MalChela GUI: Visualizing Malware Analysis with Ease

A New Face for MalChela

MalChela, a Rust based toolkit for YARA and malware analysis, was released as a set of command-line apps just a few months ago. Now, it steps into a new realm with the introduction of a graphical user interface (GUI), bringing its powerful features to a broader audience.

The transition from command-line to GUI isn’t just a cosmetic upgrade; it’s a strategic move to make malware analysis more accessible. The GUI version retains all the robust functionalities of its predecessor while offering an intuitive interface that caters to both seasoned analysts and newcomers.

Key Features at a Glance

File Analyzer Module

The updated fileanalyzer module provides a comprehensive overview of suspect files. By simply providing the path to a file, users receive:

  • SHA-256 Hash,
  • Entropy analysis,
  • Regular expression detection for packing,
  • PE header information (for PE files),
  • File metadata,
  • Suspicious API calls,
  • YARA rule matches (against your local library)
  • and VirusTotal hash matches.

This module serves as an excellent first step in static analysis, offering a detailed snapshot of the file’s characteristics.

mStrings Integration

One of MalChela’s standout features, mstrings, is seamlessly integrated into the GUI. This function extracts strings from files and applies Sigma rules defined in YAML to evaluate threats, aligning results with the MITRE ATT&CK framework. It’s a powerful tool for identifying indicators of compromise (IOCs) and understanding malware behavior. Users of MalChela can easily customize their own detection rules in YAML. About 15 new detection rules were added in this release.

Other Tools in the MalChela Suite

Beyond mstrings and fileanalyzer, the MalChela suite includes a range of focused utilities designed to support malware triage and forensic workflows.

malhash lets you quickly query both Virus Total and Malware Bazaar via API calls. The GUI includes an API configuration utility. The CLI will walk you through it.

mismatchminer walks a directory or volume looking for executables disguised as other file types.

mzmd5 and xmzmd5 generate MD5 hash sets—useful for building known-good or known-bad reference hash sets for matching against large corpora.

mzcount provides a quick census of file types in a directory.

strings_to_yara lets you transform suspicious strings into functional YARA rules.

extract_samples recursively unpacks directories of password protected archives often used in malware distribution.

nsrlquery lets you quickly check a hash against the CIRCL hash database.

MalChela’s modular approach with support for custom rule generation, gives analysts what they need without unnecessary overhead. Each tool is designed to run independently but plays well within the broader GUI ecosystem.

Output for any included tool can be saved or skipped at runtime with a simple toggle in the GUI. Structured tools support exporting results in plain text and JSON formats, while YARA rule creation and notes can also be saved in YAML or Markdown.

The Scratchpad:

Notes, YARA Strings, and Analyst Flow

Analysis often involves scattered notes, pasted IOCs, potential YARA strings, and fleeting insights. The MalChela GUI brings structure to that chaos with a built-in scratchpad — a minimalist text editor embedded directly in the interface.

The scratchpad supports live note-taking during tool runs, temporary storage of strings for strings_to_yara, manual IOC tracking and observation logging, and a copy/paste buffer for hashes, commands, or decoded payloads.

Auto-Save & Formats

By default, the scratchpad auto-saves your content every 10 seconds to prevent loss during intense analysis sessions. A simple dropdown lets you export your notes in .txt, .yaml, or .md formats—ideal for integrating with reports or detection development pipelines.

VS Code Integration

For those who prefer a full-featured editor, the “Open in VS Code” button sends your current note directly to a VS Code window, assuming it’s installed and on your system path. This bridges the gap between in-tool triage and deeper rule crafting or documentation workflows.

Bonus Tip: strings_to_yara Compatibility

Lines in the scratchpad that begin with hash: are ignored by the strings_to_yara tool. This allows analysts to keep reference hashes or tagging metadata in the same document without interfering with rule generation. You can import your scratchpad into strings_to_yara in one click.

This feature isn’t just a notepad—it’s a tactical workspace. Whether you’re building detections, jotting notes mid-investigation, or scripting quick ideas, the scratchpad keeps yourn workflow grounded and your thoughts collected.

Last but not least, a crab with karma

Update Checker

The GUI includes a function to automatically check the GitHub repository for updates, encouraging users to pull the latest changes and ensure they have the most current tools at their disposal. 🦀

Enhancing the Analysis Workflow

The GUI version of MalChela doesn’t just replicate CLI functionalities; it enhances the overall workflow. The visual interface allows for easier navigation between modules, quick access to results, and a more streamlined analysis process.

For instance, after walking a directory with mismatchminer you find a suspect file. You run fileanalyzer and can directly proceed to mstrings if the initial findings warrant deeper investigation. From there VirusTotal and Malware Bazaar information can be queried with malhash. Drop your notes in the scratchpad as you go and then use strings_to_yara to draft a YARA rule without worrying about a single tab or indent.

But wait, there’s more

Integrating Third-Party Tools with YAML

The MalChela GUI supports third-party tool integration using a simple tools.yaml configuration file. This makes MalChela not just a toolkit, but a flexible launchpad for your broader forensic workflow.

Each entry in tools.yaml defines the command, input type, and category for a tool. MalChela parses this file at startup, populating the GUI dynamically. Analysts can add their own utilities—whether it’s a custom script, a Python tool, or an external binary—without needing to recompile the application.

- name: Extract Samples
  command: ["extract_samples"]
  input_type: folder
  category: "Utilities"
- name: File Analyzer
  command: ["fileanalyzer"]
  input_type: file
  category: "File Analysis"
# Example 3rd party integration:
# Below is a disabled example for capa
# Uncomment to enable if capa is in your PATH
#
# - name: capa
#   command: "capa"
#   input_type: "file"
#   category: "External"
#   optional_args: []

Once added, the tool appears in the GUI under its specified category, ready to be launched with a single click. Tools must be available in the system PATH, and input types must be one of: file, folder, or hash.

This keeps the interface clean, configurable, and analyst-driven—allowing teams to tailor MalChela to fit their exact needs without touching a single line of Rust.

MalChela is built with the belief that collaboration fuels innovation. I welcome contributions from the broader security and forensics community—whether it’s crafting new detection logic, enhancing YARA rule coverage, refining the GUI, or integrating additional tools via YAML. If you have an idea, patch, or workflow improvement, I’d love to see it. Together, we can make MalChela a more powerful and adaptable tool for every analyst.

Getting Started

👉 MalChela on GitHub

To explore the GUI version of MalChela, visit the official GitHub repository:

Installation instructions and a user guide are available to help you get started. Whether you’re a seasoned analyst or just beginning your journey in malware analysis, the GUI version of MalChela offers a user-friendly yet powerful tool to aid your investigations.

MalChela GUI runs on Mac and Linux (with extra love for Mac users). For use on Windows the entire MalChela CLI toolset is supported under WSL 2.

mStrings: A Practical Approach to Malware String Analysis

String analysis is a cornerstone of malware investigation, revealing embedded commands, URLs, and other artifacts that can expose a threat’s intent. mStrings, a Rust-based tool, simplifies this process by scanning files, extracting meaningful strings, and structuring results for efficient analysis.

At its core, mStrings is more than a simple string extraction tool. It integrates regex-based detection rules to identify key indicators, offering a refined approach to analyzing malware artifacts. In addition to console output it also presents data in a structured JSON format, allowing for seamless integration into other security workflows.

screenshot from mStrings

In addition to specialized string searching, mStrings detections associate results with MITRE ATT&CK. When malware indicators map to known MITRE ATT&CK techniques, analysts can quickly understand the intent and behavior of a threat. Instead of just seeing a suspicious string, they can recognize that it corresponds to credential dumping, command-and-control, or privilege escalation, enabling faster triage and response.

Optimized for Practical Investigation

Security professionals often need to cross-reference findings in a hex editor. mStrings accounts for this by capturing detailed string locations in hex, allowing for immediate context when reviewing suspicious files. This level of granularity is particularly valuable when analyzing packed or obfuscated malware, where offsets can provide crucial insights.

mStrings showing hex location for identified string

After the scan, reviewing the complete strings dump is just as easy with an option to open the results directly in VS Code.

mStrings prompt to review saved strings

Technology That Powers It

Built in Rust, mStrings leverages its robust ecosystem to enhance performance and reliability. Sigma-based detection rules allow for flexible and easily modifiable patterns, giving analysts control over what indicators to track. The tool’s structured approach ensures that results are not just extracted but meaningfully categorized for deeper analysis.

A Tool That Grows with You

mStrings is extensible, enabling you to customize detections. Not satisfied with the existing detection rules? You can easily write your own in Sigma. Future improvements will refine regex patterns, enhance Windows compatibility, and introduce new features to improve investigative workflows. Designed with usability in mind, mStrings serves as a practical companion for analysts who need clear, structured, and insightful data extraction.

MStrings is one of many malware analysis utilities included in MalChela. Download from Github and let me know what you think. If you’ve already installed Malchela, git pull will download the latest updates.

https://github.com/dwmetz/MalChela

Try this out for a workflow. Use Hash It (3) and give it the file path for a malware file. Use the hash from Hash It and check it against VirusTotal an Malware Bazaar with the Malware Hash Lookup (10). Then jump into mStrings (4), give it the same file path again, and start pulling out the interesting strings. Once you have what you think is a good number of indicators, run Strings to YARA (9) and generate a fully formatted YARA rule for use in any of your security tools.

MalChela – A YARA and Malware Analysis Toolkit written in Rust

Saturday was for Python. Sunday was for Rust.

After my success with the Python + YARA + Hashing, I decided to take things to the next level. Over the past few years I’ve created a number of Python and PowerShell scripts related to YARA and Malware Analysis. What if I combined them into a single utility? While we’re at it, let’s rewrite them all from scratch in Rust. Boy, do I know how to let loose on the weekends.

MalChela

MalChela combines (currently 10) programs in one Rust workspace, that can be invoked using a launcher.

MalChela screenshot

Features:

Combine YARAPoint it at a directory of YARA files and it will output one combined rule
Extract SamplesPoint it at a directory of password protected malware files to extract all
Hash ItPoint it to a file and get the MD5, SHA1 and SHA256 hash
MZMD5Recurse a directory, for files with MZ header, create hash list
MZcountRecurse a directory, uses YARA to count MZ, Zip, PDF, other 
NSRL MD5 LookupQuery a MD5 hash against NSRL
NSRL SHA1 LookupQuery a SHA1hash against NSRL 
Strings to YARAPrompts for metadata and strings (text file) to create a YARA rule
Malware Hash LookupQuery a hash value against VirusTotal & Malware Bazaar*
XMZMD5Recurse a directory, for files without MZ, Zip or PDF header, create hash list

*The Malware Hash Lookup requires an api key for Virus Total and Malware Bazaar.  If unidentified , MalChela will prompt you to create them the first time you run the malware lookup function.

What’s with the Name?

mal — malware

chela — “crab hand”

A chela on a crab is the scientific term for a claw or pincer. It’s a specialized appendage, typically found on the first pair of legs, used for grasping, defense, and manipulating things;  just like these programs.

Sounds Awesome – How do I install it?

Install Rust – https://rustup.rs/

then

git clone https://github.com/dwmetz/MalChela.git
cd MalChela
cargo build

Run

cargo run -p malchela

Feedback

I’d love to get your feedback on this. Please download it and give it a try. I’m open to suggestions for adding additional capabilities.

https://github.com/dwmetz/MalChela

Exploring Magnet Virtual Summit 2025 CTF Challenges, Part II (Windows)

This is part II of this series; for part I see Exploring Magnet Virtual Summit 2025 CTF Challenges (iOS).


If we jump into Axiom and head to the User Accounts, we can see that the SID for chick is S-1-5-21-493923485-410185161-2094537482-1001.


Windows Event Logs will track user login and logoff activity. The primary event IDs for Windows logoff are:
1. Event ID 4647: This is logged when a user manually initiates a logoff process. It is typically associated with interactive and remote-interactive logon types and indicates user-initiated activity.
2. Event ID 4634: This is logged when a logon session is terminated and no longer exists. It can result from system actions (e.g., idle timeout or shutdown) rather than explicit user action. It often follows Event ID 4647 if the logoff was user-initiated.

In Axiom we can find the most recent 4634 event at 11/24/2024 5:36:55 PM, formatted for the challenge as 2024-11-24 17:36:55.


The download source from the crow.jpg URL is https://www.treehugger.com/thmb/EmZOvx28sGNqCtDqQQBMGv-aezM=/4288×2848/filters:fill(auto,1)/__opt__aboutcom__coeus__resources__content_migration__mnn__images__2019__10__american-crow-portrait-01-b3f0cfbdbf164de59831c9725bfdbf67.jpg


In the Installed Programs under Application Usage we can see that com.CandyCrushSaga was installed. This is the package name for Candy Crush.


Under the Web Related artifacts, specifically Edge Chromium Web History we can see traffic to https://x.com/bfp_news which is the Twitter/X site for Burlington Free Press.


Refined Results, Social Media URLs, shows that the user visited the subreddit of https://reddit.com/r/coding.


The question itself practically gives it away, but we’ll check the Installed Programs to be safe. Sure enough the user had Python installed.


Event ID 4720 is a Windows Security Log event that is generated whenever a new user account is successfully created on a system. The creation date for Mary’s account is 2024-09-24 15:11:51.


As someone who used to geocache frequently, this question was a pleasant surprise. Already having an account on geocaching.com also helped.

There’s a fair amount of results if you search on geocaching, but there is only reference in the history to an actual geocache location (GCM70J) titled “Something’s Fishy.”


First we need to identify what counter-forensics tools may have been in use. In the user’s download activity we see that SDelete was downloaded.

If we look at the PowerShell history, ConsoleHost_history.txt, we can see that the command sdelete success.txt.txt was executed.


There are multiple evidence items indicating that the user was also using Proton Mail on the device with the account hackergotyou@proton.me.


The default browser can be identified from the Registry at Software\Microsoft\Windows\Shell\Associations\UrlAssociations\https\UserChoice. In this instance the user was using Edge, the default, as their browser.


In the browser history we see that the user accessed a video at the url, https://www.tiktok.com/@dochristmass/video/7436518844501347616?is_from_webapp=1

To get the time the video was posted, we can copy the url into UNFURL. This reveals the timestamp the video was posted as 2024-11-22 22:11:09.


Again with a filter on ‘geocaching’ we see a fair amount of activity. There is a url with “join” that appears to be part of the user sign in, including username=geomaryr

We also have an entry under Edge Chromium Autofill, as the user opted to save the login ID on that page.

Lastly we can double-check the geocaching.com site with the log for “Something’s Fishy” which matches the timestamp of the web activity. geomaryr is Mary’s username on geocaching.com


The first thought would be to go to the Passwords and Tokens Refined Result. We see a hash for the chick user account. But Wait!

The key is in “Shadow.” It’s not the Windows account we’re looking for.

Looking at Installed Programs we identify that the user installed KALI in Windows Subsystem for Linux (WSL). I knew I wanted to get the /etc/shadow file from the KALI installation – but I was hitting a wall on how.

Finally I wound up exporting the ext4.vhdx (the virtual hard drive for the KALI instance) and running strings against it, and piped the results to ag (grep on steroids) with a search for ‘chick:’.

Much like the way my father would describe my shots back when we would play golf together, ugly but effective. The hash for the user account chick is $fRLLkVPTrLiLVAGhQRWjQd.kKDyvvj040aDd5zoJRt4.


There were a few more challenges under the Windows category but that was as far as I made it in the time allotted.

I hope you’ve enjoyed these walk throughs on my approach to solving the challenges.

If you’d like to access the images used for the CFT for your own training and investigation, you can find them at https://cfreds.nist.gov/all/Hexordia/2025MVSCTF. In addition to the Windows 11 image used here, there is also full file system extractions of Android and iOS, as well as two Google Takeout exports. It’s a great reference set for practicing.