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New research reveals Cross-User Poisoning vulnerability in collaborative AI agents

By PulseAugur Editorial · [1 sources] ·

A new research paper identifies a security vulnerability called Cross-User Poisoning (CUP) in multi-user collaborative language agents. This vulnerability allows adversaries to inject malicious messages into a shared context, leading to unauthorized actions or responses when the agent serves a benign user. The research demonstrates CUP's effectiveness on deployed agents like Continua and ElizaOS across various domains, showing it persists across interactions and is more potent than standard prompt-injection attacks. While context summarization alone is insufficient to prevent CUP, defenses like task clustering and provenance prompting significantly reduce its propagation. AI

IMPACT Highlights critical security flaws in multi-user AI agents, necessitating new defenses for secure collaboration.

RANK_REASON Research paper detailing a new security vulnerability in AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research reveals Cross-User Poisoning vulnerability in collaborative AI agents

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Research paper detailing a new security vulnerability in AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Atharv Singh Patlan, Peiyao Sheng, S. Ashwin Hebbar, Prateek Mittal, Pramod Viswanath ·

    Cross-User Poisoning: User-Task Boundary Failures in Multi-User Collaborative Language Agents

    arXiv:2511.17671v2 Announce Type: replace-cross Abstract: Language agents are moving from single-user assistants to shared collaborators in workspaces, forums, and group chats. A shared agent observes interleaved messages from multiple users, maintains persistent context, and exe…

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