ML Security PapersThe Laminar Flow Hypothesis: Detecting Jailbreaks via Semantic Turbulence in Large Language Models
Md. Hasib Ur Rahman · BRAC University
Detects LLM jailbreaks by measuring latent-space trajectory variance, exposing distinct conflict signatures in RLHF-aligned vs. SFT models
As Large Language Models (LLMs) become ubiquitous, the challenge of securing them against adversarial "jailbreaking" attacks has intensified. Current defense strategies often rely on computationally expensive external classifiers or brittle lexical filters, overlooking the intrinsic dynamics of the model's reasoning process. In this work, the Laminar Flow Hypothesis is introduced, which posits that benign inputs induce smooth, gradual transitions in an LLM's high-dimensional latent space, whereas adversarial prompts trigger chaotic, high-variance trajectories - termed Semantic Turbulence - resulting from the internal conflict between safety alignment and instruction-following objectives. This phenomenon is formalized through a novel, zero-shot metric: the variance of layer-wise cosine velocity. Experimental evaluation across diverse small language models reveals a striking diagnostic capability. The RLHF-aligned Qwen2-1.5B exhibits a statistically significant 75.4% increase in turbulence under attack (p less than 0.001), validating the hypothesis of internal conflict. Conversely, Gemma-2B displays a 22.0% decrease in turbulence, characterizing a distinct, low-entropy "reflex-based" refusal mechanism. These findings demonstrate that Semantic Turbulence serves not only as a lightweight, real-time jailbreak detector but also as a non-invasive diagnostic tool for categorizing the underlying safety architecture of black-box models.