Breaking the Android Bug Report Barrier with AI

Mobile security teams face overwhelming volumes of device logs during incident response, malware investigations, and vulnerability hunting. Traditional manual analysis of these logs can take security experts hours or days per device, creating significant bottlenecks, which are further compounded by the scarcity of specialized mobile security expertise. Bugalyzer is a forensic analysis framework that leverages traditional natural language processing (NLP) approaches and large language models (LLMs) to automatically analyze Android bug reports at scale.

This approach transforms complex, unstructured technical data into clear, comprehensible insights that reveal the complete security narrative of a device. By automatically connecting seemingly disparate events into coherent timelines, the methodology identifies critical security issues in minutes rather than days. Bugalyzer uses intelligent issue prioritization to highlight the most relevant security findings among thousands of log entries, ensuring that meaningful signals aren't lost in the noise. By analyzing logs in their full security context, the system uncovers patterns and anomalies that traditional tools or manual review often miss.

The result democratizes Android forensics for users ranging from non-technical device owners to specialized security researchers—anyone can understand what's happening on a device, regardless of their mobile operating system expertise. This shift from fragmented, manual workflows to machine-driven analysis enables faster, more accurate investigations and strengthens response capabilities.

Importantly, all analysis is performed within a secure, controlled environment using privacy-preserving techniques that maintain data confidentiality. Through real-world case studies, this presentation will demonstrate how Bugalyzer transforms Android security operations by surfacing hidden vulnerabilities and making complex findings universally accessible.