Long-term memory can turn untrusted external content into persistent influence over an LLM agent's future decisions, creating the threat of indirect memory poisoning. A successful attack must survive a multi-stage pipeline comprising memory writing, retrieval, and utilization. Existing attacks largely rely on intra-sta...
Chuan-Chao Zang, Jianing Wang, Wenyu Chen et al.· 0 citations
It is shown that feedback-based agents can retain early biases even when later correction is available, and a black-box framework for exploiting this weakness is proposed that operationalizes the three factors as directional-shift, contextual-plausibility, and counterevidence-resilience signals under either limited tar...
Chuan-Chao Zang, Jia-Ning Wang, Wen-Yu Chen et al.· 0 citations
This paper proposes ToolSiphon, a query-only extraction attack that introduces two complementary signals: a target-discriminative signal, implemented through Tool Contrastive Analysis, to steer queries toward the target tool; and a response-grounded factual signal, implemented through Evidence Chained Feedback, to miti...
Chuan-Chao Zang, Jia-Ning Wang, Wen-Yu Chen et al.· 0 citations
Control evaluations reveal that targeted poisoning risk varies across memory operations and motivate stage-aware evaluation and control of LLM-agent memory, showing that targeted poisoning risk varies across memory operations.
Chuan-Chao Zang, Zi-Jian Cao, Xiang-Tao Meng et al.· 0 citations
The first extraction attack designed for this threat model, SPORE decouples the adversarial command from retrieval anchors by persisting the command in short-term memory and emitting semantically pure anchors in tool responses, demonstrating that memory isolation alone is insufficient and call for reexamining tool-side...
Scaling Large Language Models (LLMs) has been driven mainly by enlarging the Transformer backbone, but for an already-strong model this requires another round of costly pretraining. We study whether an existing backbone can keep improving by allocating more computation to each token while leaving the Transformer backbo...
Aiwei Liu, Cheng Shi, Chuhan Wu et al.· 2 citations
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