Do Agents Dream of False Memories? Black-box Visual Attacks on Long-term Memory in Multimodal AI Agents
Black-box attack framework demonstrates how adversaries can inject false memories into multimodal AI agents via manipulated images, undermining long-term reasoning integrity.
Summary written by editorial AI · Source link below
arXiv:2607.15657v1 Announce Type: new Abstract: Multimodal AI agents increasingly rely on persistent long-term memory to ground generation in past visual and textual episodes. We show that unconditional trust in visual data creates a critical vulnerability. We propose Lucid, a black-box adversarial framework that compromises multimodal memory pipelines under a strictly image-bounded threat model, requiring no access to the target MLLM, target retrieval encoder, or the text channel. Lucid crafts
Editorial Analysis
As enterprises adopt multimodal AI agents with persistent memory, this attack vector could allow adversaries to silently corrupt decision-making over time — a risk that must be addressed before production deployment.
Mandate integrity validation for all visual data consumed by AI agents and include memory-poisoning scenarios in your AI red-team programme.
Research shows autonomous AI agents can be tricked into retaining fabricated memories from adversarial images, creating a new class of integrity risk for agent-driven workflows.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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