This technical deep-dive examines the integration of Large Language Models (LLMs) into the reverse engineering workflow for Endpoint Detection and Response (EDR) systems. The author introduces the "Day Shift" harness, an automated loop utilizing GPT-5.5-Cyber and Binary Ninja to systematically analyze and bypass endpoint security controls. This approach significantly accelerates the identification of user-mode hooks, the decryption of local signature databases, and the reconstruction of proprietary behavioral detection rules.
Using Palo Alto’s Cortex XDR as a case study, the research reveals how LLMs can extract thousands of YARA and DSE rules, as well as local machine learning models used for file scoring. The article concludes by demonstrating a mock C2 framework simulation that allows attackers to validate evasion techniques—such as bypassing registry export protections—against extracted EDR logic. This shift signifies a new reality where endpoint security products face rapid, automated exposure of their local detection capabilities.
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