VirusCascade: Hijacking Collaborative Reflection in LLM-Powered Recommender Agents
Advancing beyond traditional static scoring models, LLM-powered agentic recommender systems (LLM-ARS) instantiate users and items as autonomous agents, whose semantic states are dynamically refined through a recurrent process known as collaborative reflection. While this mechanism improves recommendation quality, it simultaneously introduces a systemic vulnerability: adversarial evidence injected into a single agent can be rationalised into a legitimate preference narrative, written back into memory, and propagated to other agents through interaction contexts. We term the local rationalisation process reflection laundering, and its system-wide escalation through collaborative reflection c...
| Authors | Yurong Hao, Wen Zhou, Guowei Guan et al. |
|---|---|
| Primary category | cs.CR |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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