TIDES: Test-time Inference Drift Exploitation via Scaling

We propose TIDES, a reasoning attack method that exposes a previously unrecognized failure mode of test-time scaling: as reasoning traces lengthen, model performance can degrade sharply rather than improve. Unlike prior attacks on large reasoning models (LRMs), TIDES exploits computation depth under test-time scaling as an implicit activation condition, producing degradations that remain difficult to detect under short-budget evaluations. Methodologically, we define a Depth-Guided Latent Tracker (DLT), a depth-based tracker that injects microscopic steering vectors into intermediate reasoning representations and combines them with on-policy distillation to embed computation-dependent behavior in LRMs. Theoretically, we model latent space as a depth-indexed dynamic process and derive a worst-case propagation bound characterizing the potential sensitivity of long reasoning trajectories to small intermediate perturbations. Empirically, we evaluate TIDES on multiple reasoning benchmarks using three strong reasoning models, Phi-4-Reasoning, DeepSeek-R1-Distill-Qwen-7B, and DeepSeek-R1-Distill-Llama-8B, where it outperforms strong reasoning attack methods such as DecepChain and BadChain. Notably, TIDES achieves an average 26.8% relative improvement in RAS over the strongest baseline while largely preserving utility under short reasoning budgets.