Black-box Visual Attacks on Long-term Memory in Multimodal AI Agents
Researchers introduce Lucid, a black-box adversarial framework that targets multimodal AI agents by crafting imperceptible perturbations to images, compromising their long-term memory pipelines. Lucid enables two attack modes—memory poisoning and memory injection—achieving 61.6% and 58.4% attack success rates, respectively, across five black-box memory architectures, including commercial systems. The attacks require no access to the target model or text channel, operating solely through manipulated visual inputs.
Why it matters: This work exposes a critical vulnerability in multimodal AI agents' reliance on visual data for persistent memory, highlighting the risk of adversarial manipulation through images alone.
Full story at: arXiv Cryptography and Security ↗