Physical-world models (PhyModels) perform grounded perception, reasoning, or decision tasks; physical-world agents (PhyAgents) combine these models with planning or tool use. We introduce RedPhys, an extensible red-teaming platform, instantiated here with vision-language models and agents. Shared interfaces make tasks, test methods, defenses, and victim systems configurable and extensible, producing robustness reports and datasets for future alignment. The platform emphasizes adversarial testing that seeks physically grounded functional failures, while also supporting non-adversarial input perturbations without an attack objective. The benchmark integrates 12 red-teaming methods, 3 model-agnostic defenses, and 13 PhyModels across seven physical domains under a practical black-box threat model. An agentic target-generation pipeline derives a scene-specific, physically grounded target, so targeted methods pursue the same intended error. Our evaluation shows that when attackers control the text prompt or add auxiliary visual instructions, injection exposes substantial instruction-following vulnerabilities; among bounded visual-transfer attacks, multimodal co-optimization transfers best; single-pass generative attacks approach iterative methods without per-image optimization; and neither model scale nor physical-reasoning post-training confers robustness. Code is available at https://github.com/loopleap-lab/RedPhys.

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