Volatility Framework
What is Volatility Framework?
Volatility FrameworkOpen-source memory forensics framework originally created by Aaron Walters and the Volatility Foundation for extracting digital artefacts from volatile memory (RAM) images.
The Volatility Framework is the de-facto open-source toolkit for memory forensics. Originally released in 2007 by Aaron Walters and others as the Volatility Project and now stewarded by the Volatility Foundation, it parses raw memory captures (RAM dumps, hibernation files, page files, VMware/VirtualBox snapshots) from Windows, Linux and macOS. Investigators use it to enumerate processes, network connections, loaded DLLs, registry hives, kernel modules and rootkit indicators long after the system has been shut down. Volatility 2 is written in Python 2 and was the long-time standard; Volatility 3, released in 2020, is a rewrite in Python 3 with automated symbol handling. It is a core component of IR playbooks and is heavily used in malware triage and APT investigations.
● Examples
- 01
Running 'vol.py windows.pslist' against an acquired memory image to list running processes and identify hollowed binaries.
- 02
Using 'windows.malfind' to extract injected shellcode regions during a ransomware investigation.
● Frequently asked questions
What is Volatility Framework?
Open-source memory forensics framework originally created by Aaron Walters and the Volatility Foundation for extracting digital artefacts from volatile memory (RAM) images. It belongs to the Forensics & IR category of cybersecurity.
What does Volatility Framework mean?
Open-source memory forensics framework originally created by Aaron Walters and the Volatility Foundation for extracting digital artefacts from volatile memory (RAM) images.
How does Volatility Framework work?
The Volatility Framework is the de-facto open-source toolkit for memory forensics. Originally released in 2007 by Aaron Walters and others as the Volatility Project and now stewarded by the Volatility Foundation, it parses raw memory captures (RAM dumps, hibernation files, page files, VMware/VirtualBox snapshots) from Windows, Linux and macOS. Investigators use it to enumerate processes, network connections, loaded DLLs, registry hives, kernel modules and rootkit indicators long after the system has been shut down. Volatility 2 is written in Python 2 and was the long-time standard; Volatility 3, released in 2020, is a rewrite in Python 3 with automated symbol handling. It is a core component of IR playbooks and is heavily used in malware triage and APT investigations.
How do you defend against Volatility Framework?
Defences for Volatility Framework typically combine technical controls and operational practices, as detailed in the full definition above.
What are other names for Volatility Framework?
Common alternative names include: Volatility, vol.py, Volatility 3.
● Related terms
- forensics-ir№ 668
Memory Forensics
The discipline of acquiring and analysing a system's volatile RAM to reveal running processes, network connections, injected code, and in-memory artefacts.
- forensics-ir№ 162
Chain of Custody
The chronological, documented trail showing every person, location, and action affecting a piece of evidence from seizure through final disposition.
- forensics-ir№ 426
Forensic Imaging
Creating a bit-for-bit copy of a storage medium, verified by cryptographic hashes, for use in forensic analysis and as legally admissible evidence.
- forensics-ir№ 650
Malware Analysis
The structured study of a malicious sample to understand its functionality, origin, indicators of compromise, and impact on affected systems.
- forensics-ir№ 524
Incident Response
The organised process of preparing for, detecting, analysing, containing, eradicating, and recovering from cyber security incidents, then capturing lessons learned.
- forensics-ir№ 1156
Timeline Analysis
A forensic technique that reconstructs the chronological sequence of events on a system by correlating timestamps from files, logs, and other artifacts.