Login
CyberChef is a simple, intuitive web app for carrying out all manner of "cyber" operations within a web browser. These operations include simple encoding like XOR and Base64, more complex encryption like AES, DES and Blowfish, creating binary and hexdumps, compression and decompression of data, calculating hashes and checksums, IPv6 and X.509 parsing, changing character encodings, and much more.
The tool is designed to enable both technical and non-technical analysts to manipulate data in complex ways without having to deal with complex tools or algorithms. It was conceived, designed, built and incrementally improved by an analyst in their 10% innovation time over several years.
CyberChef is still under active development. As a result, it shouldn't be considered a finished product. There is still testing and bug fixing to do, new features to be added and additional documentation to write. Please contribute!
Cryptographic operations in CyberChef should not be relied upon to provide security in any situation. No guarantee is offered for their correctness.
A live demo can be found here - have fun!
If you would like to try out CyberChef locally you can either build it yourself:
FreshRSS docker build --tag cyberchef --ulimit nofile=10000 .
docker run -it -p 8080:80 cyberchef
Or you can use our image directly:
docker run -it -p 8080:80 ghcr.io/gchq/cyberchef:latest
This image is built and published through our GitHub Workflows
There are four main areas in CyberChef:
You can use as many operations as you like in simple or complex ways. Some examples are as follows:
By manipulating CyberChef's URL hash, you can change the initial settings with which the page opens. The format is https://gchq.github.io/CyberChef/#recipe=Operation()&input=...
Supported arguments are recipe, input (encoded in Base64), and theme.
CyberChef is built to support
CyberChef is built to fully support Node.js v16. For more information, see the "Node API" wiki page
Contributing a new operation to CyberChef is super easy! The quickstart script will walk you through the process. If you can write basic JavaScript, you can write a CyberChef operation.
An installation walkthrough, how-to guides for adding new operations and themes, descriptions of the repository structure, available data types and coding conventions can all be found in the "Contributing" wiki page.
This is an alpha release of an assemblies.blob AssemblyStore parser written in Python. The tool is capable of unpack and repackaging assemblies.blob and assemblies.manifest Xamarin files from an APK.
Run the installer script:
python setup.py install
You can then use the tool by calling pyxamstore
I recommend using the tool in conjunction with apktool. The following commands can be used to unpack an APK and unpack the Xamarin DLLs:
apktool d yourapp.apk
pyxamstore unpack -d yourapp/unknown/assemblies/
Assemblies that are detected as compressed with LZ4 will be automatically decompressed in the extraction process.
If you want to make changes to the DLLs within the AssemblyStore, you can use pyxamstore along with the assemblies.json generated during the unpack to create a new assemblies.blob file(s). The following command from the directory where your assemblies.json file exists:
pyxamstore pack
From here you'll need to copy the new manifest and blobs as well as repackage/sign the APK.
Additional file format details can be found on my personal website.
AiCEF is a tool implementing the accompanying framework [1] in order to harness the intelligence that is available from online resources, as well as threat groups' activities, arsenal (eg. MITRE), to create relevant and timely cybersecurity exercise content. This way, we abstract the events from the reports in a machine-readable form. The produced graphs can be infused with additional intelligence, e.g. the threat actor profile from MITRE, also mapped in our ontology. While this may fill gaps that would be missing from a report, one can also manipulate the graph to create custom and unique models. Finally, we exploit transformer-based language models like GPT to convert the graph into text that can serve as the scenario of a cybersecurity exercise. We have tested and validated AiCEF with a group of experts in cybersecurity exercises, and the results clearly show that AiCEF significantly augments the capabilities in creating timely and relevant cybersecurity exercises in terms of both quality and time.
We used Python to create a machine-learning-powered Exercise Generation Framework and developed a set of tools to perform a set of individual tasks which would help an exercise planner (EP) to create a timely and targeted Cybersecurity Exercise Scenario, regardless of her experience.
Problems an Exercise Planner faces:
Our Main Objective: Build an AI powered tool that can generate relevant and up-to-date Cyber Exercise Content in a few steps with little technical expertise from the user.
The updated project, AiCEF v.2.0 is planned to be publicly released by the end of 2023, pending heavy code review and functionality updates. Submodules with reduced functinality will start being release by early June 2023. Thank you for your patience.
The most convenient way to install AiCEF is by using the docker-compose command. For production deployment, we advise you deploy MySQL manually in a dedicated environment and then to start the other components using Docker.
First, make sure you have docker-compose installed in your environment:
$ sudo apt-get install docker-composeThen, clone the repository:
$ git clone https://github.com/grazvan/AiCEF/docker.git /<choose-a-path>/AiCEF-docker
$ cd /<choose-a-path>/AiCEF-dockerImport the MySQL file in your
$ mysql -u <your_username> รขโฌโ-password=<your_password> AiCEF_db < AiCEF_db.sql Before running the docker-compose command, settings must be configured. Copy the sample settings file and change it accordingly to your needs.
$ cp .env.sample .envNote: Make sure you have an OpenAI API key available. Load the environment setttings (including your MySQL connection details):
set -a ; source .envFinally, run docker-compose in detached (-d) mode:
$ sudo docker-compose up -dA common usage flow consists of generating a Trend Report to analyze patterns over time, parsing relevant articles and converting them into Incident Breadcrumbs using MLTP module and storing them in a knowledge database called KDb. Incidents are then generated using IncGen component and can be enhanced using the Graph Enhancer module to simulate known APT activity. The incidents come with injects that can be edited on the fly. The CSE scenario is then created using CEGen, which defines various attributes like CSE name, number of Events, and Incidents. MLCESO is a crucial step in the methodology where dedicated ML models are trained to extract information from the collected articles with over 80% accuracy. The Incident Generation & Enhancer (IncGen) workflow can be automated, generating a variety of incidents based on filtering parameters and the existing database. The knowledge database (KDB) consists of almost 3000 articles classified into six categories that can be augmented using APT Enhancer by using the activity of known APT groups from MITRE or manually.
Find below some sample usage screenshots:
AiCEF is a product designed and developed by Alex Zacharis, Razvan Gavrila and Constantinos Patsakis.
[1] https://link.springer.com/article/10.1007/s10207-023-00693-z
[2] https://oasis-open.github.io/cti-documentation/stix/intro.html
Contributions are welcome! If you'd like to contribute to AiCEF v2.0, please follow these steps:
git checkout -b feature/your-branch-name)git commit -m 'Add some feature')git push origin feature/your-branch-name)AiCEF is licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. See for more information.
Under the following terms:
Attribution โ You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. NonCommercial โ You may not use the material for commercial purposes. No additional restrictions โ You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
This script will parse all the channels of events from the win-event log to extract all the log relatives to AppLocker. The script will gather all the important pieces of information relative to the events for forensic or threat-hunting purposes, or even in order to troubleshoot. Here are the logs we fetch from win-event:
And, The result will be saved to a csv file: AppLocker-log.csv
The juicy and useful information you will get with this script are:
This parameter specifies the type of events you are interested in, there are 04 values for this parameter:
1. All
This gets all the events of AppLocker that are interesting for threat-hunting, forensic or even troubleshooting. This is the default value.
.\Get-AppLockerEventlog.ps1 -HunType All
2. Block
This gets all the events that are triggered by the action of blocking an application by AppLocker, this type is critical for threat-hunting or forensics, and comes with high priority, since it indicates malicious attempts, or could be a good indicator of prior malicious activity in order to evade defensive mechanisms.
.\Get-AppLockerEventlog.ps1 -HunType Block |Format-Table -AutoSize
3. Allow
This gets all the events that are triggered by the action of Allowing an application by AppLocker. For threat-hunting or forensics, even the allowed applications should be monitored, in order to detect any possible bypass or configuration mistakes.
.\Get-AppLockerEventlog.ps1 -HunType Allow | Format-Table -AutoSize
4. Audit
This gets all the events generated when AppLocker would block the application if the enforcement mode were enabled (Audit mode). For threat-hunting or forensics, this could indicate any configuration mistake, neglect from the admin to switch the mode, or even a malicious action that happened in the audit phase (tuning phase).
.\Get-AppLockerEventlog.ps1 -HunType Audit
To better understand AppLocker :
Diving in AppLocker for Blue Team โ Part 1
This project welcomes contributions and suggestions.
Quietly enumerate an Active Directory Domain via LDAP parsing users, admins, groups, etc. Created by Nick Swink from Layer 8 Security.
sudo python3 -m pip install --user pipenv
git clone https://github.com/layer8secure/SilentHound.git
cd silenthound
pipenv install
This will create an isolated virtual environment with dependencies needed for the project. To use the project you can either open a shell in the virtualenv withpipenv shellor run commands directly withpipenv run.
This method is not recommended because python-ldap can cause many dependency errors.
Install dependencies with pip:
python3 -m pip install -r requirements.txt
python3 silenthound.py -h
$ pipenv run python silenthound.py -h
usage: silenthound.py [-h] [-u USERNAME] [-p PASSWORD] [-o OUTPUT] [-g] [-n] [-k] TARGET domain
Quietly enumerate an Active Directory environment.
positional arguments:
TARGET Domain Controller IP
domain Dot (.) separated Domain name including both contexts e.g. ACME.com / HOME.local / htb.net
optional arguments:
-h, --help show this help message and exit
-u USERNAME, --username USERNAME
LDAP username - not the same as user principal name. E.g. Username: bob.dole might be 'bob
dole'
-p PASSWORD, --password PASSWORD
LDAP passwo rd - use single quotes 'password'
-o OUTPUT, --output OUTPUT
Name for output files. Creates output files for hosts, users, domain admins, and descriptions
in the current working directory.
-g, --groups Display Group names with user members.
-n, --org-unit Display Organizational Units.
-k, --keywords Search for key words in LDAP objects.
A lightweight tool to quickly and quietly enumerate an Active Directory environment. The goal of this tool is to get a Lay of the Land whilst making as little noise on the network as possible. The tool will make one LDAP query that is used for parsing, and create a cache file to prevent further queries/noise on the network. If no credentials are passed it will attempt anonymous BIND.
Using the -o flag will result in output files for each section normally in stdout. The files created using all flags will be:
-rw-r--r-- 1 kali kali 122 Jun 30 11:37 BASENAME-descriptions.txt
-rw-r--r-- 1 kali kali 60 Jun 30 11:37 BASENAME-domain_admins.txt
-rw-r--r-- 1 kali kali 2620 Jun 30 11:37 BASENAME-groups.txt
-rw-r--r-- 1 kali kali 89 Jun 30 11:37 BASENAME-hosts.txt
-rw-r--r-- 1 kali kali 1940 Jun 30 11:37 BASENAME-keywords.txt
-rw-r--r-- 1 kali kali 66 Jun 30 11:37 BASENAME-org.txt
-rw-r--r-- 1 kali kali 529 Jun 30 11:37 BASENAME-users.txt
For additional feature requests please submit an issue and add the enhancement tag.
