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Preprint Sep 2026

Where Do LLMs Decide to Break the Rules? Mechanistic Localization of Prompt Injection Compliance

When a prompt injection attack succeeds, a Large Language Model (LLM) abandons its assigned system role to comply with an adversarial instruction. While prior work has extensively quantified how often this occurs, we ask a more fundamental question: where inside the network does the model actually decide to break the r...

Rui Wen, Jia-Yang Liu, Ze-Yu Yang et al. · 0 citations
#machine learning Preprint Sep 2026

No Free Efficiency: Revisiting the Trade-off Between Training Efficiency and Model Vulnerability

Across vision and language models, it is shown that efficiency-oriented training increases susceptibility to adversarial and privacy attacks, and is called for a paradigm shift toward multi-objective training that jointly optimizes for performance, cost, and security.

Yi-Yong Liu, Jun Sakuma, Michael Backes et al. · 0 citations

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