面向社交推理的编程：链式语义小部件劫持大型视觉语言模型

大型视觉语言模型 (LVLMs) 进行安全对齐以抑制有害内容。然而，当前的防御主要针对输入表示中显式的恶意模式，常常忽视了组合推理中固有的漏洞。在本文中，我们识别出一个系统性缺陷：LVLMs 可被诱导合成来自良性前提的有害逻辑。我们将这种攻击范式 formalize 为 "Reasoning-Oriented Programming"，drawing a structural analogy to Return-Oriented Programming in systems security。 Just as ROP circumvents memory protections by chaining benign instruction sequences, our approach exploits the model's instruction-following capability to orchestrate a semantic collision of orthogonal benign inputs。 We instantiate this paradigm via \tool{}, an automated framework that optimizes for \textit{semantic orthogonality} and \textit{spatial isolation}。 By generating visual gadgets that are semantically decoupled from the harmful intent and arranging them to prevent premature feature fusion, \tool{} forces the malicious logic to emerge only during the late-stage reasoning process. This effectively bypasses perception-level alignment. We evaluate \tool{} on SafeBench and MM-SafetyBench across 7 state-of-the-art 0.LVLMs, including GPT-4o and Claude 3.7 Sonnet. Our results demonstrate that \tool{} consistently circumvents safety alignment, outperforming the strongest existing baseline by an average of 4.67\% on open-source models and 9.50\% on commercial models。