SecureChain-VLM: Path-Level Adversarial Defense for Vision-Language Models in High-Risk Decision Environments
Keywords:
adversarial defense, vision-language models, path-level intervention, multimodal robustness, high-risk decision systems, secure AI deployment, infrastructure governanceAbstract
The integration of vision-language models into critical decision-making systems, such as autonomous driving, medical diagnosis, and security surveillance, introduces unprecedented vulnerabilities to adversarial perturbations. Existing defense mechanisms, including adversarial training, input preprocessing, and robust optimization, often focus on either the visual or linguistic modality independently, leaving cross-modal attack surfaces undefended. This paper introduces SecureChain-VLM, a path-level adversarial defense framework that systematically models the inference pathway of multimodal models as a chain of latent representations and applies targeted interventions at strategically chosen nodes. The proposed architecture leverages a hierarchical graph of computational paths, each corresponding to a distinct combination of visual and textual features, to detect and mitigate adversarial manipulations before they propagate to high-level decisions. We analyze the structural trade-offs between defense granularity, computational overhead, and decision accuracy in high-risk environments, drawing on cross-domain comparisons with established defense strategies in autonomous systems and critical infrastructure. Furthermore, we discuss governance and deployment considerations, including auditability, fairness across demographic subgroups, and sustainability of continual retraining. Through system-level evaluation on benchmark multimodal datasets and simulated high-risk scenarios, SecureChain-VLM demonstrates a significant reduction in attack success rates while maintaining task fidelity. The paper concludes with policy implications for deploying robust multimodal AI in regulated sectors and outlines future research directions in path-level verification and adaptive defense calibration.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
