The U.S. government today unsealed criminal charges against 16 individuals accused of operating and selling DanaBot, a prolific strain of information-stealing malware that has been sold on Russian cybercrime forums since 2018. The FBI says a newer version of DanaBot was used for espionage, and that many of the defendants exposed their real-life identities after accidentally infecting their own systems with the malware.
DanaBotβs features, as promoted on its support site. Image: welivesecurity.com.
Initially spotted in May 2018 by researchers at the email security firm Proofpoint, DanaBot is a malware-as-a-service platform that specializes in credential theft and banking fraud.
Today, the U.S. Department of Justice unsealed a criminal complaint and indictment from 2022, which said the FBI identified at least 40 affiliates who were paying between $3,000 and $4,000 a month for access to the information stealer platform.
The government says the malware infected more than 300,000 systems globally, causing estimated losses of more than $50 million. The ringleaders of the DanaBot conspiracy are named as Aleksandr Stepanov, 39, a.k.a. βJimmBee,β and Artem Aleksandrovich Kalinkin, 34, a.k.a. βOnixβ, both of Novosibirsk, Russia. Kalinkin is an IT engineer for the Russian state-owned energy giant Gazprom. His Facebook profile name is βMaffiozi.β
According to the FBI, there were at least two major versions of DanaBot; the first was sold between 2018 and June 2020, when the malware stopped being offered on Russian cybercrime forums. The government alleges that the second version of DanaBot β emerging in January 2021 β was provided to co-conspirators for use in targeting military, diplomatic and non-governmental organization computers in several countries, including the United States, Belarus, the United Kingdom, Germany, and Russia.
βUnindicted co-conspirators would use the Espionage Variant to compromise computers around the world and steal sensitive diplomatic communications, credentials, and other data from these targeted victims,β reads a grand jury indictment dated Sept. 20, 2022. βThis stolen data included financial transactions by diplomatic staff, correspondence concerning day-to-day diplomatic activity, as well as summaries of a particular countryβs interactions with the United States.β
The indictment says the FBI in 2022 seized servers used by the DanaBot authors to control their malware, as well as the servers that stored stolen victim data. The government said the server data also show numerous instances in which the DanaBot defendants infected their own PCs, resulting in their credential data being uploaded to stolen data repositories that were seized by the feds.
βIn some cases, such self-infections appeared to be deliberately done in order to test, analyze, or improve the malware,β the criminal complaint reads. βIn other cases, the infections seemed to be inadvertent β one of the hazards of committing cybercrime is that criminals will sometimes infect themselves with their own malware by mistake.β
Image: welivesecurity.com
A statement from the DOJ says that as part of todayβs operation, agents with the Defense Criminal Investigative Service (DCIS) seized the DanaBot control servers, including dozens of virtual servers hosted in the United States. The government says it is now working with industry partners to notify DanaBot victims and help remediate infections. The statement credits a number of security firms with providing assistance to the government, including ESET, Flashpoint, Google, Intel 471, Lumen, PayPal, Proofpoint, Team CYMRU, and ZScaler.
Itβs not unheard of for financially-oriented malicious software to be repurposed for espionage. A variant of the ZeuS Trojan, which was used in countless online banking attacks against companies in the United States and Europe between 2007 and at least 2015, was for a time diverted to espionage tasks by its author.
As detailed in this 2015 story, the author of the ZeuS trojan created a custom version of the malware to serve purely as a spying machine, which scoured infected systems in Ukraine for specific keywords in emails and documents that would likely only be found in classified documents.
The public charging of the 16 DanaBot defendants comes a day after Microsoft joined a slew of tech companies in disrupting the IT infrastructure for another malware-as-a-service offering β Lumma Stealer, which is likewise offered to affiliates under tiered subscription prices ranging from $250 to $1,000 per month. Separately, Microsoft filed a civil lawsuit to seize control over 2,300 domain names used by Lumma Stealer and its affiliates.
Further reading:
Danabot: Analyzing a Fallen Empire
ZScaler blog: DanaBot Launches DDoS Attack Against the Ukrainian Ministry of Defense
Flashpoint: Operation Endgame DanaBot Malware
Team CYMRU: Inside DanaBotβs Infrastructure: In Support of Operation Endgame II
March 2022 criminal complaint v. Artem Aleksandrovich Kalinkin
September 2022 grand jury indictment naming the 16 defendants
Clone the repository: bash git clone https://github.com/ALW1EZ/PANO.git cd PANO
Run the application:
./start_pano.sh
start_pano.bat
The startup script will automatically: - Check for updates - Set up the Python environment - Install dependencies - Launch PANO
In order to use Email Lookup transform You need to login with GHunt first. After starting the pano via starter scripts;
source venv/bin/activate
call venv\Scripts\activate
Visual node and edge styling
Timeline Analysis
Temporal relationship analysis
Map Integration
Connected services discovery
Username Analysis
Web presence analysis
Image Analysis
Entities are the fundamental building blocks of PANO. They represent distinct pieces of information that can be connected and analyzed:
π Text: Generic text content
Properties System
Transforms are automated operations that process entities to discover new information and relationships:
π Enrichment: Add data to existing entities
Features
Helpers are specialized tools with dedicated UIs for specific investigation tasks:
π Translator: Translate text between languages
Helper Features
We welcome contributions! To contribute to PANO:
Note: We use a single
main
branch for development. All pull requests should be made directly tomain
.
from dataclasses import dataclass
from typing import ClassVar, Dict, Any
from .base import Entity
@dataclass
class PhoneNumber(Entity):
name: ClassVar[str] = "Phone Number"
description: ClassVar[str] = "A phone number entity with country code and validation"
def init_properties(self):
"""Initialize phone number properties"""
self.setup_properties({
"number": str,
"country_code": str,
"carrier": str,
"type": str, # mobile, landline, etc.
"verified": bool
})
def update_label(self):
"""Update the display label"""
self.label = self.format_label(["country_code", "number"])
### Custom Transforms Transforms are operations that process entities and generate new insights or relationships. To create a custom transform: 1. Create a new file in the `transforms` folder (e.g., `transforms/phone_lookup.py`) 2. Implement your transform class: from dataclasses import dataclass
from typing import ClassVar, List
from .base import Transform
from entities.base import Entity
from entities.phone_number import PhoneNumber
from entities.location import Location
from ui.managers.status_manager import StatusManager
@dataclass
class PhoneLookup(Transform):
name: ClassVar[str] = "Phone Number Lookup"
description: ClassVar[str] = "Lookup phone number details and location"
input_types: ClassVar[List[str]] = ["PhoneNumber"]
output_types: ClassVar[List[str]] = ["Location"]
async def run(self, entity: PhoneNumber, graph) -> List[Entity]:
if not isinstance(entity, PhoneNumber):
return []
status = StatusManager.get()
operation_id = status.start_loading("Phone Lookup")
try:
# Your phone number lookup logic here
# Example: query an API for phone number details
location = Location(properties={
"country": "Example Country",
"region": "Example Region",
"carrier": "Example Carrier",
"source": "PhoneLookup transform"
})
return [location]
except Exception as e:
status.set_text(f"Error during phone lookup: {str(e)}")
return []
finally:
status.stop_loading(operation_id)
### Custom Helpers Helpers are specialized tools that provide additional investigation capabilities through a dedicated UI interface. To create a custom helper: 1. Create a new file in the `helpers` folder (e.g., `helpers/data_analyzer.py`) 2. Implement your helper class: from PySide6.QtWidgets import (
QWidget, QVBoxLayout, QHBoxLayout, QPushButton,
QTextEdit, QLabel, QComboBox
)
from .base import BaseHelper
from qasync import asyncSlot
class DummyHelper(BaseHelper):
"""A dummy helper for testing"""
name = "Dummy Helper"
description = "A dummy helper for testing"
def setup_ui(self):
"""Initialize the helper's user interface"""
# Create input text area
self.input_label = QLabel("Input:")
self.input_text = QTextEdit()
self.input_text.setPlaceholderText("Enter text to process...")
self.input_text.setMinimumHeight(100)
# Create operation selector
operation_layout = QHBoxLayout()
self.operation_label = QLabel("Operation:")
self.operation_combo = QComboBox()
self.operation_combo.addItems(["Uppercase", "Lowercase", "Title Case"])
operation_layout.addWidget(self.operation_label)
operation_layout.addWidget(self.operation_combo)
# Create process button
self.process_btn = QPushButton("Process")
self.process_btn.clicked.connect(self.process_text)
# Create output text area
self.output_label = QLabel("Output:")
self.output_text = QTextEdit()
self.output_text.setReadOnly(True)
self.output_text.setMinimumHeight(100)
# Add widgets to main layout
self.main_layout.addWidget(self.input_label)
self.main_layout.addWidget(self.input_text)
self.main_layout.addLayout(operation_layout)
self.main_layout.addWidget(self.process_btn)
self.main_layout.addWidget(self.output_label)
self.main_layout.addWidget(self.output_text)
# Set dialog size
self.resize(400, 500)
@asyncSlot()
async def process_text(self):
"""Process the input text based on selected operation"""
text = self.input_text.toPlainText()
operation = self.operation_combo.currentText()
if operation == "Uppercase":
result = text.upper()
elif operation == "Lowercase":
result = text.lower()
else: # Title Case
result = text.title()
self.output_text.setPlainText(result)
This project is licensed under the Creative Commons Attribution-NonCommercial (CC BY-NC) License.
You are free to: - β Share: Copy and redistribute the material - β Adapt: Remix, transform, and build upon the material
Under these terms: - βΉοΈ Attribution: You must give appropriate credit - π« NonCommercial: No commercial use - π No additional restrictions
Special thanks to all library authors and contributors who made this project possible.
Created by ALW1EZ with AI β€οΈ