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

Detecting harmful memes is critical for maintaining safe online communities. However, harmful intent is often implicit, arising from visual–textual incongruity and cultural stereotypes, which challenges existing multimodal detectors. We propose SyRHM, a framework that decomposes harmful meme detection into meaning-grounded retrieval and symbolic-language-enhanced multi-stage reasoning. SyRHM retrieves semantically related memes by parsing multimodal content into textual elements and description, providing grounded context beyond surface-level similarity. Building on the retrieved context, SyRHM uses a translator stage to convert multimodal inputs into symbolic intermediate representations, and then performs multi-stage reasoning via planner and solver stages, enabling expressive and interpretable analysis of harmful intent. Experiments on FHM, HarM, and MultiOff show that SyRHM consistently outperforms multimodal and reasoning-based baselines, while providing reasoning traces for harmful content. The code is available at: https://anonymous.4open.science/r/SyRHM-CC46