Food safety directly affects human health in daily life, as unsafe preparation or storage can lead to contamination and foodborne illness. PwC's 2025 consumer survey reports that 47% of respondents are comfortable using generative AI for meal planning, highlighting the need for safe food-related assistance. Yet the safety of large language models (LLMs) against malicious food-related requests remains underexplored. We introduce FoodGuardBench, an FDA-grounded benchmark with 2,124 seed malicious questions, 16,992 variants from eight jailbreak strategies, and 1,000 benign questions across five food-safety risk categories. Across twelve LLMs, the mean overall ASR is 25.51%, whereas the average per-model maximum across eight attacks is 60.59%, revealing that testing only one attack can substantially underestimate the full extent of food safety risk. For several models, nearly half or more of unsafe answers are rated as ready-to-use guides for harmful actions. Existing general-purpose guardrails average 96.25% benign utility but only 55.80% detection accuracy against these eight jailbreak attacks. Using FoodGuardBench, we fine-tune FoodGuard-4B, a food-safety-specific guardrail that achieves 97.64% out-of-distribution (OOD) accuracy while maintaining high benign utility. This demonstrates the value of FoodGuardBench for identifying and mitigating food safety risks.

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