Volatility 3 Tutorial: Features, Use Cases, How It Works

TL;DR, What is Volatility 3?

Volatility 3 is a memory forensics framework designed to pull digital artifacts from volatile memory (RAM) samples.

When attackers use memory-resident techniques like fileless malware, they leave few traces on storage, making disk-based analysis less effective. Volatility 3 helps solve this problem by analyzing a system's runtime state from a memory capture. The framework gives you visibility into running processes, active network connections, loaded drivers, and user activity that exists only in RAM. Using Volatility 3 helps you uncover active threats and understand the full timeline of an attack during incident response.

Developed by the Volatility Foundation, the framework is a common tool for digital forensics and incident response (DFIR) professionals.

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At-A-Glance

• GitHub: https://github.com/volatilityfoundation/volatility3

• License: Volatility Software License

• Primary Language: Python

• Stars: 3.5k ⭐

• Last Release: v2.11.0 (January 2025)

• Topics/Tags: volatility, memory, forensics, framework, windows, linux, volshell

Common use cases

1. Malware Analysis and APT Detection: You can use Volatility 3 to analyze memory dumps from systems infected with malware, including those compromised by advanced persistent threats (APTs). You can also dump malicious code injected into legitimate processes. The framework's plugins help you reconstruct malware behavior, identify loaded modules, trace process execution, and recover configuration data or encryption keys from memory. Having this level of detail helps with understanding the capabilities of a particular threat, developing indicators of compromise (IOCs), and attributing an attack.

2. Incident Response and Threat Hunting: During an active security incident, you can use Volatility 3 to quickly assess a compromised system and understand the attack's scope. The framework helps detect evasion techniques that bypass typical security controls, like fileless malware, rootkits that hide processes, or in-memory PowerShell attacks. With Volatility 3, you can create a timeline of attacker activity, trace lateral movement by analyzing network artifacts, and identify malicious processes. Threat hunting teams can also use the tool to search for subtle signs of compromise across systems by looking for memory anomalies. Gaining this visibility helps you contain a breach, remove the threat, and recover the system.

3. Digital Forensics and Evidence Collection: For digital forensics investigations, memory analysis with Volatility 3 helps recover evidence that disappears when a system powers down. Investigators use the framework to extract material from a memory dump, including recently used applications, open documents, or clipboard contents. The tool can also recover sensitive data like passwords or cryptographic keys from memory, which you can then use to decrypt files or access other systems. A memory snapshot provides a record of user and system activity at a specific moment, which helps establish intent and verify actions.

4. Security Research and Exploit Analysis: Security researchers can use Volatility 3 to study malware behavior and analyze exploit techniques in a controlled environment. After running a malware sample in a virtual machine, you can capture a memory dump to examine changes made to the system. Analyzing the dump provides a detailed look at how an exploit payload operates, how a rootkit modifies kernel structures, or how ransomware manages encryption keys in memory. Gaining this level of detail is useful for understanding new attack vectors and developing detection signatures for security tools.

5. Compromise Assessment and Scope Analysis: After detecting a security breach, you need to determine the full scope of the compromise. Volatility 3 helps with this process. By analyzing memory images from different systems like domain controllers or workstations, you can hunt for common indicators of compromise across the environment. Doing so helps identify other compromised systems, even those not generating alerts. Comparing memory snapshots from multiple machines can reveal patterns of lateral movement, identify staging servers, and confirm which assets were impacted.

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How does Volatility 3 work?

Volatility 3 processes a memory image using a layered architecture. First, the tool ingests the dump and automatically detects the OS with its “Automagic” system, so you don't need manual profiles. The Automagic system configures the right translation layers and symbol tables. Plugins then use this setup to navigate the memory space, parse kernel data structures, and extract forensic artifacts for you to review.

• Translation Layers: Translation layers manage the mapping between virtual memory addresses used by processes and their physical locations in the memory dump.

• Dynamic Symbol Tables: Instead of using static profiles, Volatility 3 dynamically fetches OS structure definitions. For Windows, the tool connects to Microsoft's symbol servers; for Linux/macOS, it uses Intermediate Symbol Format (ISF) files to ensure accuracy.

• Automagic Configuration & Plugins: The ”Automagic” system simplifies analysis by identifying the OS and setting up the correct layers and symbol tables. The plugin interface then uses this context to perform specific tasks, like listing processes or finding rootkits.

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Core Capabilities

1. Automatic OS Detection and Symbol Loading: Volatility 3 simplifies memory analysis by automating operating system identification. The tool scans a memory image to find the exact OS version and build number, so you don't have to specify a profile manually. For Windows images, Volatility 3 can fetch the needed debugging symbols from Microsoft's servers if you have an internet connection. Symbols are required to correctly interpret kernel data structures, which helps plugins accurately parse information about processes, drivers, and network connections. For offline analysis or for Linux and macOS systems, the framework uses pre-compiled symbol packs.

2. Advanced Filtering and Integrated Output Processing: Volatility 3 has a built-in data filtering engine that helps you target and extract information without using external command-line tools. The `--filters` flag allows you to apply multi-layered filtering on any plugin's output. You can filter specific columns using string matches, regular expressions, or numerical comparisons for values like process IDs. The framework also has a flexible output system that supports formats like plain text, structured JSON, and CSV. JSON output is useful for security operations because you can integrate it with SIEM systems, data analysis platforms, or custom scripts.

3. Comprehensive Cross-Platform Plugin Ecosystem: Volatility 3 is built around a plugin architecture that provides deep analysis across Windows, Linux, and macOS. This cross-platform support allows you to apply a consistent set of tools regardless of the target system. The framework comes with over one hundred built-in plugins that cover a full range of forensic tasks, such as enumerating processes, listing network connections, inspecting kernel modules, and dumping registry hives. The ecosystem is collaborative, letting plugins use the output of other plugins to build complex workflows for uncovering attack patterns. This modular design means you can start with a broad survey using plugins like `pslist`, then dive deeper into suspicious processes with `memmap` or `malfind`, all within the same framework.

4. Timeline Correlation and Structured Data Output: Volatility 3 has a timeline feature that combines time-stamped artifacts from across the system into a single, chronological view. Almost every plugin that extracts data with a timestamp feeds its findings into this timeline. By running the `timeliner` plugin, you can generate a sorted list of all system activity to correlate events and build a narrative of an intrusion.The framework's TreeGrid output system also presents data with parent-child relationships, like process trees, in a structured, machine-readable format. Structured output is useful for programmatic analysis and for sending complex data to other analytical platforms.

5. Extensible and Modular Framework Architecture: Volatility 3 has an extensible and modular architecture designed for easier maintenance and customization. The framework has a clean API that simplifies custom plugin development, so you can create specialized tools for your specific needs. Beyond plugins, you can also extend core components. Developers can add new translation layers to support memory images from different sources, create parsers for new symbol table formats, or design custom output renderers to integrate with reporting systems. 

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Limitations

1. Steep Learning Curve: Volatility 3 has a steep learning curve, especially if you're new to memory forensics. The large number of plugins can be hard to manage. To use the tool well, you need a solid understanding of operating system internals like kernel architecture and memory management. Learning advanced features like the filtering engine also takes time and practice.

2. Dependency on High-Quality Memory Acquisitions: The quality of your analysis depends entirely on the quality of the memory image. If a memory dump is corrupted or incomplete, Volatility 3's results will be unreliable. For example, a “smeared” image, where memory changes during capture, can create inconsistent data that plugins can't parse. The memory capture process happens outside of Volatility and has its own challenges, like choosing the right tool and running it with the right privileges.

3. High Resource Consumption on Large Dumps: Modern systems often have large amounts of RAM (32GB, 64GB, or more), and analyzing these large memory dumps can use a lot of resources. Running plugins that scan all of physical memory, like `malfind` or `yarascan`, can take a long time, sometimes hours.

4. Vulnerability to Advanced Anti-Forensics: Since memory forensics is now common, attackers develop anti-forensics techniques to avoid detection. Some malware tries to corrupt the kernel data structures that forensic tools depend on, like unlinking a process to hide it. Other malware might encrypt its in-memory components. More advanced threats might even try to detect memory capture tools and shut down. Volatility 3 relies on parsing standard OS structures, so it must be constantly updated to counter new evasion tactics. An older version might not detect modern threats.

5. Primarily Post-Mortem Analysis: Volatility 3 is designed for post-mortem analysis, meaning it analyzes a static snapshot of system memory taken at a specific time. A memory snapshot is useful for a deep investigation, but the analysis is not real-time. Any malicious activity that happens between memory captures will be missed. The framework’s snapshot-based approach lacks the continuous visibility and real-time alerts of an endpoint detection and response (EDR) agent.

Getting Started:

Step 1: Make sure you have Python 3.8.0 or later installed on your system.

Step 2: Install Volatility 3 using pip:

pip install volatility3

Step 3: After installation, check that the tool is installed and see help options by running:

vol -h

Step 4: To analyze your first memory image, run a command like:

vol -f /path/to/sample.vmem windows.info

Step 5: For more features or development, you can clone the source code and install it in editable mode:

git clone https://github.com/volatilityfoundation/volatility3.git

cd volatility3/\npython3 -m venv venv && . venv/bin/activate

pip install -e ".[dev]"

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