Mark Vella

Robert Abela, Mark Vella

Exploits constitute malware in the form of application inputs. They take advantage of security vulnerabilities inside programs in order to yield execution control to attackers. The root cause of successful exploitation lies in emergent functionality introduced when programs are compiled and loaded in memory for execution, called `Weird Machines' (WMs). Essentially WMs are unexpected virtual machines that execute attackers' bytecode, complicating malware analysis whenever the bytecode set is unknown. We take the direction that WM bytecode is best understood at the level of the process memory layout attained by exploit execution. Each step building towards this memory layout comprises an exploit primitive, an exploit's basic building block. This work presents a WM reconstruction algorithm that works by identifying pre-defined exploit primitive-related behaviour during the dynamic analysis of target binaries, associating it with the responsible exploit segment - the WM bytecode. In this manner any analyst familiar with exploit programming will immediately recognise the reconstructed WM bytecode's semantics. This work is a first attempt at studying the feasibility of this method and focuses on web browsers when targeted by JavaScript exploits.

Jennifer Bellizzi, Mark Vella, Christian Colombo et al.

Digital investigations of stealthy attacks on Android devices pose particular challenges to incident responders. Whereas consequential late detection demands accurate and comprehensive forensic timelines to reconstruct all malicious activities, reduced forensic footprints with minimal malware involvement, such as when Living-Off-the-Land (LOtL) tactics are adopted, leave investigators little evidence to work with. Volatile memory forensics can be an effective approach since app execution of any form is always bound to leave a trail of evidence in memory, even if perhaps ephemeral. Just-in-Time Memory Forensics (JIT-MF) is a recently proposed technique that describes a framework to process memory forensics on existing stock Android devices, without compromising their security by requiring them to be rooted. Within this framework, JIT-MF drivers are designed to promptly dump in-memory evidence related to app usage or misuse. In this work, we primarily introduce a conceptualized presentation of JIT-MF drivers. Subsequently, through a series of case studies involving the hijacking of widely-used messaging apps, we show that when the target apps are forensically enhanced with JIT-MF drivers, investigators can generate richer forensic timelines to support their investigation, which are on average 26% closer to ground truth.