As large language models (LLMs) are increasingly deployed, harmful generations have become a critical safety concern. Existing safeguards operate at the input, output, or streaming-generation stages, while early-risk methods that rely on surface tokens or output logits often suffer from weak initial signals, and internals-based detectors using dense representations may retain highly entangled and redundant safety-irrelevant information. It therefore remains unclear whether the earliest post-generation hidden states already contain reliable signals about final-response harmfulness. To address this gap, we propose ForeSight, a first-token output-risk forecasting framework that distills weak and redundant early safety signals into compact, layer-aware risk representations. Experiments across five safety benchmarks and two target models demonstrate that ForeSight achieves superior and efficient early-risk forecasting while using only first-token hidden states. The code is available at: https://anonymous.4open.science/r/Foresight-B067