New vulnerabilities have emerged with the rapid advancement and adoption of multimodal foundational AI models, significantly expanding the potential for cybersecurity attacks. Researchers at Los Alamos National Laboratory have put forward a novel framework that identifies adversarial threats to foundation models—artificial intelligence approaches that seamlessly integrate and process text and image data. This work empowers system developers and security experts to better understand model vulnerabilities and reinforce resilience against ever more sophisticated attacks.
The study is published on the arXiv preprint server.
“As multimodal models grow more prevalent, adversaries can exploit weaknesses through either text or visual channels, or even both simultaneously,” said Manish Bhattarai, a computer scientist at Los Alamos.
“AI systems face escalating threats from subtle, malicious manipulations that can mislead or corrupt their outputs, and attacks can result in misleading or toxic content that looks like a genuine output for the model. When taking on increasingly complex and difficult-to-detect attacks, our unified, topology-based framework uniquely identifies threats regardless of their origin.”
Multimodal AI systems excel at integrating diverse data types by embedding text and images into a shared high-dimensional space, aligning image concepts to their textual semantic notion (like the word “circle” with a circular shape). However, this alignment capability also introduces unique vulnerabilities.
As these models are increasingly deployed in high-stakes applications, adversaries can exploit them through text or visual inputs—or both—using imperceptible perturbations that disrupt alignment and potentially produce misleading or harmful outcomes.
Defense strategies for multimodal systems have remained relatively unexplored, even as these models are increasingly used in sensitive domains where they can be applied to complex national security topics and contribute to modeling and simulation. Building on the team’s experience developing a purification strategy that…
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