arXiv:2508.08521v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) are increasingly being used in a broad range of applications, bringing their security and behavioral control to the forefront. While existing approaches for behavioral control or output redirection, like system prompting in VLMs, are easily detectable and often ineffective, activation-based steering vectors require invasive runtime access to model internals--incompatible with API-based services and closed-source deployments. We introduce VISOR (Visual Input-based Steering for Output Redirection), a novel me
The increasing deployment of VLMs across diverse applications necessitates robust and user-friendly methods for controlling their behavior and output in a secure manner.
This research addresses a critical limitation in VLM control, offering a novel method that could improve the safety and reliability of AI systems, particularly in closed-source or API-driven environments.
The ability to steer VLM output via visual input reduces reliance on internal model access or easily circumvented text prompts, making behavioral control more practical and resilient.
- · AI developers
- · API-based VLM services
- · Enterprises deploying VLMs
- · AI safety researchers
- · Malicious actors attempting VLM exploits
- · Less sophisticated VLM control methods
VLMs become more controllable and safer to deploy in sensitive applications.
Increased trust in VLM outputs could accelerate their integration into critical infrastructure and decision-making processes.
The development of highly robust visual steering could lead to new forms of human-AI interaction and dynamic content generation based purely on visual cues.
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Read at arXiv cs.AI