ReShift: Aha-Moment-Driven Reasoning-Level Backdoor Attacks on Vision–Language Models

Vision–Language Models (VLMs) are increasingly deployedin safety-critical applications, yet remain vulnerable to backdoor attacks.Existing methods primarily manipulate final outputs, often producingreasoning traces that are inconsistent or easily detectable. In this paper,we propose ReShift, the novel aha-moment-driven reasoning-level back-door framework that explicitly redirects the internal chain-of-thought(CoT) trajectory while preserving surface-level coherence. ReShift intro-duces a Poisoned Reasoning-Aware Data Construction (PRDC) pipelineand a Supervised–Reinforcement Joint Optimization (SRJO) strategyto induce stable trigger-conditioned reasoning shifts. We further for-malize Entropy Rebound as a principled signal for characterizing rea-soning redirection and provide theoretical guarantees linking entropygaps to trajectory-level divergence. Extensive experiments demonstratethat ReShift achieves high attack success rates while maintaining clean-task performance and realistic reasoning traces, substantially improv-ing stealthiness against existing defenses. Code can be found at https://github.com/AlbertZhaoCA/ReShift.