When Safety Routing Breaks: Understanding Alignment Fragility under Benign Fine-Tuning
Even benign fine-tuning can silently break LLM safety alignment — a Fisher-information analysis reveals why, with direct implications for enterprises customising foundation models under EU AI Act rules.
Summary written by editorial AI · Source link below
arXiv:2609.01455v1 Announce Type: new Abstract: Benign fine-tuning severely weakens the safety alignment of large language models (LLMs), so we study why refusal behavior is so fragile. While prior work often attributes this failure to gradient conflict, we propose a fundamentally different Fisher-geometric explanation: safety Fisher is low-rank, and alignment makes the safety geometry flatter while preserving an output-routing pathway. After 100 benign fine-tuning examples, this pathway is sel
Editorial Analysis
Enterprises customising LLMs for internal use risk unknowingly degrading safety guardrails, creating regulatory and reputational exposure under the EU AI Act's post-deployment obligations.
Institute mandatory safety-alignment regression testing after every fine-tuning run, with results documented for AI Act conformity records.
Routine model customisation can silently disable AI safety controls — automated regression testing must become a governance requirement.
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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