Harmfulness Propagation Dynamics: Layer-wise Trajectories of Adversarial Intent in Large Language Models

Researchers identified Harmfulness Propagation Dynamics (HPD), a cross-layer signature in which the projection of the last-token hidden state onto a learned harm direction rises monotonically with transformer depth for harmful prompts while benign prompts stay flat or oscillatory, according to an arXiv paper (arXiv:2609.13534v1). Building on HPD, the authors introduced HERALD (Harmful Encoding Recognition via Activation Layer Dynamics), a 288-parameter MLP moderator that stores one d-dimensional direction per layer (262 KB for a 32-layer, d=4096 model), adds only 2.6×10⁻⁶ prefill FLOPs at inference, and requires no gradient computation during training. Across eight prompt-harmfulness benchmarks and four model families, HERALD reached an average F1 of 89.3 on OLMo2-7B, beating all tested guard models on adversarial jailbreak detection (98.4 vs. 96.9 F1) and outperforming prior latent-based methods by 2.3–4.1 F1 points on every backbone.

arXiv:2609.13534v1 Announce Type: new Abstract: We identify \textbf{Harmfulness Propagation Dynamics HPD }: for harmful prompts, the projection of the last-token hidden state onto a learned harm direction rises monotonically with transformer depth, whereas benign prompts remain flat or oscillatory. This cross-layer signature reflects harmful intent as a \emph{progressively resolved} semantic property: surface form appears early, while pragmatic intent consolidates later, making the \emph{trajectory shape} more informative than any single-layer snapshot. Moreover, LDA-based harm directions, learned per layer, remain stable across random splits pairwise cosine similarity $ 0.97$ , supporting the projection sequence as a reproducible structured signal. Building on HPD, we introduce \textbf{\herald{}} \textbf{H}armful \textbf{E}ncoding \textbf{R}ecognition via \textbf{A}ctivation \textbf{L}ayer \textbf{D}ynamics . This lightweight input moderator extracts a seven-dimensional feature record, slope, curvature, monotonicity, onset layer, and related statistics from the cross-layer projection sequence and classifies it with a 288-parameter MLP. \herald{} stores one $d$-dimensional direction per layer $262$\,KB for a 32-layer, $d{=}4096$ model , requires no gradient computation during training, and adds only $2.6{\times}10^{-6}$ prefill FLOPs at inference. Across eight prompt-harmfulness benchmarks and four model families, \herald{} achieves an average F1 of $89.3$ on OLMo2-7B, surpassing all tested guard models on adversarial jailbreak detection $98.4$ vs.\ $96.9$ F1 and outperforming prior latent-based methods by $2.3$-$4.1$ F1 points on every backbone. Per-instance trajectories provide machine-readable audit records that reveal \emph{when} and \emph{how} harmfulness emerges, offering an interpretability advantage over single-layer approaches.