Predictions by machine learning (ML) and artificial intelligence (AI) models are often received skeptically unless they are paired with intelligible explanations. In the context of just-in-time de- fect prediction, highlighting small portions of a software change (diff )—beyond rule-based lints—where risk may be concentrated has not yet been extensively investigated. In this work, we leverage attention weights from an LLM-based Diff Risk Score (DRS) model to highlight parts of a diff that the model focuses on when pre- dicting risk. We aggregate token-level attention into interpretable code units (lines, hunks, and files), and present the top-𝐾 units to developers as a lightweight form of guidance during code re- view. We evaluate our approach using expert-labeled changes that have caused real outages. Results show that the highlighted snip- pets cover expert-labeled outage-causing change lines 53.85% of the time when highlighting the top-2 hunks, while requiring developers to review 26.28% of the changed lines on average. Because atten- tion is produced during standard model inference, the approach is scalable for large development workflows and can be surfaced in the code review UI with low additional latency.