Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs
arXiv:2605.21541v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) remain vulnerable to transfer-based targeted attacks, where perturbations optimized on open-source surrogate encoders can generalize to closed-source MLLMs. A key challenge for improving adversarial transferability is to effectively capture the intrinsic visual focus shared across different models, such that perturbations align with transferable semantic cues rather than surrogate-specific behaviors. However, existing methods suffer from spatial-domain feature redundancy and surrogate-specific gradient s
The rapid deployment and increasing sophistication of MLLMs across various applications necessitate continuous research into their security vulnerabilities, especially concerning transferable attacks.
This research highlights a significant security vulnerability in multimodal large language models, indicating that attacks developed for open-source models can be transferred to closed-source commercial systems, posing risks to data integrity and model trustworthiness.
The understanding that MLLMs, even closed-source ones, can be exploited through transferable adversarial attacks refined on open-source surrogates means a more robust defense strategy considering cross-model vulnerabilities is urgently required.
- · Cybersecurity researchers
- · Security vendors
- · Developers of robust MLLM defense mechanisms
- · Developers of vulnerable MLLMs
- · Users relying on MLLMs for sensitive applications
- · Organizations with closed-source MLLM security assumptions
Increased focus on adversarial robustness and attack transferability in MLLM development.
New industry standards and regulatory frameworks for MLLM security, especially for deployed systems.
A potential slow-down in MLLM adoption for high-stakes applications if security concerns are not adequately addressed.
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Read at arXiv cs.LG