Securing Internet of Things (IoT) firmware remains difficult due to proprietary binaries, stripped symbols, heterogeneous architectures, and limited access to executable code. Existing analysis methods, such as static analysis, symbolic execution, and fuzzing, depend on binary visibility and functional emulation, making them unreliable when firmware is encrypted or inaccessible. To address this limitation, we propose a binary-free, architecture-agnostic solution that estimates the likelihood of conceptual zero-day vulnerabilities using only high-level descriptors. The approach integrates a tri-LLM reasoning architecture combining a LLaMA-based configuration interpreter, a DeepSeek-based structural abstraction analyzer, and a GPT-4o semantic fusion model. The solution also incorporates LLM computational signatures, including latency patterns, uncertainty markers, and reasoning depth indicators, as well as an energy-aware symbolic load model, to enhance interpretability and operational feasibility. In addition, we formally derive the mathematical foundations of the reasoning pipeline, establishing monotonicity, divergence, and energy-risk coupling properties that theoretically justify the model's behavior. Simulation-based evaluation reveals that high exposure conditions increase the predicted zero-day likelihood by 20 to 35 percent across models, with GPT-4o demonstrating the strongest cross-layer correlations and the highest sensitivity. Energy and divergence metrics significantly predict elevated risk (p < 0.01), reinforcing the effectiveness of the proposed reasoning framework.

翻译：物联网（IoT）固件的安全防护仍然面临挑战，原因在于其专有二进制文件、剥离的符号表、异构的硬件架构以及对可执行代码的有限访问权限。现有的分析方法，如静态分析、符号执行和模糊测试，都依赖于二进制可见性和功能仿真，当固件被加密或无法访问时，这些方法变得不可靠。为应对这一局限，我们提出了一种无需二进制、架构无关的解决方案，该方案仅使用高层描述符来评估概念性零日漏洞存在的可能性。该方法集成了一种三元LLM推理架构，该架构结合了基于LLaMA的配置解释器、基于DeepSeek的结构抽象分析器以及GPT-4o语义融合模型。该解决方案还整合了LLM计算特征（包括延迟模式、不确定性标记和推理深度指标）以及一个能量感知的符号负载模型，以增强可解释性和操作可行性。此外，我们形式化地推导了该推理流程的数学基础，建立了单调性、发散性以及能量-风险耦合特性，从理论上证明了模型行为的合理性。基于仿真的评估表明，在高暴露条件下，各模型预测的零日漏洞可能性增加了20%至35%，其中GPT-4o表现出最强的跨层相关性和最高的敏感性。能量和发散度指标能显著预测风险升高（p < 0.01），进一步证实了所提推理框架的有效性。