# VolWeb: browser based memory forensics over Volatility 3

> VolWeb is a digital forensic memory analysis platform built on the Volatility 3 framework, and its pitch is that an investigator should be able to upload a memory image once and then work through the artifacts in a browser rather than at a command line. It adds a process graph, YARA scanning over the dump, STIX indicator sharing with CTI platforms, and a REST API, and it is careful to say that it complements the Volatility CLI rather than replacing it.

**k1nd0ne/VolWeb** — A centralized and enhanced memory analysis platform

- Repository: https://github.com/k1nd0ne/VolWeb
- Stars: 540 · Forks: 56
- Language: TypeScript
- License: GPL-3.0
- Published: 2026-10-22 · Updated: 2026-10-22 · Language: en
- Canonical page: https://hysenlabs.com/projects/k1nd0ne-volweb

## A web platform on top of Volatility, not a replacement for it

VolWeb is a digital forensic memory analysis platform that uses the power of the Volatility 3 framework, dedicated to aiding in investigations and incident responses.

The stated goal is to enhance the efficiency of memory collection and forensic analysis by providing a centralized, visual and enhanced web application for incident responders and digital forensics investigators.

The workflow it imagines is straightforward. Once an investigator obtains a memory image from a Linux or Windows system, with Mac coming soon, the evidence can be uploaded to VolWeb, which triggers automatic processing and extraction of artifacts using the Volatility 3 framework.

And then there is the disclaimer, which is the sentence to read before anything else. A quick disclaimer: VolWeb is meant to be used in conjunction with the Volatility 3 framework CLI. It offers a different way to review and investigate some of the results and will not do all of the deep dive analysis job for you.

That is an honest positioning, and it is the difference between a useful tool and one that oversells. Volatility 3 is a large framework with hundreds of plugins and a deep CLI. No web interface replaces that. What the web interface replaces is the part where you run twenty plugins one at a time and read their output in a terminal, and the part where the results live in your shell history instead of in a case file.

The implementation is a Django backend with a TypeScript frontend, and the licence is GPL-3.0. The releases are v3.13.3 in January 2025, v3.15.0 in July 2025 and v3.16.0 on 2026-03-20, with the last push to the repository on 2026-09-29.

## Hybrid storage, and why the upload model matters

The storage section of the README is short, and it explains one of the decisions that shape everything else.

Your evidences are uploaded to the VolWeb platform and use filesystem analysis by default, for the best performances. But you can also bind evidences from a cloud storage solution, AWS or MinIO, and bind them to your cases in order to perform the analysis directly on the cloud.

The reasoning is about size. A memory image from a busy machine is tens of gigabytes, and the Volatility plugins are not cheap to run over one. If the image sits on a local disk, the analysis is local and fast. If the image is in object storage, the platform can work on it there instead of requiring a copy to be dragged through the analyst's connection first.

The third option is the one that matters for a distributed team. By utilizing hybrid storage technologies, VolWeb also enables incident responders to directly upload memory images into the platform from various locations using dedicated scripts interfaced with the platform and maintained by the community.

So there are three paths for an image to reach the analysis: upload it directly, bind it to a case from object storage, or push it from a machine at the incident site with one of the community scripts. The last is the one that gets mentioned in practice, because an acquisition is often done on the machine that is still running, and copying the image off it before anything else happens is the slow part.

That is also the part with the sharpest operational edges. Getting an image off a machine at the incident site and into a case is a custody question, and the way the platform handles the file is a decision an organisation has to make before it deploys the tool rather than after.

## Investigate for the overview, Explore for the graph

The two central features divide the work between a list view and a graph view, and the README is explicit about why.

The investigate feature provides an overview of the available artifacts that were retrieved by the custom Volatility 3 engine in the backend. If available, you can visualize the process tree and get basic information about each process, dump them, and you also get an enhanced view of all of the plugins' results by categories.

That is the orientation layer: what did the plugins find, grouped so that the output of twenty plugins is browsable rather than a wall of text.

The explore feature comes with VolWeb 3.0, for Windows investigations with Linux coming soon, and it lets a memory forensics expert investigate potential suspicious processes in a graph view, allowing another way to look at the data, and to correlate the Volatility 3 plugins to get more context.

The section header under which it sits is a quotation: defenders think in lists, attackers think in graphs, and as long as this is true, attackers win.

That framing is the argument for the feature. A process list tells you what ran. A graph tells you what talked to what, and in a memory image the relationships are where the answer usually is. A process that looks alone in a list may be the child of something that looks equally innocent, and the graph makes the parent visible.

The limitation is stated too: the graph view is for Windows for now, with Linux on the way. For a Linux memory investigation, the graph is not available, which is a real gap given that Linux is one of the two platforms the platform currently handles.

## STIX indicators and YARA rules inside the same case

Two features move VolWeb beyond the analysis itself, and they are the reason a team would run it centrally rather than locally.

The first is indicator handling. When the expert finds malicious activities, VolWeb gives you the possibility to create STIX V2 indicators directly from the interface and centralize them in your case. Once your case is closed, you can generate your STIX bundle and share your indicators with your community using CTI platforms like MISP or OpenCTI.

The value is in not retyping. An indicator found in a memory image is an address, a domain or a file hash, and the process of writing it into a CTI platform by hand is where the record of an investigation gets lost. Generating the bundle from the case means the indicators and the evidence they came from stay connected.

The second is YARA integration. VolWeb integrates YARA scanning to detect patterns and malware signatures within memory dumps. You can create rules manually, import them from .yar files or from GitHub repositories, or paste existing rules directly into the editor. Rules can be organized into rulesets for better management and grouped scanning.

The scanning interface allows flexible selection of rulesets, individual rules or custom combinations. Results appear in a searchable grid showing memory offsets and matched content, and VolWeb automatically maintains your most recent scan, so you can get back to the last result without re-running the search.

That last point matters more than it sounds. A YARA scan over a memory image is not fast, and if you are iterating on a rule you do not want to re-run the scan every time you change it. Keeping the last scan and filtering it is what makes rule development practical.

## The stack: Django, Postgres, Redis, Daphne and nginx

The compose file shows the architecture, and it is a conventional Python application deployment.

There is a Postgres service, pinned to postgres:14.1, with the user, password and database taken from the environment and a named volume for the data directory. There is a Redis service running with appendonly yes and its own named volume, and the environment calls it the broker, so Redis is the task queue rather than a cache.

The backend service is built from the ./backend directory and runs Daphne as the ASGI server:

```yaml
    command: daphne -b 0.0.0.0 -p 8000 backend.asgi:application
```

Daphne rather than a WSGI server, which is what you need for the asynchronous view the platform uses, and it depends on both the database and the broker services. A separate workers service is built from the same context, which is the classic split between the web process and the background workers, and the background workers are where the Volatility runs happen.

The environment block for the backend carries the database settings, the Django secret key, the trusted origins for CSRF and a flag to run migrations on start. There are named volumes for the static files and for the media, which is where uploaded evidence ends up.

The example environment file lists the same set of variables, with the Django secret as change_me_or_die and the CSRF trusted origins pointing at the frontend on port 3000, plus the compose build flags. There are separate compose files for development and production, an nginx/ directory for the reverse proxy, and a frontend/ directory holding the TypeScript client.

The administrative side is Django's own, since VolWeb uses Django in the backend and you manage users and the database directly from the admin panel. There is also a REST API for analysts, with Swagger on the platform for the full documentation, and a separate community repository of scripts, forensicxlab/VolWeb-Scripts.

## Where VolWeb is the wrong tool

There are clear cases where a centralised platform is the wrong answer, and they come from what the platform centralises.

The first is evidence handling. A memory image is the most sensitive artefact an incident responder holds, and uploading it to a platform means the evidence leaves the machine it came from and lands somewhere with its own access control, its own storage and its own retention. For a case that has to survive legal scrutiny, or for an organisation whose policy says evidence never leaves the acquisition host, that is a decision that ends the discussion of the tool.

The second is the deep analysis the README already warns about. VolWeb does not do the deep dive analysis job, and it is meant to be used alongside the Volatility 3 CLI. If your work is writing a new Volatility plugin, or investigating an unusual artefact with plugin options the interface does not expose, the CLI is where you will end up, and the platform adds a deployment to maintain.

The third is coverage. The graph view is Windows only for now, with Linux to follow, so on a Linux investigation the graph is not available. And the platform supports Linux and Windows images with Mac coming soon.

The alternative approach is the local one: run Volatility 3 on the image where it lies, keep the output, and move only the findings. The difference in approach is that the local route leaves the evidence in place and gives you full control of every plugin invocation, at the cost of no shared case system, no YARA iteration across the team and no STIX generation from the results.

For a team with a case workflow and a CTI pipeline, VolWeb earns its keep. For a single analyst on a single image, the CLI is the shorter path.

## Conclusion

VolWeb suits a team that handles memory images repeatedly and wants the artifacts in a shared case system rather than in a folder of command line output, and that needs to turn findings into STIX indicators without retyping them. It does not suit an analyst doing deep work on a single image, because the README is explicit that it does not do the deep dive analysis job, and it does not suit a setup where evidence cannot leave the analyst's machine. Check three things before you deploy it: that the Volatility 3 command line is what you will still use for the hard questions, since VolWeb is explicitly a different way to review the results, which platforms the features you need cover, since the graph view arrived with 3.0 for Windows investigations with Linux to follow, and how your evidence images are stored and uploaded, because that is a decision about custody and bandwidth rather than about the software. The last push to the repository was on 2026-09-29 and the newest release is v3.16.0.

## FAQ

### What is VolWeb?

It is a digital forensic memory analysis platform built on the Volatility 3 framework, providing a centralized, visual web application for incident responders and investigators. You upload a memory image from a Linux or Windows system and the platform processes it and extracts artifacts automatically.

### Does VolWeb replace the Volatility 3 command line?

No. The README states that VolWeb is meant to be used in conjunction with the Volatility 3 framework CLI, offering a different way to review and investigate some of the results, and that it will not do all of the deep dive analysis job.

### Where are memory images stored in VolWeb?

Uploaded evidence uses filesystem analysis by default for the best performance, and you can alternatively bind evidence from cloud storage such as AWS or MinIO to a case so the analysis runs on the cloud. Dedicated community-maintained scripts also allow uploading images directly from other locations.

### What can VolWeb do with indicators and YARA rules?

You can create STIX V2 indicators directly in the interface, centralize them in the case and generate a STIX bundle to share with CTI platforms such as MISP or OpenCTI. For YARA you can write rules, import them from .yar files or GitHub repositories, group them into rulesets, and scan with results shown in a searchable grid with memory offsets and matched content.

### How is VolWeb deployed?

With Docker Compose. The stack is a Postgres service, a Redis service used as the broker, a Django backend served by Daphne on port 8000, a separate workers service, and nginx in front, with the frontend served separately on port 3000 and the compose build flags set in the environment file.

## Sources

- [Issues](https://github.com/k1nd0ne/VolWeb/issues)
- [k1nd0ne/VolWeb on GitHub](https://github.com/k1nd0ne/VolWeb)
- [License: GPL-3.0](https://github.com/k1nd0ne/VolWeb/blob/main/LICENSE)
- [README](https://github.com/k1nd0ne/VolWeb/blob/main/README.md)
- [Releases](https://github.com/k1nd0ne/VolWeb/releases)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/k1nd0ne-volweb