ETA: Evaluating Then Aligning Safety of Vision Language Models at Inference-Time

ICLR 2025 Conference Submission675 Authors

Keywords: VLMs, safety alignment, inference-time

TL;DR: We propose a robust inference-time alignment framework, ETA, to safeguard VLMs and enhance both safety and usefulness.

Abstract: Vision Language Models (VLMs) have become essential backbones for multi-modal intelligence, yet significant safety challenges limit their real-world application. While textual inputs can often be effectively safeguarded, adversarial visual inputs can often easily bypass VLM defense mechanisms. Existing defense methods are either resource-intensive, requiring substantial data and compute, or fail to simultaneously ensure safety and usefulness in responses. To address these limitations, we propose a novel two-phase inference-time alignment framework, **E**valuating **T**hen **A**ligning (ETA): i) Evaluating input visual contents and output responses to establish a robust safety awareness in multimodal settings, and ii) Aligning unsafe behaviors at both shallow and deep levels by conditioning the VLMs' generative distribution with an interference prefix and performing sentence-level best-of-$N$ to search the most harmless and helpful generation paths. Extensive experiments show that ETA outperforms baseline methods in terms of harmlessness, helpfulness, and efficiency, reducing the unsafe rate by 87.5\% in cross-modality attacks and achieving 96.6\% win-ties in GPT-4 helpfulness evaluation.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 675