with a compromised mem…

cs.CR2025

Multi-Faceted Studies on Data Poisoning can Advance LLM Development

Pengfei He, Yue Xing, Han Xu +2

The lifecycle of large language models (LLMs) is far more complex than that of traditional machine learning models, involving multiple training stages, diverse data sources, and va…

cs.CR2025

Unveiling Privacy Risks in LLM Agent Memory

Bo Wang, Weiyi He, Shenglai Zeng +4

Large Language Model (LLM) agents have become increasingly prevalent across various real-world applications. They enhance decision-making by storing private user-agent interactions…