TL;DR Modern attacks increasingly target user-space memory, leveraging dynamic heap allocations to store payloads and evade detection. However, existing forensic tools typically treat dynamic memory as a flat, unstructured region. Our recent work presents a novel forensic methodology for the extraction and structural analysis of Windows NT heap entries, implemented as an open-source Volatility 3 plugin called HeapList. We are presenting this work at the upcoming DFRWS EU 2026 conference, so anyone can reach out and talk to us if interested! Keep reading this briefing or get the full paper here.
Why Heap Memory Forensics is Challenging
While kernel-mode analysis has traditionally been the focus of memory forensics, modern threats are increasingly exploiting user-space memory. Attackers use it as a platform for payloads, evasion tactics, and command and control beacons. In Windows (the most widely deployed desktop platform) the heap’s complexity and legacy architectural features make it especially attractive to attackers seeking persistence and stealth.
Despite this, current forensic tools offer limited visibility, relying on coarse-grained techniques such as memory carving or signature comparison that typically fail against novel or polymorphic threats. Security analysts are often forced to treat the heap as an opaque region, lacking the granularity required to trace attacker behavior, reconstruct heap manipulations, or attribute artifacts to specific heap operations.
Our Solution: A Structural Analysis Approach
To address this gap, we present a forensic approach for the structurally accurate extraction and analysis of Windows NT heap entries from user-space memory dumps. Rather than treating the heap as a contiguous block, our methodology allows analysts to explore its internal structures (specifically the backend layer and the frontend layer, the Low Fragmentation Heap) and extract meaningful, well-defined data.
Key Findings: The HeapList Plugin
Our contributions are embodied in an open-source plugin, HeapList, developed for the Volatility 3 framework. Here is what our implementation achieves:
- Broad Compatibility: The plugin systematically traverses the heap and is compatible with a wide range of Windows versions, from Vista to Windows 11, on both x86 and x64 architectures.
- Accurate Reconstruction: It successfully reconstructs both backend and frontend heap layers, decodes encoded metadata (such as obfuscated user blocks introduced in Windows 8.1), and distinguishes committed from uncommitted memory.
- Validated Reliability: We validated the plugin’s correctness by cross-verifying its output with WinDbg, the official Microsoft Windows debugger. We confirmed that the extracted entries matched those reported by WinDbg perfectly in terms of address, size, and status.
Takeaways for Defenders
Our methodology allows analysts to interpret heap structures beyond simple byte-level searches, opening new avenues for behavior-based memory threat detection.
Security professionals and incident responders can:
- Identify dynamic payloads and inspect heap layouts with structural accuracy.
- Facilitate reverse engineering and advanced threat detection during memory triage.
- Recover forensic-relevant artifacts from real-world user-space applications, as we successfully demonstrated through a case study extracting information from Telegram Desktop.
Funding Acknowledgments
This research was supported in part by grant PID2023-151467OA-I00 (CRAPER), funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU, by grant TED2021-131115A-I00 (MIMFA), funded by MICIU/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR, and the University of Zaragoza, by grant Proyecto Estratégico Ciberseguridad EINA UNIZAR, funded by the Spanish National Cybersecurity Institute (INCIBE) and the European Union NextGenerationEU/PRTR, and by grant Programa de Proyectos Estratégicos de Grupos de Investigación (refs. T21-23R), funded by the University, Industry and Innovation Department of the Aragonese Government.
And that’s all, folks! Stay tuned for our next deep dive into the evolution of memory forensics and threat detection. In the meantime, we encourage you to read the full article, explore the HeapList plugin on GitHub, or reach out to us if you’re interested in collaborating. As mentioned earlier, we will be presenting this work at DFRWS EU 2026, so we’d love to connect and talk there! Thanks for reading, and stay secure!
Declaration of Generative AI Technologies in the Writing Process
During the preparation of this post, the author used Gemini (Gemini 3.1 Pro model) to improve readability and language. After using this tool, the author reviewed and edited the content as necessary and takes full responsibility for the content of this publication.
