Open-weight language models are routinely customized after alignment, but deployers still lack a cheap post-update check that helps decide which benign updates deserve deeper safety evaluation before shipment. We study this problem in a deliberately narrow regime: the base model and downstream task are fixed, and the main decision is the update method. Across Qwen2.5-7B-Instruct and Meta-Llama-3.1-8B-Instruct, translation and summarization tasks, and FullFT, LoRA, QLoRA, and partial unfreeze updates, we find that benign method choice induces large differences in external safety regression. Dense FullFT is often the riskiest option, while partial unfreezing usually gives the best safety and utility frontier. We introduce UpgradeGuard, a fixed-budget post-update audit that combines behavioral canaries, refusal consistency checks, late-layer safety drift, and a specificity control. The right evaluation target is not a universal safety oracle, but within-setting update selection: deciding which candidates to escalate in a fixed model-task panel. In that conditioned regime, the audit provides useful ranking signal, and representation-level components carry much of that signal, while random-text activation drift remains a strong global baseline. In a 256-split gating simulation, the audit catches 62.5% of risky updates while avoiding 51.8% of full external evaluation cost. We therefore present UpgradeGuard as a conservative triage tool for post-update assurance, not as a replacement for external red-teaming or human review.
Ashish Panday (Sun,) studied this question.