TIDES: Test-time Inference Drift Exploitation via Scaling

Haoran Dai, Haozheng Luo, Haotian Zhang, Meng lin, Yan Chen, Binghui Wang

Keywords: Large Reasoning Model, Test time scaling, Backdoor Attack

TL;DR: We propose TIDES, a reasoning-attacking method that exposes a previously unrecognized failure of test-time scaling: as reasoning traces lengthen, model performance degrades sharply rather than improves.

Abstract: We propose TIDES, a reasoning-attacking method that exposes a previously unrecognized failure of test-time scaling: as reasoning traces lengthen, model performance degrades sharply rather than improves. Unlike prior attacks on large reasoning models (LRMs), TIDES exploits the intrinsic properties of test-time scaling laws to manipulate reasoning trace length, producing degradations that are inherently difficult to detect. Methodologically, we define Depth-Guided Latent Tracker (DLT), a depth-based tracker that injects microscopic steering vectors into intermediate reasoning traces stealthily and combines them with on-policy distillation to precisely position LRMs under test-time scaling. Theoretically, we model latent space as a depth-indexed dynamic process and prove that under test-time scaling, small bounded perturbations introduced at intermediate layers induce non-vanishing trajectory drift, explaining why DLT remains effective yet difficult to detect in large reasoning models. Empirically, we evaluate TIDES on multiple reasoning benchmarks using two strong reasoning models, DeepSeek-R1-Distill-Qwen-7B, and DeepSeek-R1-Distill-Llama-8B, where it consistently outperforms state-of-the-art reasoning attack methods such as DecepChain and BadChain. Notably, TIDES delivers an average 30.3% improvement in attack performance over the baselines, demonstrating that TIDES remains efficient within large reasoning model generation.

Submission Number: 31