Doppelgänger Info Operations Target Europe & US

Mid-year Doppelgänger Information Operations in Europe and the US

HarfangLab Webinar – Security at 300km/h: How Fragmented Endpoint Strategies Derail Attack Surface Management?

Published on 25 July, 2024 • 54min Read

Introduction

Digital disinformation has taken on new and sophisticated forms over the past decade. One of the more concerning phenomena is the Doppelgänger information operation—a coordinated, state-sponsored campaign leveraging fake news websites, social media bot networks, and intricate redirection chains to manipulate public opinion. This post is based on the HarfangLab Webinar in collaboration with Forrester, which examined these operations in detail. In it, we review the background, technical details, real-world examples, mitigation strategies, and even include sample code to help cybersecurity professionals better understand and approach this threat.

Whether you are a beginner or an experienced threat intelligence analyst, this long-form technical post will guide you step-by-step from the basics of Doppelgänger operations to advanced cybersecurity practices involving endpoint protection, redirection chain analysis, and Attack Surface Management (ASM).

Background and Overview
- What Are Doppelgänger Information Operations?
- Historical Context and Current Trends
Dissection of the (Dis)information Chain
- First-level Redirectors
- Second-level Redirectors
Social Media and Bot Networks
- Role of X/Twitter in Dissemination
- Anatomy of a Bot Post
Technical Deep Dive: Infrastructure and Tactics
- Domain Patterns and Registration Trends
- Redirection Chain Analysis
Impact on Endpoint and Attack Surface Management
- Fragmented Endpoint Strategies
- Tools and Methodologies for ASM
Code Samples and Practical Analysis
- Scanning Commands with Bash
- Parsing Output with Python
Mitigation Strategies and Recommendations
- Best Practices for Threat Intelligence
- Role of AI and Behavioral Engines
Case Studies and Real-World Examples
Conclusion
References

Background and Overview

What Are Doppelgänger Information Operations?

Doppelgänger operations refer to coordinated efforts—attributed to Russian actors—to manipulate public opinion by impersonating legitimate news sources. Named for the twin-like imitation of real entities, these operations use the following tactics:

Fake or Manipulated Websites: Impersonating popular news domains.
Social Networks: Dissemination across platforms like X/Twitter.
Redirection Chains: Obfuscating the origin with multiple layers of redirects.
Bot Networks: Use of automated accounts to amplify content.

This multifaceted approach enables operators to obscure their infrastructure, making detection and timely counteraction challenging.

Historical Context and Current Trends

Historically, information operations were simple “fake news” efforts during election cycles. However, as digital infrastructures and endpoint technologies evolved, so did disinformation operations. Key trends include:

Integration of AI: For both generating content (AI-generated music videos, fake news stories) and automating bot activities.
Sophisticated Redirections: Multi-level chaining designed to thwart real-time detection.
Infrastructure Rotation: Rapidly changing domains, IP addresses, and top-level domains (TLDs) to avoid blacklisting.
Fragmented Endpoint Strategies: Enterprises often use disparate endpoint protection solutions that create vulnerabilities in Attack Surface Management (ASM).

Recent events, such as France’s unexpected snap general election in June 2024, have brought these operations into sharp focus. The operation known as “Mid-year Doppelgänger” highlights the need for comprehensive threat intelligence and improved endpoint management practices.

Dissection of the (Dis)information Chain

Understanding the underlying technical structure is essential for countering these operations. Doppelgänger campaigns employ an intricate redirection chain to mask their activities.

First-level Redirectors

The first step in the chain involves URLs that are designed to:

Appear innocuous to regular users.
Generate link previews on social media platforms such as X/Twitter using metadata.
Immediately redirect visitors to another URL.

Example Analysis:
Below is an excerpt from a first-level redirector web page:

<!DOCTYPE html>
<html>
  <head>
    <title>Citizenship Doesn't Matter If You Support Biden</title>
    <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
    <meta name="twitter:card" content="summary_large_image">
    <meta property="og:title" content="Citizenship Doesn't Matter If You Support Biden"/>
    <meta property="twitter:title" content="Citizenship Doesn't Matter If You Support Biden"/>
    <meta property="og:description" content="Republicans are trying to rush a bill through Congress to allow only U.S. citizens to vote in presidential elections."/>
    <meta property="twitter:description" content="Republicans are trying to rush a bill through Congress to allow only U.S. citizens to vote in presidential elections."/>
    <meta property="og:image" content="https://telegra.ph/file/d1629e477f84abbc37dbc.jpg">
    <meta property="twitter:image" content="https://telegra.ph/file/d1629e477f84abbc37dbc.jpg">
    <meta http-equiv='refresh' content='0; url=http://vickypitner.com/wash9261378'>
  </head>
  <body>
    <script type="text/javascript">
      var _0xc80e=["","split", ...];
      document.body.style.color = "white";
    </script>
    <div>
      пример текста на кириллице – placeholder text that is completely unrelated.
    </div>
  </body>
</html>

This page illustrates the following:

Metadata Manipulation: Tailored for social media preview generation.
Redirection: Instant redirection to the second-level redirector URL.
Obfuscated Code: Minimal JavaScript to obscure the actual content.
Placeholders: Irrelevant Cyrillic text embedded as a smokescreen.

Second-level Redirectors

The second-level redirectors continue the chain, ensuring that the final landing page is further hidden behind layers of obfuscation. For example, a second-level redirector might use different HTTP headers and HTML layouts while redirecting to content that is even less traceable.

Example Analysis – Excerpt:

<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta http-equiv="X-UA-Compatible" content="IE=edge" />
    <meta name="robots" content="noindex, nofollow">
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>Redirecting...</title>
  </head>
  <body>
    <noscript>Please enable JavaScript to view our website.</noscript>
    <script>
      window.location.href = "http://finalcontent.example.com";
    </script>
  </body>
</html>

Key observations:

The use of the noindex, nofollow attribute prevents search engines from crawling the page.
The streamlined design and absence of engaging content further emphasize that its role is purely transitional.

Social networks, particularly X/Twitter, play a critical role in disseminating disinformation. Research has identified approximately 800 bot accounts actively engaged in sharing links to first-level redirectors.

Role of X/Twitter in Dissemination

X/Twitter serves two primary functions in these operations:

Amplification of Disinformation: Automated accounts post links that lead users into the redirection chain.
Artificial Engagement: Bots generate high engagement metrics (likes, retweets, shares) that may simulate organic popularity, thus misleading observers and influencing algorithms.

Anatomy of a Bot Post

Bot posts share several characteristics:

Unique, Possibly AI-generated Content: Each post tends to have a custom message, avoiding verbatim repetition.
Multiple Languages: Posts include languages such as English, French, German, Polish, and Ukrainian.
Misaligned Engagements: Engagement metrics vastly exceed what would be expected from their follower counts.
Historical Ties: Some of these bot accounts previously engaged in cryptocurrency scams, implying a possible overlap between cybercriminal circles and state-sponsored operations.

Real-world Example:
A monitored bot account once posted an AI-generated music video impersonating the band Little Big. The video made satirical remarks about the Paris Olympics and subtly discouraged attendance, highlighting the blend of political commentary with cultural manipulation.

Technical Deep Dive: Infrastructure and Tactics

Understanding the technical infrastructure is crucial for any security professional tasked with defending against such information operations.

Domain Patterns and Registration Trends

The observed infrastructure uses a pattern of randomized subdomains combined with recent TLDs such as .click, .top, or .shop. For example:

URL Pattern 1:
http(s)://<5 to 6 random characters>.<domain name.tld>/<6 random characters>
URL Pattern 2:
http(s)://<short domain name.tld>/<6 random characters>

The domains are often hosted on IP addresses with known associations, such as:

168.100.9.238 – ASN 399629, BLNWX
77.105.135.48 – ASN 216309, EVILEMPIRE-AS / TNSECURITY LTD
185.172.128.161 – ASN 216309, EVILEMPIRE-AS / TNSECURITY LTD

Furthermore, web servers on these IPs typically run:

OpenSSH on Port 22.
OpenResty and PHP 7 on Ports 80 and 443.
Self-signed certificates with generic issuer information.

Redirection Chain Analysis

The redirection chain is engineered to delay and complicate the identification of the final hostile payload:

Initial Click: The user clicks on the seemingly benign link on social media.
First Redirection: The link points to a first-level redirector that handles metadata for social sharing and immediately issues a meta-refresh to the next stage.
Secondary Redirection: The second-level redirector, hosted on a different infrastructure, then finally redirects the user to the ultimate landing page where disinformation material is hosted.

The use of HTTP meta tags, obfuscated JavaScript, and rapid redirections are classic techniques designed to confound automated scanners and manual analysts alike.

Impact on Endpoint and Attack Surface Management

Fragmented Endpoint Strategies

Fragmented endpoint strategies refer to the use of a heterogeneous mix of security products across an organization’s endpoints. This fragmentation can result in:

Inconsistent Coverage: Some endpoints might be well-protected, while others could have outdated or incompatible security software.
Delayed Incident Response: Without a unified approach, detecting and responding to threats becomes slower, allowing adversaries time to exploit vulnerabilities.
Complex ASM: Attack Surface Management (ASM) efforts become difficult as disparate technologies make it challenging to track all endpoints, vulnerabilities, and potential shadow IT.

HarfangLab’s recent research suggests that when endpoint strategies are not seamlessly integrated, adversaries can take advantage of the gaps to deploy disinformation mechanisms and inject malicious payloads.

Tools and Methodologies for ASM

Effective ASM must involve:

Vulnerability Assessments: Frequent scanning of systems to identify missing patches or unprotected services.
Shadow IT Discovery: Identifying unauthorized or unmonitored systems within the network.
Endpoint Protection Platforms (EPP) and Endpoint Detection & Response (EDR): These tools help monitor and mitigate threats in real time.
Behavioral and Signatures Engines: The use of YARA rules, Sigma for behavioral detection, and IOC engines to identify known malicious artifacts.
AI and Machine Learning: The integration of AI engines can assist in detecting patterns that traditional signature-based detection might miss.

An integrated security approach, combining these capabilities, is crucial for effective ASM against multifaceted threats like Doppelgänger operations.

Code Samples and Practical Analysis

To empower you in analyzing redirection chains and scanning endpoints, here are some practical code samples using Bash and Python.

Scanning Commands with Bash

The following Bash script demonstrates how to scan a list of potential redirector URLs to extract HTTP headers and identify redirection chains. This script uses curl to perform HTTP requests.

#!/bin/bash
# List of first-level redirectors
redirectors=("http://a1b2c3.top/xyz123" "http://d4e5f6.click/abc789")

# Function to extract HTTP headers using curl
scan_url() {
    local url=$1
    echo "Scanning URL: $url"
    curl -sIL "$url" | grep -i "Location:"
    echo "---------------------------------"
}

# Loop through each URL and scan
for url in "${redirectors[@]}"; do
    scan_url "$url"
done

Explanation:

The script loops through a hardcoded list of URLs.
For each URL, it sends a HEAD request (-I option) to retrieve HTTP headers.
The headers are filtered for “Location:” to show redirection targets.

Parsing Output with Python

After gathering redirection data, you may want to parse and analyze the output further. The following Python sample uses regular expressions to parse URLs from a text file containing HTTP headers:

import re

def parse_redirection(file_path):
    redirections = {}
    with open(file_path, 'r') as file:
        content = file.read()
        # Regex pattern to match Location headers
        pattern = re.compile(r'Location:\s*(\S+)', re.IGNORECASE)
        matches = pattern.findall(content)
        for url in matches:
            # Group redirections incrementally
            domain = re.findall(r'://([^/]+)/?', url)
            if domain:
                redirections.setdefault(domain[0], []).append(url)
    return redirections

# Sample usage
if __name__ == '__main__':
    file_path = 'http_headers.txt'
    redirection_dict = parse_redirection(file_path)
    for domain, urls in redirection_dict.items():
        print(f"Domain: {domain}")
        for link in urls:
            print(f"  -> {link}")

Explanation:

The script reads the content from a file (http_headers.txt) which might include raw HTTP header data.
It uses a regular expression to extract all “Location:” headers.
The URLs are grouped by domain for easier analysis of redirection patterns.

These samples can be extended or integrated into larger threat intelligence systems to automate the identification of malicious redirection chains.

Mitigation Strategies and Recommendations

Best Practices for Threat Intelligence

Centralized ASM Platform:
Use platforms that integrate threat intelligence, vulnerability management, shadow IT discovery, and detailed endpoint monitoring. Centralization helps reduce blind spots in network defense.
Regular Threat Hunting:
Security teams should regularly hunt for indicators of Doppelgänger activities. This includes monitoring for anomalous bot behaviors on social media platforms and unusual redirection patterns.
Share Threat Intelligence:
Collaboration between government organizations, private companies, and cybersecurity communities can help disseminate newly observed TTPs (Tactics, Techniques, and Procedures) faster.
Enhanced Logging and Monitoring:
Implement comprehensive logging on all endpoints and network devices to quickly correlate events and spot redirection chains early in the process.

Role of AI and Behavioral Engines

The integration of AI engines and behavioral analytics is becoming increasingly important in spotting patterns that static signatures cannot. Consider the following strategies:

AI-Assisted Analysis:
Use machine learning algorithms to identify anomalies in redirection patterns or social media posts.
Behavioral Engine Integration:
Engines such as Sigma (for behavioral detection) or custom YARA rules can detect newly emerging malicious indicators.
Ransomguard and Sidewatch Engines:
These engines monitor and investigate suspicious activities that traditional antivirus software might miss. Their use in combination provides layered defense against advanced persistent threats.

Endpoint Protection and Response

Unified EPP and EDR Solutions:
Avoid fragmented security strategies by adopting unified endpoint protection platforms. This minimizes gaps that adversaries could exploit.
AI Assistant and Connectors:
Leverage AI-based assistance and various connectors to integrate various security tools, creating an ecosystem that is responsive, cohesive, and less prone to vulnerabilities.
Regular Patching and Endpoint Audits:
Establish a schedule for endpoint vulnerability assessments and patch management, ensuring that misconfigured or outdated endpoints do not serve as entry points.

Case Studies and Real-World Examples

Case Study 1: Election Influence in France

During France’s snap general election in June 2024, multiple Doppelgänger operations were observed. Analysts noted the following:

Dissemination via Bot Networks:
Approximately 800 Twitter accounts artificially inflated engagement with unique posts, leading to rapid yet deceptive dissemination of fabricated information.
Rapid Infrastructure Rotation:
The associated redirection chains were continuously updated with a rotation of domains registered on new TLDs (e.g., .top, .click). This agility made it challenging for law enforcement and cybersecurity firms to keep pace.
Impact on Public Discourse:
The disinformation spread by these networks influenced debate by creating confusion and mistrust in legitimate sources. Real-time monitoring and detection eventually curtailed the spread, but the episode underscored the need for more integrated ASM solutions.

Case Study 2: Cross-Platform Disruption in the US

In the United States, Doppelgänger tactics were not confined solely to one social media platform:

Multi-Platform Abuse:
Although X/Twitter was a primary channel, operations were also noted on Meta-owned platforms. Content was shared in various formats, from text posts to multimedia content such as AI-generated music videos.
Overlap with Cybercrime:
Some monitored bot accounts had histories in cryptocurrency scams. This suggests that either cybercriminal networks are renting bot services or that state-sponsored operators are collaborating with financially motivated individuals.
Endpoint Vulnerability Exploitation:
The fragmented endpoint strategies employed by several organizations in the US led to delayed detection of payload delivery. Enterprises with inconsistent security postures provided adversaries with exploitable vulnerabilities that could lead to further attack surface expansion.

Conclusion

Mid-year Doppelgänger information operations represent a new breed of digital disinformation—where sophisticated redirection chains, AI-generated content, and fragmented endpoint strategies converge to shape public discourse and exploit endpoint vulnerabilities. As demonstrated by HarfangLab’s in-depth research and the HarfangLab x Forrester webinar, it is crucial for cybersecurity professionals to integrate threat intelligence with centralized Attack Surface Management (ASM) and unified endpoint protection solutions.

Key takeaways include:

The importance of understanding multi-layered redirection chains that obfuscate malicious infrastructure.
The need for unified endpoint strategies to reduce vulnerabilities that arise from fragmented protection approaches.
The role of AI and behavioral analysis in detecting and mitigating mutating threat vectors.

By embracing comprehensive threat intelligence, investing in integrated security platforms, and fostering collaboration across industries, organizations can better defend against the advanced tactics employed by Doppelgänger operations and similar disinformation campaigns.

References

Feel free to share your thoughts and questions in the comments below. As we move into an era of ever-evolving disinformation tactics, staying informed and prepared is our best defense.

By following the insights and technical deep dives provided in this post, you can better understand the mechanics behind these operations and fortify your organization’s digital defenses against advanced, fragmented endpoint risks. Happy threat hunting!

Doppelgänger Info Operations Target Europe & US | HarfangLab