The PTRR Framework: A Metacognitive Approach to Measuring and Mitigating Automation Bias in AI-Assisted Vulnerability Research

Note: This is the extended technical report and the comprehensive methodological design of the PTRR Framework. This document provides the full derivation, theoretical anchoring, and preliminary validation of the PTRR (Prompts, Time, Results, Rubric) Framework. The study investigates the "Dual-Edge Effect" of Artificial Intelligence in cybersecurity research, specifically focusing on how practitioner expertise moderates cognitive biases like Automation Bias. Drawing on Dual Process Theory and Cognitive Load Theory, this report introduces three novel metrics: Automation Bias Index (ABI): To quantify uncritical reliance on AI outputs. Cognitive Struggle Index (CSI): To measure productive mental effort and hypothesis testing. Tool Integration Intensity Score (TIIS): To evaluate the synergy between traditional security tools and LLMs. Key Highlights of this Version: Full breakdown of the PTRR scoring rubric and operationalized constructs. Detailed case study results (Section 11) showcasing the transition from VDP-level findings to BBP-qualifying Critical vulnerabilities (including DB exposures and ATOs). Complete comparative analysis of performance trajectory before and after the adoption of the PTRR model. In-depth discussion on preserving human accountability when interacting with opaque AI systems.