Saeid Jamshidi

Saeid Jamshidi, Foutse Khomh, Kawser Wazed Nafi et al.

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.

Saeid Jamshidi, Kawser Wazed Nafi, Arghavan Moradi Dakhel et al.

The rapid advancement and adaptability of Large Language Models (LLMs) highlight the need for moral consistency, the capacity to maintain ethically coherent reasoning across varied contexts. Existing alignment frameworks, structured approaches designed to align model behavior with human ethical and social norms, often rely on static datasets and post-hoc evaluations, offering limited insight into how ethical reasoning may evolve across different contexts or temporal scales. This study presents the Moral Consistency Pipeline (MoCoP), a dataset-free, closed-loop framework for continuously evaluating and interpreting the moral stability of LLMs. MoCoP combines three supporting layers: (i) lexical integrity analysis, (ii) semantic risk estimation, and (iii) reasoning-based judgment modeling within a self-sustaining architecture that autonomously generates, evaluates, and refines ethical scenarios without external supervision. Our empirical results on GPT-4-Turbo and DeepSeek suggest that MoCoP effectively captures longitudinal ethical behavior, revealing a strong inverse relationship between ethical and toxicity dimensions (correlation rET = -0.81, p value less than 0.001) and a near-zero association with response latency (correlation rEL approximately equal to 0). These findings demonstrate that moral coherence and linguistic safety tend to emerge as stable and interpretable characteristics of model behavior rather than short-term fluctuations. Furthermore, by reframing ethical evaluation as a dynamic, model-agnostic form of moral introspection, MoCoP offers a reproducible foundation for scalable, continuous auditing and advances the study of computational morality in autonomous AI systems.