AI Research
Harmfulness Propagation Dynamics: Layer-wise Trajectories of Adversarial Intent in Large Language Models
Researchers map the internal trajectory of adversarial intent within transformer architectures.
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 ...
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ArXiv CS.CL
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ArXiv CS.CL
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Generated insight by OnAirToday AI
This research identifies a distinct internal signature for adversarial intent that emerges predictably as data traverses a model's layers. By mapping how harmfulness manifests in hidden states, developers can intercept malicious prompts more effectively before final output generation.
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