Security of Information, Threat Intelligence, Hacking, Offensive Security, Pentest, Open Source, Hackers Tools, Leaks, Pr1v8, Premium Courses Free, etc

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    BackBox is a penetration test and security assessment oriented Ubuntu-based Linux distribution providing a network and informatic systems analysis toolkit. It includes a complete set of tools required for ethical hacking and security testing...
  • Pentest Distro Linux - Weakerth4n

    Weakerth4n is a penetration testing distribution which is built from Debian Squeeze.For the desktop environment it uses Fluxbox...
  • The Amnesic Incognito Live System - Tails

    Tails is a live system that aims to preserve your privacy and anonymity. It helps you to use the Internet anonymously and circumvent censorship...
  • Penetration Testing Distribution - BlackArch

    BlackArch is a penetration testing distribution based on Arch Linux that provides a large amount of cyber security tools. It is an open-source distro created specially for penetration testers and security researchers...
  • The Best Penetration Testing Distribution - Kali Linux

    Kali Linux is a Debian-based distribution for digital forensics and penetration testing, developed and maintained by Offensive Security. Mati Aharoni and Devon Kearns rewrote BackTrack...
  • Friendly OS designed for Pentesting - ParrotOS

    Parrot Security OS is a cloud friendly operating system designed for Pentesting, Computer Forensic, Reverse engineering, Hacking, Cloud pentesting...

Tuesday, February 20, 2024

SwaggerSpy - Automated OSINT On SwaggerHub


SwaggerSpy is a tool designed for automated Open Source Intelligence (OSINT) on SwaggerHub. This project aims to streamline the process of gathering intelligence from APIs documented on SwaggerHub, providing valuable insights for security researchers, developers, and IT professionals.


What is Swagger?

Swagger is an open-source framework that allows developers to design, build, document, and consume RESTful web services. It simplifies API development by providing a standard way to describe REST APIs using a JSON or YAML format. Swagger enables developers to create interactive documentation for their APIs, making it easier for both developers and non-developers to understand and use the API.


About SwaggerHub

SwaggerHub is a collaborative platform for designing, building, and managing APIs using the Swagger framework. It offers a centralized repository for API documentation, version control, and collaboration among team members. SwaggerHub simplifies the API development lifecycle by providing a unified platform for API design and testing.


Why OSINT on SwaggerHub?

Performing OSINT on SwaggerHub is crucial because developers, in their pursuit of efficient API documentation and sharing, may inadvertently expose sensitive information. Here are key reasons why OSINT on SwaggerHub is valuable:

  1. Developer Oversights: Developers might unintentionally include secrets, credentials, or sensitive information in API documentation on SwaggerHub. These oversights can lead to security vulnerabilities and unauthorized access if not identified and addressed promptly.

  2. Security Best Practices: OSINT on SwaggerHub helps enforce security best practices. Identifying and rectifying potential security issues early in the development lifecycle is essential to ensure the confidentiality and integrity of APIs.

  3. Preventing Data Leaks: By systematically scanning SwaggerHub for sensitive information, organizations can proactively prevent data leaks. This is especially crucial in today's interconnected digital landscape where APIs play a vital role in data exchange between services.

  4. Risk Mitigation: Understanding that developers might forget to remove or obfuscate sensitive details in API documentation underscores the importance of continuous OSINT on SwaggerHub. This proactive approach mitigates the risk of unintentional exposure of critical information.

  5. Compliance and Privacy: Many industries have stringent compliance requirements regarding the protection of sensitive data. OSINT on SwaggerHub ensures that APIs adhere to these regulations, promoting a culture of compliance and safeguarding user privacy.

  6. Educational Opportunities: Identifying oversights in SwaggerHub documentation provides educational opportunities for developers. It encourages a security-conscious mindset, fostering a culture of awareness and responsible information handling.

By recognizing that developers can inadvertently expose secrets, OSINT on SwaggerHub becomes an integral part of the overall security strategy, safeguarding against potential threats and promoting a secure API ecosystem.


How SwaggerSpy Works

SwaggerSpy obtains information from SwaggerHub and utilizes regular expressions to inspect API documentation for sensitive information, such as secrets and credentials.


Getting Started

To use SwaggerSpy, follow these steps:

  1. Installation: Clone the SwaggerSpy repository and install the required dependencies.
git clone https://github.com/UndeadSec/SwaggerSpy.git
cd SwaggerSpy
pip install -r requirements.txt
  1. Usage: Run SwaggerSpy with the target search terms (more accurate with domains).
python swaggerspy.py searchterm
  1. Results: SwaggerSpy will generate a report containing OSINT findings, including information about the API, endpoints, and secrets.

Disclaimer

SwaggerSpy is intended for educational and research purposes only. Users are responsible for ensuring that their use of this tool complies with applicable laws and regulations.


Contribution

Contributions to SwaggerSpy are welcome! Feel free to submit issues, feature requests, or pull requests to help improve this tool.


About the Author

SwaggerSpy is developed and maintained by Alisson Moretto (UndeadSec)

I'm a passionate cyber threat intelligence pro who loves sharing insights and crafting cybersecurity tools.


TODO

Regular Expressions Enhancement
  • [ ] Review and improve existing regular expressions.
  • [ ] Ensure that regular expressions adhere to best practices.
  • [ ] Check for any potential optimizations in the regex patterns.
  • [ ] Test regular expressions with various input scenarios for accuracy.
  • [ ] Document any complex or non-trivial regex patterns for better understanding.
  • [ ] Explore opportunities to modularize or break down complex patterns.
  • [ ] Verify the regular expressions against the latest specifications or requirements.
  • [ ] Update documentation to reflect any changes made to the regular expressions.

License

SwaggerSpy is licensed under the MIT License. See the LICENSE file for details.


Thanks

Special thanks to @Liodeus for providing project inspiration through swaggerHole.


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Sunday, February 18, 2024

Navigating Telegram’s Underworld: A Cipher for the Elite Hackers




In the encrypted depths of Telegram, far beyond the scrutiny of average netizens, lies a network pulsating with the lifeblood of the hacking elite. This isn’t your run-of-the-mill tutorial or a hacker’s 101 guide. This post is a deep dive into the abyss, mapping the veins of active and dormant channels that are the backbone of cyber threat intelligence and underground hacking operations.

The channels we’re dissecting today are not just communication lines; they are the hidden layers of the onion, each peel revealing more about the dark arts of digital dominance. From active dens where real-time data breaches, exploit trades, and botnet controls unfold, to the ghostly silence of channels once alive with the chatter of codes and hacks now lying dormant or expired - every link, every channel, serves as a node in the vast neural network of the global hacking community.

Active Channels: The Frontlines

Here, in the buzzing hive of active channels, you're as likely to find a zero-day exploit as you are a discussion on the latest evasion techniques. This isn't just about sharing tools or data; it's a relentless innovation race. Techniques, scripts, and methodologies discussed here are not for the faint-hearted but for those who command the terminal like it’s an extension of their mind.

Dormant/Expired Channels: The Archives

The silent corridors of expired channels are not just digital tombs; they are treasure troves of past operations, a testament to the ephemeral nature of digital power. Each one holds lessons, failures, and victories. They are the archives for those willing to learn from history to master the future.



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AzSubEnum - Azure Service Subdomain Enumeration


AzSubEnum is a specialized subdomain enumeration tool tailored for Azure services. This tool is designed to meticulously search and identify subdomains associated with various Azure services. Through a combination of techniques and queries, AzSubEnum delves into the Azure domain structure, systematically probing and collecting subdomains related to a diverse range of Azure services.


How it works?

AzSubEnum operates by leveraging DNS resolution techniques and systematic permutation methods to unveil subdomains associated with Azure services such as Azure App Services, Storage Accounts, Azure Databases (including MSSQL, Cosmos DB, and Redis), Key Vaults, CDN, Email, SharePoint, Azure Container Registry, and more. Its functionality extends to comprehensively scanning different Azure service domains to identify associated subdomains.

With this tool, users can conduct thorough subdomain enumeration within Azure environments, aiding security professionals, researchers, and administrators in gaining insights into the expansive landscape of Azure services and their corresponding subdomains.


Why i create this?

During my learning journey on Azure AD exploitation, I discovered that the Azure subdomain tool, Invoke-EnumerateAzureSubDomains from NetSPI, was unable to run on my Debian PowerShell. Consequently, I created a crude implementation of that tool in Python.


Usage
➜  AzSubEnum git:(main) ✗ python3 azsubenum.py --help
usage: azsubenum.py [-h] -b BASE [-v] [-t THREADS] [-p PERMUTATIONS]

Azure Subdomain Enumeration

options:
-h, --help show this help message and exit
-b BASE, --base BASE Base name to use
-v, --verbose Show verbose output
-t THREADS, --threads THREADS
Number of threads for concurrent execution
-p PERMUTATIONS, --permutations PERMUTATIONS
File containing permutations

Basic enumeration:

python3 azsubenum.py -b retailcorp --thread 10

Using permutation wordlists:

python3 azsubenum.py -b retailcorp --thread 10 --permutation permutations.txt

With verbose output:

python3 azsubenum.py -b retailcorp --thread 10 --permutation permutations.txt --verbose



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NullSection - An Anti-Reversing Tool That Applies A Technique That Overwrites The Section Header With Nullbytes


NullSection is an Anti-Reversing tool that applies a technique that overwrites the section header with nullbytes.


Install
git clone https://github.com/MatheuZSecurity/NullSection
cd NullSection
gcc nullsection.c -o nullsection
./nullsection

Advantage

When running nullsection on any ELF, it could be .ko rootkit, after that if you use Ghidra/IDA to parse ELF functions, nothing will appear no function to parse in the decompiler for example, even if you run readelf -S / path /to/ elf the following message will appear "There are no sections in this file."

Make good use of the tool!


Note
We are not responsible for any damage caused by this tool, use the tool intelligently and for educational purposes only.

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WEB-Wordlist-Generator - Creates Related Wordlists After Scanning Your Web Applications


WEB-Wordlist-Generator scans your web applications and creates related wordlists to take preliminary countermeasures against cyber attacks.


Done
  • [x] Scan Static Files.
  • [ ] Scan Metadata Of Public Documents (pdf,doc,xls,ppt,docx,pptx,xlsx etc.)
  • [ ] Create a New Associated Wordlist with the Wordlist Given as a Parameter.

Installation

From Git
git clone https://github.com/OsmanKandemir/web-wordlist-generator.git
cd web-wordlist-generator && pip3 install -r requirements.txt
python3 generator.py -d target-web.com

From Dockerfile

You can run this application on a container after build a Dockerfile.

docker build -t webwordlistgenerator .
docker run webwordlistgenerator -d target-web.com -o

From DockerHub

You can run this application on a container after pulling from DockerHub.

docker pull osmankandemir/webwordlistgenerator:v1.0
docker run osmankandemir/webwordlistgenerator:v1.0 -d target-web.com -o

Usage
-d DOMAINS [DOMAINS], --domains DOMAINS [DOMAINS] Input Multi or Single Targets. --domains target-web1.com target-web2.com
-p PROXY, --proxy PROXY Use HTTP proxy. --proxy 0.0.0.0:8080
-a AGENT, --agent AGENT Use agent. --agent 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)'
-o PRINT, --print PRINT Use Print outputs on terminal screen.


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CloudMiner - Execute Code Using Azure Automation Service Without Getting Charged


Execute code within Azure Automation service without getting charged

Description

CloudMiner is a tool designed to get free computing power within Azure Automation service. The tool utilizes the upload module/package flow to execute code which is totally free to use. This tool is intended for educational and research purposes only and should be used responsibly and with proper authorization.

  • This flow was reported to Microsoft on 3/23 which decided to not change the service behavior as it's considered as "by design". As for 3/9/23, this tool can still be used without getting charged.

  • Each execution is limited to 3 hours


Requirements

  1. Python 3.8+ with the libraries mentioned in the file requirements.txt
  2. Configured Azure CLI - https://learn.microsoft.com/en-us/cli/azure/install-azure-cli
    • Account must be logged in before using this tool

Installation

pip install .

Usage

usage: cloud_miner.py [-h] --path PATH --id ID -c COUNT [-t TOKEN] [-r REQUIREMENTS] [-v]

CloudMiner - Free computing power in Azure Automation Service

optional arguments:
-h, --help show this help message and exit
--path PATH the script path (Powershell or Python)
--id ID id of the Automation Account - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Automation/a
utomationAccounts/{automationAccountName}
-c COUNT, --count COUNT
number of executions
-t TOKEN, --token TOKEN
Azure access token (optional). If not provided, token will be retrieved using the Azure CLI
-r REQUIREMENTS, --requirements REQUIREMENTS
Path to requirements file to be installed and use by the script (relevant to Python scripts only)
-v, --verbose Enable verbose mode

Example usage

Python

Powershell

License

CloudMiner is released under the BSD 3-Clause License. Feel free to modify and distribute this tool responsibly, while adhering to the license terms.

Author - Ariel Gamrian


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BounceBack - Stealth Redirector For Your Red Team Operation Security


BounceBack is a powerful, highly customizable and configurable reverse proxy with WAF functionality for hiding your C2/phishing/etc infrastructure from blue teams, sandboxes, scanners, etc. It uses real-time traffic analysis through various filters and their combinations to hide your tools from illegitimate visitors.

The tool is distributed with preconfigured lists of blocked words, blocked and allowed IP addresses.

For more information on tool usage, you may visit project's wiki.

Features

  • Highly configurable and customizable filters pipeline with boolean-based concatenation of rules will be able to hide your infrastructure from the most keen blue eyes.
  • Easily extendable project structure, everyone can add rules for their own C2.
  • Integrated and curated massive blacklist of IPv4 pools and ranges known to be associated with IT Security vendors combined with IP filter to disallow them to use/attack your infrastructure.
  • Malleable C2 Profile parser is able to validate inbound HTTP(s) traffic against the Malleable's config and reject invalidated packets.
  • Out of the box domain fronting support allows you to hide your infrastructure a little bit more.
  • Ability to check the IPv4 address of request against IP Geolocation/reverse lookup data and compare it to specified regular expressions to exclude out peers connecting outside allowed companies, nations, cities, domains, etc.
  • All incoming requests may be allowed/disallowed for any time period, so you may configure work time filters.
  • Support for multiple proxies with different filter pipelines at one BounceBack instance.
  • Verbose logging mechanism allows you to keep track of all incoming requests and events for analyzing blue team behaviour and debug issues.

Rules

BounceBack currently supports the following filters:

  • Boolean-based (and, or, not) rules combinations
  • IP and subnet analysis
  • IP geolocation fields inspection
  • Reverse lookup domain probe
  • Raw packet regexp matching
  • Malleable C2 profiles traffic validation
  • Work (or not) hours rule

Custom rules may be easily added, just register your RuleBaseCreator or RuleWrapperCreator. See already created RuleBaseCreators and RuleWrapperCreators

Rules configuration page may be found here.

Proxies

At the moment, BounceBack supports the following protocols:

  • HTTP(s) for your web infrastructure
  • DNS for your DNS tunnels
  • Raw TCP (with or without tls) and UDP for custom protocols

Custom protocols may be easily added, just register your new type in manager. Example proxy realizations may be found here.

Proxies configuration page may be found here.

Installation

Just download latest release from release page, unzip it, edit config file and go on.

If you want to build it from source, install goreleaser and run:

goreleaser release --clean --snapshot

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PurpleKeep - Providing Azure Pipelines To Create An Infrastructure And Run Atomic Tests


With the rapidly increasing variety of attack techniques and a simultaneous rise in the number of detection rules offered by EDRs (Endpoint Detection and Response) and custom-created ones, the need for constant functional testing of detection rules has become evident. However, manually re-running these attacks and cross-referencing them with detection rules is a labor-intensive task which is worth automating.

To address this challenge, I developed "PurpleKeep," an open-source initiative designed to facilitate the automated testing of detection rules. Leveraging the capabilities of the Atomic Red Team project which allows to simulate attacks following MITRE TTPs (Tactics, Techniques, and Procedures). PurpleKeep enhances the simulation of these TTPs to serve as a starting point for the evaluation of the effectiveness of detection rules.

Automating the process of simulating one or multiple TTPs in a test environment comes with certain challenges, one of which is the contamination of the platform after multiple simulations. However, PurpleKeep aims to overcome this hurdle by streamlining the simulation process and facilitating the creation and instrumentation of the targeted platform.

Primarily developed as a proof of concept, PurpleKeep serves as an End-to-End Detection Rule Validation platform tailored for an Azure-based environment. It has been tested in combination with the automatic deployment of Microsoft Defender for Endpoint as the preferred EDR solution. PurpleKeep also provides support for security and audit policy configurations, allowing users to mimic the desired endpoint environment.

To facilitate analysis and monitoring, PurpleKeep integrates with Azure Monitor and Log Analytics services to store the simulation logs and allow further correlation with any events and/or alerts stored in the same platform.

TLDR: PurpleKeep provides an Attack Simulation platform to serve as a starting point for your End-to-End Detection Rule Validation in an Azure-based environment.

Requirements

The project is based on Azure Pipelines and requires the following to be able to run:

  • Azure Service Connection to a resource group as described in the Microsoft Docs
  • Assignment of the "Key Vault Administrator" Role for the previously created Enterprise Application
  • MDE onboarding script, placed as a Secure File in the Library of Azure DevOps and make it accessible to the pipelines

Optional

You can provide a security and/or audit policy file that will be loaded to mimic your Group Policy configurations. Use the Secure File option of the Library in Azure DevOps to make it accessible to your pipelines.

Refer to the variables file for your configurable items.

Design

Infrastructure

Deploying the infrastructure uses the Azure Pipeline to perform the following steps:

  • Deploy Azure services:
    • Key Vault
    • Log Analytics Workspace
    • Data Connection Endpoint
    • Data Connection Rule
  • Generate SSH keypair and password for the Windows account and store in the Key Vault
  • Create a Windows 11 VM
  • Install OpenSSH
  • Configure and deploy the SSH public key
  • Install Invoke-AtomicRedTeam
  • Install Microsoft Defender for Endpoint and configure exceptions
  • (Optional) Apply security and/or audit policy files
  • Reboot

Simulation

Currently only the Atomics from the public repository are supported. The pipelines takes a Technique ID as input or a comma seperate list of techniques, for example:

  • T1059.003
  • T1027,T1049,T1003

The logs of the simulation are ingested into the AtomicLogs_CL table of the Log Analytics Workspace.

There are currently two ways to run the simulation:

Rotating simulation

This pipeline will deploy a fresh platform after the simulation of each TTP. The Log Analytic workspace will maintain the logs of each run.

Warning: this will onboard a large number of hosts into your EDR

Single deploy simulation

A fresh infrastructure will be deployed only at the beginning of the pipeline. All TTP's will be simulated on this instance. This is the fastests way to simulate and prevents onboarding a large number of devices, however running a lot of simulations in a same environment has the risk of contaminating the environment and making the simulations less stable and predictable.

TODO

Must have

  • Check if pre-reqs have been fullfilled before executing the atomic
  • Provide the ability to import own group policy
  • Cleanup biceps and pipelines by using a master template (Complete build)
  • Build pipeline that runs technique sequently with reboots in between
  • Add Azure ServiceConnection to variables instead of parameters

Nice to have

  • MDE Off-boarding (?)
  • Automatically join and leave AD domain
  • Make Atomics repository configureable
  • Deploy VECTR as part of the infrastructure and ingest results during simulation. Also see the VECTR API issue
  • Tune alert API call to Microsoft Defender for Endpoint (Microsoft.Security alertsSuppressionRules)
  • Add C2 infrastructure for manual or C2 based simulations

Issues

  • Atomics do not return if a simulation succeeded or not
  • Unreliable OpenSSH extension installer failing infrastructure deployment
  • Spamming onboarded devices in the EDR

References


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BucketLoot - An Automated S3-compatible Bucket Inspector


BucketLoot is an automated S3-compatible Bucket inspector that can help users extract assets, flag secret exposures and even search for custom keywords as well as Regular Expressions from publicly-exposed storage buckets by scanning files that store data in plain-text.

The tool can scan for buckets deployed on Amazon Web Services (AWS), Google Cloud Storage (GCS), DigitalOcean Spaces and even custom domains/URLs which could be connected to these platforms. It returns the output in a JSON format, thus enabling users to parse it according to their liking or forward it to any other tool for further processing.

BucketLoot comes with a guest mode by default, which means a user doesn't needs to specify any API tokens / Access Keys initially in order to run the scan. The tool will scrape a maximum of 1000 files that are returned in the XML response and if the storage bucket contains more than 1000 entries which the user would like to run the scanner on, they can provide platform credentials to run a complete scan. If you'd like to know more about the tool, make sure to check out our blog.

Features

Secret Scanning

Scans for over 80+ unique RegEx signatures that can help in uncovering secret exposures tagged with their severity from the misconfigured storage bucket. Users have the ability to modify or add their own signatures in the regexes.json file. If you believe you have any cool signatures which might be helpful for others too and could be flagged at scale, go ahead and make a PR!

Sensitive File Checks

Accidental sensitive file leakages are a big problem that affects the security posture of individuals and organisations. BucketLoot comes with a 80+ unique regEx signatures list in vulnFiles.json which allows users to flag these sensitive files based on file names or extensions.

Dig Mode

Want to quickly check if any target website is using a misconfigured bucket that is leaking secrets or any other sensitive data? Dig Mode allows you to pass non-S3 targets and let the tool scrape URLs from response body for scanning.

Asset Extraction

Interested in stepping up your asset discovery game? BucketLoot extracts all the URLs/Subdomains and Domains that could be present in an exposed storage bucket, enabling you to have a chance of discovering hidden endpoints, thus giving you an edge over the other traditional recon tools.

Searching

The tool goes beyond just asset discovery and secret exposure scanning by letting users search for custom keywords and even Regular Expression queries which may help them find exactly what they are looking for.

To know more about our Attack Surface Management platform, check out NVADR.


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Raven - CI/CD Security Analyzer


RAVEN (Risk Analysis and Vulnerability Enumeration for CI/CD) is a powerful security tool designed to perform massive scans for GitHub Actions CI workflows and digest the discovered data into a Neo4j database. Developed and maintained by the Cycode research team.

With Raven, we were able to identify and report security vulnerabilities in some of the most popular repositories hosted on GitHub, including:

We listed all vulnerabilities discovered using Raven in the tool Hall of Fame.

What is Raven

The tool provides the following capabilities to scan and analyze potential CI/CD vulnerabilities:

  • Downloader: You can download workflows and actions necessary for analysis. Workflows can be downloaded for a specified organization or for all repositories, sorted by star count. Performing this step is a prerequisite for analyzing the workflows.
  • Indexer: Digesting the downloaded data into a graph-based Neo4j database. This process involves establishing relationships between workflows, actions, jobs, steps, etc.
  • Query Library: We created a library of pre-defined queries based on research conducted by the community.
  • Reporter: Raven has a simple way of reporting suspicious findings. As an example, it can be incorporated into the CI process for pull requests and run there.

Possible usages for Raven:

  • Scanner for your own organization's security
  • Scanning specified organizations for bug bounty purposes
  • Scan everything and report issues found to save the internet
  • Research and learning purposes

This tool provides a reliable and scalable solution for CI/CD security analysis, enabling users to query bad configurations and gain valuable insights into their codebase's security posture.

Why Raven

In the past year, Cycode Labs conducted extensive research on fundamental security issues of CI/CD systems. We examined the depths of many systems, thousands of projects, and several configurations. The conclusion is clear – the model in which security is delegated to developers has failed. This has been proven several times in our previous content:

  • A simple injection scenario exposed dozens of public repositories, including popular open-source projects.
  • We found that one of the most popular frontend frameworks was vulnerable to the innovative method of branch injection attack.
  • We detailed a completely different attack vector, 3rd party integration risks, the most popular project on GitHub, and thousands more.
  • Finally, the Microsoft 365 UI framework, with more than 300 million users, is vulnerable to an additional new threat – an artifact poisoning attack.
  • Additionally, we found, reported, and disclosed hundreds of other vulnerabilities privately.

Each of the vulnerabilities above has unique characteristics, making it nearly impossible for developers to stay up to date with the latest security trends. Unfortunately, each vulnerability shares a commonality – each exploitation can impact millions of victims.

It was for these reasons that Raven was created, a framework for CI/CD security analysis workflows (and GitHub Actions as the first use case). In our focus, we examined complex scenarios where each issue isn't a threat on its own, but when combined, they pose a severe threat.

Setup && Run

To get started with Raven, follow these installation instructions:

Step 1: Install the Raven package

pip3 install raven-cycode

Step 2: Setup a local Redis server and Neo4j database

docker run -d --name raven-neo4j -p7474:7474 -p7687:7687 --env NEO4J_AUTH=neo4j/123456789 --volume raven-neo4j:/data neo4j:5.12
docker run -d --name raven-redis -p6379:6379 --volume raven-redis:/data redis:7.2.1

Another way to setup the environment is by running our provided docker compose file:

git clone https://github.com/CycodeLabs/raven.git
cd raven
make setup

Step 3: Run Raven Downloader

Org mode:

raven download org --token $GITHUB_TOKEN --org-name RavenDemo

Crawl mode:

raven download crawl --token $GITHUB_TOKEN --min-stars 1000

Step 4: Run Raven Indexer

raven index

Step 5: Inspect the results through the reporter

raven report --format raw

At this point, it is possible to inspect the data in the Neo4j database, by connecting http://localhost:7474/browser/.

Prerequisites

  • Python 3.9+
  • Docker Compose v2.1.0+
  • Docker Engine v1.13.0+

Infrastructure

Raven is using two primary docker containers: Redis and Neo4j. make setup will run a docker compose command to prepare that environment.

Usage

The tool contains three main functionalities, download and index and report.

Download

Download Organization Repositories

usage: raven download org [-h] --token TOKEN [--debug] [--redis-host REDIS_HOST] [--redis-port REDIS_PORT] [--clean-redis] --org-name ORG_NAME

options:
-h, --help show this help message and exit
--token TOKEN GITHUB_TOKEN to download data from Github API (Needed for effective rate-limiting)
--debug Whether to print debug statements, default: False
--redis-host REDIS_HOST
Redis host, default: localhost
--redis-port REDIS_PORT
Redis port, default: 6379
--clean-redis, -cr Whether to clean cache in the redis, default: False
--org-name ORG_NAME Organization name to download the workflows

Download Public Repositories

usage: raven download crawl [-h] --token TOKEN [--debug] [--redis-host REDIS_HOST] [--redis-port REDIS_PORT] [--clean-redis] [--max-stars MAX_STARS] [--min-stars MIN_STARS]

options:
-h, --help show this help message and exit
--token TOKEN GITHUB_TOKEN to download data from Github API (Needed for effective rate-limiting)
--debug Whether to print debug statements, default: False
--redis-host REDIS_HOST
Redis host, default: localhost
--redis-port REDIS_PORT
Redis port, default: 6379
--clean-redis, -cr Whether to clean cache in the redis, default: False
--max-stars MAX_STARS
Maximum number of stars for a repository
--min-stars MIN_STARS
Minimum number of stars for a repository, default : 1000

Index

usage: raven index [-h] [--redis-host REDIS_HOST] [--redis-port REDIS_PORT] [--clean-redis] [--neo4j-uri NEO4J_URI] [--neo4j-user NEO4J_USER] [--neo4j-pass NEO4J_PASS]
[--clean-neo4j] [--debug]

options:
-h, --help show this help message and exit
--redis-host REDIS_HOST
Redis host, default: localhost
--redis-port REDIS_PORT
Redis port, default: 6379
--clean-redis, -cr Whether to clean cache in the redis, default: False
--neo4j-uri NEO4J_URI
Neo4j URI endpoint, default: neo4j://localhost:7687
--neo4j-user NEO4J_USER
Neo4j username, default: neo4j
--neo4j-pass NEO4J_PASS
Neo4j password, default: 123456789
--clean-neo4j, -cn Whether to clean cache, and index f rom scratch, default: False
--debug Whether to print debug statements, default: False

Report

usage: raven report [-h] [--redis-host REDIS_HOST] [--redis-port REDIS_PORT] [--clean-redis] [--neo4j-uri NEO4J_URI]
[--neo4j-user NEO4J_USER] [--neo4j-pass NEO4J_PASS] [--clean-neo4j]
[--tag {injection,unauthenticated,fixed,priv-esc,supply-chain}]
[--severity {info,low,medium,high,critical}] [--queries-path QUERIES_PATH] [--format {raw,json}]
{slack} ...

positional arguments:
{slack}
slack Send report to slack channel

options:
-h, --help show this help message and exit
--redis-host REDIS_HOST
Redis host, default: localhost
--redis-port REDIS_PORT
Redis port, default: 6379
--clean-redis, -cr Whether to clean cache in the redis, default: False
--neo4j-uri NEO4J_URI
Neo4j URI endpoint, default: neo4j://localhost:7687
--neo4j-user NEO4J_USER
Neo4j username, default: neo4j
--neo4j-pass NEO4J_PASS
Neo4j password, default: 123456789
--clean-neo4j, -cn Whether to clean cache, and index from scratch, default: False
--tag {injection,unauthenticated,fixed,priv-esc,supply-chain}, -t {injection,unauthenticated,fixed,priv-esc,supply-chain}
Filter queries with specific tag
--severity {info,low,medium,high,critical}, -s {info,low,medium,high,critical}
Filter queries by severity level (default: info)
--queries-path QUERIES_PATH, -dp QUERIES_PATH
Queries folder (default: library)
--format {raw,json}, -f {raw,json}
Report format (default: raw)

Examples

Retrieve all workflows and actions associated with the organization.

raven download org --token $GITHUB_TOKEN --org-name microsoft --org-name google --debug

Scrape all publicly accessible GitHub repositories.

raven download crawl --token $GITHUB_TOKEN --min-stars 100 --max-stars 1000 --debug

After finishing the download process or if interrupted using Ctrl+C, proceed to index all workflows and actions into the Neo4j database.

raven index --debug

Now, we can generate a report using our query library.

raven report --severity high --tag injection --tag unauthenticated

Rate Limiting

For effective rate limiting, you should supply a Github token. For authenticated users, the next rate limiting applies:

  • Code search - 30 queries per minute
  • Any other API - 5000 per hour

Research Knowledge Base

Current Limitations

  • It is possible to run external action by referencing a folder with a Dockerfile (without action.yml). Currently, this behavior isn't supported.
  • It is possible to run external action by referencing a docker container through the docker://... URL. Currently, this behavior isn't supported.
  • It is possible to run an action by referencing it locally. This creates complex behavior, as it may come from a different repository that was checked out previously. The current behavior is trying to find it in the existing repository.
  • We aren't modeling the entire workflow structure. If additional fields are needed, please submit a pull request according to the contribution guidelines.

Future Research Work

  • Implementation of taint analysis. Example use case - a user can pass a pull request title (which is controllable parameter) to an action parameter that is named data. That action parameter may be used in a run command: - run: echo ${{ inputs.data }}, which creates a path for a code execution.
  • Expand the research for findings of harmful misuse of GITHUB_ENV. This may utilize the previous taint analysis as well.
  • Research whether actions/github-script has an interesting threat landscape. If it is, it can be modeled in the graph.

Want more of CI/CD Security, AppSec, and ASPM? Check out Cycode

If you liked Raven, you would probably love our Cycode platform that offers even more enhanced capabilities for visibility, prioritization, and remediation of vulnerabilities across the software delivery.

If you are interested in a robust, research-driven Pipeline Security, Application Security, or ASPM solution, don't hesitate to get in touch with us or request a demo using the form https://cycode.com/book-a-demo/.


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ADCSync - Use ESC1 To Perform A Makeshift DCSync And Dump Hashes


This is a tool I whipped up together quickly to DCSync utilizing ESC1. It is quite slow but otherwise an effective means of performing a makeshift DCSync attack without utilizing DRSUAPI or Volume Shadow Copy.


This is the first version of the tool and essentially just automates the process of running Certipy against every user in a domain. It still needs a lot of work and I plan on adding more features in the future for authentication methods and automating the process of finding a vulnerable template.

python3 adcsync.py -u clu -p theperfectsystem -ca THEGRID-KFLYNN-DC-CA -template SmartCard -target-ip 192.168.0.98 -dc-ip 192.168.0.98 -f users.json -o ntlm_dump.txt

___ ____ ___________
/ | / __ \/ ____/ ___/__ ______ _____
/ /| | / / / / / \__ \/ / / / __ \/ ___/
/ ___ |/ /_/ / /___ ___/ / /_/ / / / / /__
/_/ |_/_____/\____//____/\__, /_/ /_/\___/
/____/

Grabbing user certs:
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 105/105 [02:18<00:00, 1.32s/it]
THEGRID.LOCAL/shirlee.saraann::aad3b435b51404eeaad3b435b51404ee:68832255545152d843216ed7bbb2d09e:::
THEGRID.LOCAL/rosanne.nert::aad3b435b51404eeaad3b435b51404ee:a20821df366981f7110c07c7708f7ed2:::
THEGRID.LOCAL/edita.lauree::aad3b435b51404eeaad3b435b51404ee:b212294e06a0757547d66b78bb632d69:::
THEGRID.LOCAL/carol.elianore::aad3b435b51404eeaad3b435b51404ee:ed4603ce5a1c86b977dc049a77d2cc6f:::
THEGRID.LOCAL/astrid.lotte::aad3b435b51404eeaad3b435b51404ee:201789a1986f2a2894f7ac726ea12a0b:::
THEGRID.LOCAL/louise.hedvig::aad3b435b51404eeaad3b435b51404ee:edc599314b95cf5635eb132a1cb5f04d:::
THEGRID.LO CAL/janelle.jess::aad3b435b51404eeaad3b435b51404ee:a7a1d8ae1867bb60d23e0b88342a6fab:::
THEGRID.LOCAL/marie-ann.kayle::aad3b435b51404eeaad3b435b51404ee:a55d86c4b2c2b2ae526a14e7e2cd259f:::
THEGRID.LOCAL/jeanie.isa::aad3b435b51404eeaad3b435b51404ee:61f8c2bf0dc57933a578aa2bc835f2e5:::

Introduction

ADCSync uses the ESC1 exploit to dump NTLM hashes from user accounts in an Active Directory environment. The tool will first grab every user and domain in the Bloodhound dump file passed in. Then it will use Certipy to make a request for each user and store their PFX file in the certificate directory. Finally, it will use Certipy to authenticate with the certificate and retrieve the NT hash for each user. This process is quite slow and can take a while to complete but offers an alternative way to dump NTLM hashes.

Installation

git clone https://github.com/JPG0mez/adcsync.git
cd adcsync
pip3 install -r requirements.txt

Usage

To use this tool we need the following things:

  1. Valid Domain Credentials
  2. A user list from a bloodhound dump that will be passed in.
  3. A template vulnerable to ESC1 (Found with Certipy find)
# python3 adcsync.py --help
___ ____ ___________
/ | / __ \/ ____/ ___/__ ______ _____
/ /| | / / / / / \__ \/ / / / __ \/ ___/
/ ___ |/ /_/ / /___ ___/ / /_/ / / / / /__
/_/ |_/_____/\____//____/\__, /_/ /_/\___/
/____/

Usage: adcsync.py [OPTIONS]

Options:
-f, --file TEXT Input User List JSON file from Bloodhound [required]
-o, --output TEXT NTLM Hash Output file [required]
-ca TEXT Certificate Authority [required]
-dc-ip TEXT IP Address of Domain Controller [required]
-u, --user TEXT Username [required]
-p, --password TEXT Password [required]
-template TEXT Template Name vulnerable to ESC1 [required]
-target-ip TEXT IP Address of th e target machine [required]
--help Show this message and exit.

TODO

  • Support alternative authentication methods such as NTLM hashes and ccache files
  • Automatically run "certipy find" to find and grab templates vulnerable to ESC1
  • Add jitter and sleep options to avoid detection
  • Add type validation for all variables

Acknowledgements

  • puzzlepeaches: Telling me to hurry up and write this
  • ly4k: For Certipy
  • WazeHell: For the script to set up the vulnerable AD environment used for testing

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FalconHound - A Blue Team Multi-Tool. It Allows You To Utilize And Enhance The Power Of BloodHound In A More Automated Fashion


FalconHound is a blue team multi-tool. It allows you to utilize and enhance the power of BloodHound in a more automated fashion. It is designed to be used in conjunction with a SIEM or other log aggregation tool.

One of the challenging aspects of BloodHound is that it is a snapshot in time. FalconHound includes functionality that can be used to keep a graph of your environment up-to-date. This allows you to see your environment as it is NOW. This is especially useful for environments that are constantly changing.

One of the hardest releationships to gather for BloodHound is the local group memberships and the session information. As blue teamers we have this information readily available in our logs. FalconHound can be used to gather this information and add it to the graph, allowing it to be used by BloodHound.

This is just an example of how FalconHound can be used. It can be used to gather any information that you have in your logs or security tools and add it to the BloodHound graph.

Additionally, the graph can be used to trigger alerts or generate enrichment lists. For example, if a user is added to a certain group, FalconHound can be used to query the graph database for the shortest path to a sensitive or high-privilege group. If there is a path, this can be logged to the SIEM or used to trigger an alert.

Other examples where FalconHound can be used:

  • Adding, removing or timing out sessions in the graph, based on logon and logoff events.
  • Marking users and computers as compromised in the graph when they have an incident in Sentinel or MDE.
  • Adding CVE information and whether there is a public exploit available to the graph.
  • All kinds of Azure activities.
  • Recalculating the shortest path to sensitive groups when a user is added to a group or has a new role.
  • Adding new users, groups and computers to the graph.
  • Generating enrichment lists for Sentinel and Splunk of, for example, Kerberoastable users or users with ownerships of certain entities.

The possibilities are endless here. Please add more ideas to the issue tracker or submit a PR.

A blog detailing more on why we developed it and some use case examples can be found here

Index:

Supported data sources and targets

FalconHound is designed to be used with BloodHound. It is not a replacement for BloodHound. It is designed to leverage the power of BloodHound and all other data platforms it supports in an automated fashion.

Currently, FalconHound supports the following data sources and or targets:

  • Azure Sentinel
  • Azure Sentinel Watchlists
  • Splunk
  • Microsoft Defender for Endpoint
  • Neo4j
  • MS Graph API (early stage)
  • CSV files

Additional data sources and targets are planned for the future.

At this moment, FalconHound only supports the Neo4j database for BloodHound. Support for the API of BH CE and BHE is under active development.


Installation

Since FalconHound is written in Go, there is no installation required. Just download the binary from the release section and run it. There are compiled binaries available for Windows, Linux and MacOS. You can find them in the releases section.

Before you can run it, you need to create a config file. You can find an example config file in the root folder. Instructions on how to creat all crededentials can be found here.

The recommened way to run FalconHound is to run it as a scheduled task or cron job. This will allow you to run it on a regular basis and keep your graph, alerts and enrichments up-to-date.

Requirements

  • BloodHound, or at least the Neo4j database for now.
  • A SIEM or other log aggregation tool. Currently, Azure Sentinel and Splunk are supported.
  • Credentials for each endpoint you want to talk to, with the required permissions.

Configuration

FalconHound is configured using a YAML file. You can find an example config file in the root folder. Each section of the config file is explained below.


Usage

Default run

To run FalconHound, just run the binary and add the -go parameter to have it run all queries in the actions folder.

./falconhound -go

List all enabled actions

To list all enabled actions, use the -actionlist parameter. This will list all actions that are enabled in the config files in the actions folder. This should be used in combination with the -go parameter.

./falconhound -actionlist -go

Run with a select set of actions

To run a select set of actions, use the -ids parameter, followed by one or a list of comma-separated action IDs. This will run the actions that are specified in the parameter, which can be very handy when testing, troubleshooting or when you require specific, more frequent updates. This should be used in combination with the -go parameter.

./falconhound -ids action1,action2,action3 -go

Run with a different config file

By default, FalconHound will look for a config file in the current directory. You can also specify a config file using the -config flag. This can allow you to run multiple instances of FalconHound with different configurations, against different environments.

./falconhound -go -config /path/to/config.yml

Run with a different actions folder

By default, FalconHound will look for the actions folder in the current directory. You can also specify a different folder using the -actions-dir flag. This makes testing and troubleshooting easier, but also allows you to run multiple instances of FalconHound with different configurations, against different environments, or at different time intervals.

./falconhound -go -actions-dir /path/to/actions

Run with credentials from a keyvault

By default, FalconHound will use the credentials in the config.yml (or a custom loaded one). By setting the -keyvault flag FalconHound will get the keyvault from the config and retrieve all secrets from there. Should there be items missing in the keyvault it will fall back to the config file.

./falconhound -go -keyvault

Actions

Actions are the core of FalconHound. They are the queries that FalconHound will run. They are written in the native language of the source and target and are stored in the actions folder. Each action is a separate file and is stored in the directory of the source of the information, the query target. The filename is used as the name of the action.

Action folder structure

The action folder is divided into sub-directories per query source. All folders will be processed recursively and all YAML files will be executed in alphabetical order.

The Neo4j actions should be processed last, since their output relies on other data sources to have updated the graph database first, to get the most up-to-date results.

Action files

All files are YAML files. The YAML file contains the query, some metadata and the target(s) of the queried information.

There is a template file available in the root folder. You can use this to create your own actions. Have a look at the actions in the actions folder for more examples.

While most items will be fairly self explanatory,there are some important things to note about actions:

Enabled

As the name implies, this is used to enable or disable an action. If this is set to false, the action will not be run.

Enabled: true

Debug

This is used to enable or disable debug mode for an action. If this is set to true, the action will be run in debug mode. This will output the results of the query to the console. This is useful for testing and troubleshooting, but is not recommended to be used in production. It will slow down the processing of the action depending on the number of results.

Debug: false

Query

The Query field is the query that will be run against the source. This can be a KQL query, a SPL query or a Cypher query depending on your SourcePlatform. IMPORTANT: Try to keep the query as exact as possible and only return the fields that you need. This will make the processing of the results faster and more efficient.

Additionally, when running Cypher queries, make sure to RETURN a JSON object as the result, otherwise processing will fail. For example, this will return the Name, Count, Role and Owners of the Azure Subscriptions:

MATCH p = (n)-[r:AZOwns|AZUserAccessAdministrator]->(g:AZSubscription) 
RETURN {Name:g.name , Count:COUNT(g.name), Role:type(r), Owners:COLLECT(n.name)}

Targets

Each target has several options that can be configured. Depending on the target, some might require more configuration than others. All targets have the Name and Enabled fields. The Name field is used to identify the target. The Enabled field is used to enable or disable the target. If this is set to false, the target will be ignored.

CSV

  - Name: CSV
Enabled: true
Path: path/to/filename.csv

Neo4j

The Neo4j target will write the results of the query to a Neo4j database. This output is per line and therefore it requires some additional configuration. Since we can transfer all sorts of data in all directions, FalconHound needs to understand what to do with the data. This is done by using replacement variables in the first line of your Cypher queries. These are passed to Neo4j as parameters and can be used in the query. The ReplacementFields fields are configured below.

  - Name: Neo4j
Enabled: true
Query: |
MATCH (x:Computer {name:$Computer}) MATCH (y:User {objectid:$TargetUserSid}) MERGE (x)-[r:HasSession]->(y) SET r.since=$Timestamp SET r.source='falconhound'
Parameters:
Computer: Computer
TargetUserSid: TargetUserSid
Timestamp: Timestamp

The Parameters section defines a set of parameters that will be replaced by the values from the query results. These can be referenced as Neo4j parameters using the $parameter_name syntax.

Sentinel

The Sentinel target will write the results of the query to a Sentinel table. The table will be created if it does not exist. The table will be created in the workspace that is specified in the config file. The data from the query will be added to the EventData field. The EventID will be the action ID and the Description will be the action name.

This is why also query output needs to be controlled, you might otherwise flood your target.

  - Name: Sentinel
Enabled: true

Sentinel Watchlists

The Sentinel Watchlists target will write the results of the query to a Sentinel watchlist. The watchlist will be created if it does not exist. The watchlist will be created in the workspace that is specified in the config file. All columns returned by the query will be added to the watchlist.

 - Name: Watchlist
Enabled: true
WatchlistName: FH_MDE_Exploitable_Machines
DisplayName: MDE Exploitable Machines
SearchKey: DeviceName
Overwrite: true

The WatchlistName field is the name of the watchlist. The DisplayName field is the display name of the watchlist.

The SearchKey field is the column that will be used as the search key.

The Overwrite field is used to determine if the watchlist should be overwritten or appended to. If this is set to false, the results of the query will be appended to the watchlist. If this is set to true, the watchlist will be deleted and recreated with the results of the query.

Splunk

Like Sentinel, Splunk will write the results of the query to a Splunk index. The index will need to be created and tied to a HEC endpoint. The data from the query will be added to the EventData field. The EventID will be the action ID and the Description will be the action name.

  - Name: Splunk
Enabled: true

Azure Data Explorer

Like Sentinel, Splunk will write the results of the query to a ADX table. The data from the query will be added to the EventData field. The EventID will be the action ID and the Description will be the action name.

  - Name: ADX
Enabled: true
Table: "name"

Extensions to the graph

Relationship: HadSession

Once a session has ended, it had to be removed from the graph, but this felt like a waste of information. So instead of removing the session,it will be added as a relationship between the computer and the user. The relationship will be called HadSession. The relationship will have the following properties:

{
"till": "2021-08-31T14:00:00Z",
"source": "falconhound",
"reason": "logoff",
}

This allows for additional path discoveries where we can investigate whether the user ever logged on to a certain system, even if the session has ended.

Properties

FalconHound will add the following properties to nodes in the graph:

Computer: - 'exploitable': true/false - 'exploits': list of CVEs - 'exposed': true/false - 'ports': list of ports accessible from the internet - 'alertids': list of alert ids

Credential management

The currently supported ways of providing FalconHound with credentials are:

  • Via the config.yml file on disk.
  • Keyvault secrets. This still requires a ServicePrincipal with secrets in the yaml.
  • Mixed mode.

Config.yml

The config file holds all details required by each platform. All items in the config file are case-sensitive. Best practise is to separate the apps on a per service level but you can use 1 AppID/AppSecret for all Azure based actions.

The required permissions for your AppID/AppSecret are listed here.

Keyvault

A more secure way of storing the credentials would be to use an Azure KeyVault. Be aware that there is a small cost aspect to using Keyvaults. Access to KeyVaults currently only supports authentication based on a AppID/AppSecret which needs to be configured in the config.yml file.

The recommended way to set this up is to use a ServicePrincipal that only has the Key Vault Secrets User role to this Keyvault. This role only allows access to the secrets, not even list them. Do NOT reuse the ServicePrincipal which has access to Sentinel and/or MDE, since this almost completely negates the use of a Keyvault.

The items to configure in the Keyvault are listed below. Please note Keyvault secrets are not case-sensitive.

SentinelAppSecret
SentinelAppID
SentinelTenantID
SentinelTargetTable
SentinelResourceGroup
SentinelSharedKey
SentinelSubscriptionID
SentinelWorkspaceID
SentinelWorkspaceName
MDETenantID
MDEAppID
MDEAppSecret
Neo4jUri
Neo4jUsername
Neo4jPassword
GraphTenantID
GraphAppID
GraphAppSecret
AdxTenantID
AdxAppID
AdxAppSecret
AdxClusterURL
AdxDatabase
SplunkUrl
SplunkApiToken
SplunkIndex
SplunkApiPort
SplunkHecToken
SplunkHecPort
BHUrl
BHTokenID
BHTokenKey
LogScaleUrl
LogScaleToken
LogScaleRepository

Once configured you can add the -keyvault parameter while starting FalconHound.

Mixed mode / fallback

When the -keyvault parameter is set on the command-line, this will be the primary source for all required secrets. Should FalconHound fail to retrieve items, it will fall back to the equivalent item in the config.yml. If both fail and there are actions enabled for that source or target, it will throw errors on attempts to authenticate.

Deployment

FalconHound is designed to be run as a scheduled task or cron job. This will allow you to run it on a regular basis and keep your graph, alerts and enrichments up-to-date. Depending on the amount of actions you have enabled, the amount of data you are processing and the amount of data you are writing to the graph, this can take a while.

All log based queries are built to run every 15 minutes. Should processing take too long you might need to tweak this a little. If this is the case it might be recommended to disable certain actions.

Also there might be some overlap with for instance the session actions. If you have a lot of sessions you might want to disable the session actions for Sentinel and rely on the one from MDE. This is assuming you have MDE and Sentinel connected and most machines are onboarded into MDE.

Sharphound / Azurehound

While FalconHound is designed to be used with BloodHound, it is not a replacement for Sharphound and Azurehound. It is designed to compliment the collection and remove the moment-in-time problem of the peroiodic collection. Both Sharphound and Azurehound are still required to collect the data, since not all similar data is available in logs.

It is recommended to run Sharphound and Azurehound on a regular basis, for example once a day/week or month, and FalconHound every 15 minutes.

License

This project is licensed under the BSD3 License - see the LICENSE file for details.

This means you can use this software for free, even in commercial products, as long as you credit us for it. You cannot hold us liable for any damages caused by this software.


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