Information stealer malware has become one of the most prolific and damaging threats in today’s cybercrime landscape, siphoning off everything from browser-stored credentials to session tokens and other systems secrets. In 2024 alone, we witnessed more than 30 million stealer logs traded on underground markets. Yet buried within these logs is an underexplored goldmine: screenshots captured at the precise moment of infection. Think of it as a thief taking a selfie mid-heist, unexpected but convenient for us, right? Surprisingly, these crime scene snapshots have been largely overlooked until now.

Leveraging infostealer infection screenshots and Large Language Models (LLMs), we propose a new approach to identify infection vectors, extract indicators of compromise (IoCs) and track infostealer campaigns at scale. Our approach found several hundred potential IoCs in the form of URLs leading to the download of the malware-laden payload. By applying this method to ”fresh” stealer logs, we can detect and mitigate infection vectors almost instantaneously, reducing further infections. Our analysis uncovered distribution strategies, lure themes and social engineering techniques used by threat actors in successful infection campaigns. We will break down three distinct campaigns to illustrate the tactics they use to deliver malware and deceive victims: cracked versions of popular software, ads pointing to popular software and free AI image generators.

This presentation, with its live demonstration, shows how LLMs can be harnessed to extract IoCs at scale while addressing the challenges and costs of implementation. Attendees will walk away with a deeper understanding of the modern infostealer ecosystem and will want to apply LLM to other illicit artifacts to extract actionable intelligence.