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原文摘要

Text-steerable visual encoders condition a frozen Vision Transformer on a natural-language prompt and point at the matching image region, producing prompt-conditioned heatmaps and features. We audit the released SteerViT model and find that its steering interface turns language-driven pointing into demographic profiling. On counterfactual face mosaics and real multi-person photographs, "the person" selects a man more often than the base rate, crime words select Black people more often than occupation words do, "the man" among four women selects the Black woman 75.6% of the time on SocialCounterfactuals, and "the monkey" selects Black people. None of them is in the backbone's unsteered saliency, and the crime association is not in the frozen text encoder; the race effects grow with steering strength and enter through the last two cross-attention layers: giving the prompt to those layers alone reproduces them. Four other grounding models, CLIPSeg, Grounding-DINO, SAM 3, and Qwen2.5-VL, show no crime routing and weaker race routing. The model cannot refuse. Its patch softmax must place its mass somewhere, and its heatmap separates a groundable request ("the police officer") from one that is absent ("the pizza") or that would require a stereotyped demographic inference ("the criminal") only at chance (AUROC 0.52). We introduce SafeSteer, which adds a learned abstention output and counterfactual-invariance training on the same 23M steering parameters. SafeSteer separates should-point from should-abstain prompts at AUROC 0.997, generalizes to held-out sensitive and absent words (0.998; at a 0.5 threshold it refuses 99.8% of trained and 62% of held-out sensitive words) with no over-refusal of visible people on the benchmark, and removes most race routing while keeping localization and retrieval within 1 point of the released model. Code, benchmarks, and the curated mosaic sets will be released.