GUIDE: Self-Consistent Geometric Deception in Spatial Foundation Models

Spatial foundation models jointly predict camera poses, depth, and 3D points from shared visual features, making geometric consistency a useful signal for validating their predictions. However, consistency among these outputs does not necessarily imply correctness, as coordinated errors may remain mutually compatible and pass internal geometric checks. We propose GUIDE (Geometric Untruths In Dependent Estimates), the first attack that systematically constructs self-consistent geometric deception against spatial foundation models. Specifically, GUIDE first specifies a target camera trajectory and constructs shared world anchors under the target geometry. It then projects these anchors across views to derive compatible depth and point targets. Finally, it optimizes bounded image perturbations to jointly steer the model toward these prescribed geometric predictions. We evaluate GUIDE on three models and five datasets. GUIDE achieves 29/36 joint successes, compared with 12/36 for a camera-only attack. We further evaluate its downstream impact on depth-label generation and robot control, where GUIDE leads to 103/144 accepted erroneous depth predictions and reduces task success by 39.5 and 37.4 percentage points for FALCON and R3DP, respectively.