Title: VISOR++ : VISUAL INPUT BASED STEERING FOR LARGE VISION LANGUAGE MODELS
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: model steering, vision language models, adversarial machine learning

Abstract: As Vision Language Models (VLMs) are deployed across safety-critical applications, understanding and controlling their behavioral patterns has become increasingly important. Existing behavioral control methods face significant limitations: system prompting is a popular approach but could easily be overridden by user instructions, while applying activation-based steering vectors requires invasive runtime access to model internals, precluding deployment with API-based services and closed-source models. Finding steering methods that transfer across multiple VLMs is still an open area of research. To this end, we introduce universal visual input based steering for output redirection (VISOR++), a novel approach that achieves behavioral control through optimized visual inputs alone. We demonstrate that a single VISOR++ image can be generated for an ensemble of VLMs that by itself can emulate each of their steering vectors. By crafting universal visual inputs that induce target activation patterns, VISOR++ eliminates the need for runtime model access while remaining deployment-agnostic. This means that when an underlying model supports multimodal capability, model behaviors can be steered by inserting an image input completely replacing runtime steering vector based interventions. We first demonstrate the effectiveness of the VISOR++ images on open-access models such as LLaVA-1.5-7B and IDEFICS2-8B along three alignment directions: refusal, sycophancy and survival instinct. Both the model-specific steering images and the jointly optimized images achieve performance parity closely following that of steering vectors for both positive and negative steering tasks. We also show the promise of VISOR++ images in achieving directional behavioral shifts for unseen models that include both open-access and closed-access models. At the same time, VISOR++ images are able to preserve 99.9\% performance on 14,000 unrelated MMLU evaluation tasks highlighting their specificity to inducing only behavioral shifts.

TL;DR: The paper proposes a novel way of using a single image to mimic steering vectors for an ensemble of vision language models