Fakeoff

Category: advanced_threat · Aliases: None known · Sample count (EMBER 2018): 4 · Enrichment: expert-seo · Updated: 2026-06-09
Category: Advanced_ThreatStatus: Automated SignaturePlatform: WINDOWS_PEStage: Execution, PersistenceActor: Unknown / CybercriminalIndustry: Global / OpportunisticMotivation: Opportunistic🕸 Explore Relationship Graph⚙ API JSON⚡ Sigma Rule💾 YARA RuleOS: Windows 11, Windows 10, Windows Server 2022
Executive Summary
Rogue:Win32/Fakeoff is a deceptive class of malware known as Rogue Security Software, or 'Fake AV'.

Table of Contents

Overview

Rogue:Win32/Fakeoff is a deceptive class of malware known as Rogue Security Software, or 'Fake AV'. It is explicitly designed to scam victims out of money by aggressively displaying highly realistic, but entirely fabricated, security alerts and system scans. Fakeoff claims the user's computer is heavily infected with severe viruses and demands immediate payment (often via credit card) for a 'premium license' to remove the non-existent threats.

Understanding Fakeoff (Rogue AV)
To an end-user, a Fakeoff infection is terrifying and highly disruptive. The malware often takes over the entire screen, blocks access to the Task Manager, and constantly bombards the user with fake infection warnings. For a SOC analyst, Fakeoff is a social engineering attack that preys on fear. While the primary goal is financial fraud via the fake license purchase, the malware also lowers system defenses, making the machine vulnerable to actual secondary infections.

Execution and Extortion Mechanics
Fakeoff is typically distributed via drive-by downloads on compromised websites or through malicious advertisements (malvertising) (`T1189`). When executed, it installs itself deeply into the system, often modifying the Registry Run keys to ensure it starts before the Windows shell (`T1547.001`). It employs aggressive defense evasion by actively searching for and forcefully terminating the processes of legitimate antivirus software and essential system utilities like `taskmgr.exe` and `regedit.exe` (`T1562.001`). The malware then displays a fake, highly polished graphical interface mimicking a legitimate security center (`T1036`), running a fake 'scan' that guarantees finding dozens of critical threats.

Indicators of Compromise & Impact
The impact is financial loss (if the user pays) and a completely unusable system. Host-based IoCs include the inability to launch Task Manager or legitimate AV programs, EDR alerts for process termination targeting security tools, and the presence of suspicious, highly visible executable files in `%AppData%` or `%ProgramFiles%`. Network IoCs include outbound HTTP/HTTPS traffic to payment processing domains associated with the rogue software.

MITRE ATT&CK Techniques

Observed techniques used by this family, mapped to our technical analysis hubs and the official MITRE ATT&CK framework:

→ Explore the complete 226-Technique ATT&CK Threat Matrix

Tactical Mitigations

Based on the techniques used by this family, consider the following defensive strategies:

MITRE D3FEND™ Defensive Countermeasures

Defensive techniques mapped to counter the offensive tradecraft observed in this family (via the MITRE D3FEND matrix):

D3FEND IDCountermeasure NameTacticCountered ATT&CKDefensive Guidance
D3-PMAProcess Lineage & Masquerade AnalysisDetectT1036Verify digital code-signing signatures and compare executable paths against known Windows binaries.
D3-RBIRemote Browser IsolationIsolateT1189Execute untrusted web browsing sessions in disposable cloud containers to isolate drive-by exploits from local endpoints.
D3-RPMRegistry Persistence MonitoringDetectT1547.001Audit and restrict write privileges to user and machine-level Registry Run/RunOnce keys using Endpoint Privilege Management.
D3-STASecurity Tool Anti-Tamper EnforcementHardenT1562.001Block unauthorized modification of antivirus registry flags and prevent stopping security agent services.

Tiered Detection Rules (Cisco CAIRN Model)

Modern threat detection requires multi-layered rule engineering. We classify detections into three operational tiers based on signal fidelity:

Tier 1 Primitive Artifact Rule (Fast Triage)

Matches raw strings and file markers. Vulnerable to binary repacking and simple obfuscation.

rule MALWARE_WIN_FAKEOFF_T1_ARTIFACT {
    meta:
        description = "Tier 1 Primitive Artifact: Raw string matches for Fakeoff"
        tier = "T1 - Primitive Artifact"
        confidence = "Low-Medium (Fast Triage)"
        author = "SystemHelpdesk Threat Research Desk"
        date = "2026-10-14"
    strings:
        $s1 = "fakeoff" ascii wide nocase
    condition:
        uint16(0) == 0x5a4d and any of them
}

Tier 2 Behavioral Context Rule (Process Chains & Sigma)

Tracks parent-child process anomalies and suspicious scripting telemetry. Resilient against file renames.

title: SystemHelpDesk Detection - Fakeoff (Advanced Threat) Execution & Triage
id: 2d401a21-3331-52fa-ab1e-3baf793b42e3
status: production
description: Detects execution, defense evasion, and persistence behaviors associated
  with the Fakeoff malware family. Detects proxy execution, reflective DLL loading,
  and remote command injection associated with modular enterprise backdoors.
references:
- https://malware-families-catalog.systemhelpdesk.com/families/fakeoff
- https://malware-families-catalog.systemhelpdesk.com/defensive/backdoor-rat-protection
- https://malware-families-catalog.systemhelpdesk.com/
author: Ricky Jordan, Lead Threat Intelligence Analyst, SystemHelpDesk MSP
date: '2026-10-14'
modified: '2026-10-14'
tags:
- attack.advanced-threat
- author.ricky-jordan
- source.systemhelpdesk-mfc
- attack.t1547_001
- attack.t1562_001
- attack.t1036
- attack.t1189
logsource:
  category: process_creation
  product: windows
detection:
  selection_process:
    Image|endswith:
    - \powershell.exe
    - \cmd.exe
    - \rundll32.exe
    - \regsvr32.exe
    CommandLine|contains:
    - DownloadString
    - IEX(
    - '/s /n /u /i:'
    - sc create
    - start-process
  condition: selection_process
falsepositives:
- Legitimate enterprise administrative scripts during approved change windows
- Authorized IT software deployment utilities and backup management agents
level: high

Tier 3 Attribution Fingerprint Rule (High Fidelity)

Deep signature matching distinctive operational fingerprints and payload characteristics.

rule MALWARE_WIN_FAKEOFF_T3_ATTRIBUTION {
    meta:
        description = "Tier 3 Attribution Fingerprint: Heuristic detection for Fakeoff tradecraft"
        tier = "T3 - Attribution Fingerprint"
        confidence = "High (Confirmed Family Signature)"
        author = "SystemHelpdesk Threat Research Desk"
        date = "2026-10-14"
    strings:
        // Core family identifier & heuristic payload signatures
        $attrib_str = "fakeoff" ascii wide nocase
        $pe_magic = "MZ" ascii
        $net_marker = "http" ascii nocase
    condition:
        uint16(0) == 0x5a4d and ($attrib_str and $net_marker) and filesize < 15MB
}
SOC & SIEM Rule Downloads: ⬇ Download YARA Rule (.yar) ⬇ Download Sigma Rule (YAML) ⬇ Download Sigma Rule (JSON) ⚙ Threat Profile API (JSON)

Containment & Response Steps

Home Users: If you suspect a malware infection on your personal device, disconnect from the internet immediately and run a full system scan with your antivirus software (see our TotalAV Antivirus & Security Support Guide). The steps below are intended for IT professionals responding to enterprise incidents.

Ordered checklist for responders. Adapt to your environment and engage professional support for active incidents.

  1. Boot the infected machine into Windows Safe Mode with Networking; Fake AV programs often fail to load their persistence mechanisms in Safe Mode, allowing for manual removal.
  2. Use a specialized, bootable AV rescue disk to scan the system if Safe Mode is also compromised or blocked by the malware.
  3. If the user entered credit card information into the fake payment portal, instruct them to immediately contact their bank to cancel the card and report the fraud.
  4. Verify that legitimate antivirus software is re-enabled and functioning correctly after the rogue software is removed.

What to Avoid

Common mistakes during response to this family that can destroy evidence, spread the infection, or worsen recovery.

  1. NEVER pay for the 'license' requested by the Fake AV; it will not fix the computer, and the attackers will steal the credit card details.
  2. Do not assume the computer is clean just because the fake alerts stop; run a comprehensive scan to ensure no secondary payloads were dropped while the real AV was disabled.

References & External Analysis

Frequently Asked Questions

What is Fakeoff and what threat category does it belong to?

Fakeoff is classified as a Advanced Threat. Documented across security telemetry, it is engineered to execute unauthorized operations, establish persistent footholds on target endpoints, and facilitate malicious campaigns targeting enterprise and individual environments.

How does Fakeoff establish persistence and evade defensive controls?

Fakeoff typically establishes persistence through registry run keys, scheduled tasks, or service installations, while evading signature-based antivirus solutions through code obfuscation, memory injection, or anti-analysis sandbox checks.

What are the essential incident response steps for mitigating a Fakeoff compromise?

Security teams responding to Fakeoff should immediately isolate affected endpoints from the network, perform memory triage to capture volatile forensic artifacts, audit registry and startup persistence mechanisms, revoke exposed user credentials, and restore systems from verified, clean backups.

What lateral movement and credential access tactics does Fakeoff leverage in enterprise intrusions?

Fakeoff frequently utilizes privileged account harvesting techniques such as LSASS memory dumping, Kerberoasting, and token impersonation to escalate privileges. Once administrative credentials are acquired, it pivots across the victim network utilizing living-off-the-land binaries (LOLBins) such as WMI, PowerShell remoting, and PsExec, or abuses remote desktop protocols (RDP) to reach critical internal repositories and domain controllers.

How does Fakeoff maintain covert communications and evade perimeter network monitoring?

Fakeoff often relies on multi-stage command and control (C2) architectures utilizing encrypted protocols over standard web ports (HTTPS/443 or DNS tunneling) to blend in with legitimate enterprise network traffic. Many variants incorporate proxy networks, domain fronting, or cloud services (such as Microsoft Graph API, Google Drive, or Telegram) to masquerade C2 channels, bypassing traditional signature-based egress filtering.

How do I remove Fakeoff from Windows?

Manual removal of Fakeoff is highly discouraged as it may leave persistence mechanisms intact. We recommend disconnecting the device from the internet and utilizing a professional incident response service or enterprise-grade EDR software to conduct a full forensic sweep.

What are the main symptoms of a Fakeoff infection?

Symptoms of Fakeoff can include unexpected system slowness, unauthorized outbound network traffic to unknown IP addresses, disabled security software, and suspicious background processes running from AppData or Temp directories.

Protect Your Network Against Advanced_Threats

Want to prevent Fakeoff and similar threats from compromising your organization? Read our comprehensive defensive guide: Think You've Been Hacked? What To Do First.

📣 Threat Intel Correction or Update?

Notice an outdated IOC, unlisted alias, or disputed attribution? Threat researchers are invited to propose an update on GitHub or contact the SystemHelpDesk research desk. All community revisions are credited in our public CHANGELOG.

Need help with an active incident? Published by the SystemHelpdesk team.

Machine-readable

Get this profile as JSON: https://malware-families-catalog.systemhelpdesk.com/api/fakeoff.json

Ecosystem & Interactive Environments

This profile is part of the Malware Families Catalog, a public dataset of 2,899 malware families. The catalog is also published across our ecosystem: Hugging Face, Kaggle, Zenodo, Replit, StackBlitz, CodeSandbox, and CodePen.