Progressive Behavioral Drift through Compression Valleys in Large Language Models
Attention sinks and compression valleys in decoder-only Transformers create exploitable regions where small perturbations cascade into progressive behavioural drift.
Summary written by editorial AI · Source link below
arXiv:2511.17194v2 Announce Type: replace Abstract: We show that attention sinks and compression valleys create a vulnerable region in decoder-only Transformers, where small activation perturbations can be amplified through the autoregressive trajectory. Based on this, we propose Sensitivity-Scaled Steering (SSS), a progressive activation-space attack that anchors perturbations at the beginning-of-sequence token and adaptively reinforces them at sensitive layers and tokens. Instead of forcing a
Editorial Analysis
Enterprises deploying autoregressive LLMs should be aware that architectural features like attention sinks can be weaponised for subtle output manipulation.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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