The Uncontrolled Variable: Vision-Language Model Refusal Responds to Image Presence in Ways Risk Cannot Explain

arXiv:2609.26174v1 Announce Type: cross Abstract: Vision-Language Model (VLM) safety is expected to depend on what a request asks for. We show that safety-aligned VLMs also key refusal on a property of a request's form: whether an image is attached, holding everything the request asks fixed. Attaching a blank canvas - unreadable, unrelated to the request, identical across prompts - shifts refusal by tens of percentage points, with no defense in the loop. The shift is not blanket caution. Neutral instructions are almost unaffected while borderline-benign prompts move sharply, so the cost falls on sensitivity-adjacent traffic: benign questions about privacy, self-harm and violence. Attachment alone is sufficient, while the image's properties set the price: a black canvas costs substantially more than a white one of identical size, and on an open checkpoint the carrying axis is pixel count. Nor is the shift under instructional control - telling the model the image is a placeholder to be disregarded removes only a fraction of it, and on one model asserting that an attachment exists moves refusal substantially with nothing attached. Attachment may correlate with risk in deployment; what these models do with it does not track risk. It is not the serving stack, since the same weights reached two ways behave alike, nor a property of VLMs as such, since several open-weight checkpoints show nothing. It belongs to particular aligned checkpoints, one of them open. It is also decoupled from what it buys: the canvas does prevent some attack success on a matched harmful set, but far less than it costs, and its sign is not fixed - on one open model the identical canvas makes the model markedly easier to attack. Image presence is not a default a deployer chose or priced; it is an uncontrolled variable inherited with the weights.