Reasoning models have attracted increasing attention for their ability to tackle complex tasks, embodying the System II (slow thinking) paradigm in contrast to System I (fast, intuitive responses). Yet a key question remains: Does slower reasoning necessarily lead to more truthful answers? Our findings suggest otherwise. We conduct the first systematic study of the inverse scaling law in slow-thinking paradigms for multimodal reasoning. We find that when confronted with incomplete or misleading visual inputs, slow-thinking models are more prone to fabricating plausible yet false details to justify untruthful reasoning. To analyze this behavior, we construct a 5,000-sample hierarchical prompt dataset annotated by 50 human participants. The prompts progressively increase in complexity, revealing a consistent pattern: slower reasoning models tend to follow depth-first search (DFS) thinking, persistently exploring flawed premises, while faster chat models favor breadth-first search (BFS) inference, showing greater caution under uncertainty. These findings reveal a critical vulnerability of reasoning models: while effective in structured domains such as math, their DFS-style reasoning becomes fragile when confronted with ambiguous, multimodal inputs.