攻击者通过修改聊天模板，在推理时植入后门，无需改模型权重或控制部署环境。

Open-weight language models are increasingly used in production settings, raising new security challenges. One prominent threat is backdoor attacks, in which adversaries embed hidden behaviors that activate under specific conditions. Previous work has assumed that adversaries have access to training pipelines or deployment infrastructure. We propose a novel attack surface requiring neither: the "chat template". Chat templates are executable programs invoked at every inference call, often implemented in Jinja2, that occupy a privileged position between user input and model processing. We show that an adversary who distributes a model with a maliciously modified template can implant an inference-time backdoor without modifying model weights, poisoning training data, or controlling runtime infrastructure. We evaluate this attack across three deployment tiers. At the LLM level, triggered backdoors reduce factual accuracy from 90% to 15% on average and induce attacker-controlled URL emission with success rates exceeding 80%, while benign inputs show no measurable degradation; these results hold across eighteen models. At the agent level, template backdoors hijack tool-use across two benchmarks spanning 3,868 episodes, bypassing every tested injection defense offered by the benchmarks while remaining fully dormant absent the trigger. At the multi-agent system level, we demonstrate how a single poisoned artifact compromises a real-world agentic deployment and propagates supply-chain code poisoning downstream. The poisoned artifacts evade all security scans on the largest open model distribution platform; and because the payload is rendered by the template before user input is processed, it is architecturally unreachable by input-level defenses such as prompt injection guardrails. These results establish chat templates as a reliable and undefended attack in the open-weight AI supply chain.