Securing Algorithmic Trading Infrastructures via Large Language Model Driven Automated Vulnerability Analysis and Real-Time Protocol Hardening - Calvin. Sterling; Cassie L. Vance (Computational Intelligence Systems, 2026)
The transition of global financial markets toward hyper-automated, low-latency environments has significantly amplified the cyber-physical risks associated with algorithmic trading infrastructures. Traditional cybersecurity methodologies, characterized by periodic manual audits and static rule-based detection, are increasingly inadequate for identifying deep semantic flaws in complex, interconnected trading protocols and execution engines. This paper investigates a systemic framework for enhancing the resilience of these infrastructures through the integration of Large Language Models (LLMs) into continuous vulnerability analysis and real-time protocol hardening pipelines. We propose an architectural paradigm where LLMs facilitate the automated synthesis of adversarial scenarios and the identification of latent logic flaws in Financial Information eXchange (FIX) protocols and proprietary execution logic. The discussion focuses on a system-level analysis, examining the structural trade-offs between computational overhead and execution latency, the socio-technical infrastructure required for automated defensive deployment, and the governance frameworks necessary to maintain market fairness and stability. Furthermore, the paper analyzes the policy implications of deploying autonomous security agents within regulated financial environments, addressing concerns regarding algorithmic robustness, transparency, and the long-term sustainability of AI-driven defensive systems. By synthesizing perspectives from computational finance, artificial intelligence, and large-scale systems engineering, this research provides a comprehensive roadmap for securing the next generation of algorithmic trading infrastructures against increasingly sophisticated adversarial threats.