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ATT&CK Technique

Masquerade File Type

T1036.008 · stealth

Adversaries may masquerade malicious payloads as legitimate files through changes to the payload's formatting, including the file’s signature, extension, icon, and contents. Various file types have a typical standard format, including how they are encoded and organized. For example, a file’s signature (also known as header or magic bytes) is the beginning bytes of a file and is often used to identify the file’s type.

For example, the header of a JPEG file, is 0xFF 0xD8 and the file extension is either .JPE, .JPEG or .JPG. Adversaries may edit the header’s hex code and/or the file extension of a malicious payload in order to bypass file validation checks and/or input sanitization. This behavior is commonly used when payload files are transferred (e.g., Ingress Tool Transfer) and stored (e.g., Upload Malware) so that adversaries may move their malware without triggering detections.

Common non-executable file types and extensions, such as text files (.txt) and image files (.jpg, .gif, etc.) may be typically treated as benign. Based on this, adversaries may use a file extension to disguise malware, such as naming a PHP backdoor code with a file name of test.gif. A user may not know that a file is malicious due to the benign appearance and file extension.

Polyglot files, which are files that have multiple different file types and that function differently based on the application that will execute them, may also be used to disguise malicious malware and capabilities.

LinuxmacOSWindows

Actors Using This

8
latin_america_brazilian_organized_cybercrimeGrandoreiro
north_koreaLazarus Group
palestinian_territoriesMolerats / Gaza Cybergang
russia_speaking_organized_cybercrimePikaBot Operators / Water Curupira
financially_motivated_italy_based_criminal_mandiant_medium_confidence_unc4990UNC4990 (Italy USB Cryptojacking Operator)

Likely Attack Path

Techniques the same actors pair with this one distinctively - those showing up among actors who use this technique noticeably more than across all actors (lift > 1.15), grouped by kill-chain phase. The × is that lift multiplier; the shared-actor count is in the tooltip. A near-universal technique pairs with everything at baseline, so its list is short by design.
command-and-control same

Mitigations

3
MITRE ATT&CK mitigations - vendor-agnostic guidance for reducing exposure to this technique.
M1038Execution Prevention

Prevent the execution of unauthorized or malicious code on systems by implementing application control, script blocking, and other execution prevention mechanisms. This ensures that only trusted and authorized code is executed, reducing the risk of malware and unauthorized actions.

Application Control
  • Use Case: Use tools like AppLocker or Windows Defender Application Control (WDAC) to create whitelists of authorized applications and block unauthorized ones. On Linux, use tools like SELinux or AppArmor to define mandatory access control policies for application execution.
  • Implementation: Allow only digitally signed or pre-approved applications to execute on servers and endpoints. (e.g., `New-AppLockerPolicy -PolicyType Enforced -FilePath "C:\Policies\AppLocker.
xml"`) Script Blocking
  • Use Case: Use script control mechanisms to block unauthorized execution of scripts, such as PowerShell or JavaScript. Web Browsers: Use browser extensions or settings to block JavaScript execution from untrusted sources.
  • Implementation: Configure PowerShell to enforce Constrained Language Mode for non-administrator users. (e.g.
, Set-ExecutionPolicy AllSigned) Executable Blocking
  • Use Case: Prevent execution of binaries from suspicious locations, such as %TEMP% or %APPDATA% directories.
  • Implementation: Block execution of .exe, .bat, or .ps1 files from user-writable directories.
Dynamic Analysis Prevention
  • Use Case: Use behavior-based execution prevention tools to identify and block malicious activity in real time.
  • Implemenation: Employ EDR solutions that analyze runtime behavior and block suspicious code execution.
M1040Behavior Prevention on Endpoint

Behavior Prevention on Endpoint refers to the use of technologies and strategies to detect and block potentially malicious activities by analyzing the behavior of processes, files, API calls, and other endpoint events. Rather than relying solely on known signatures, this approach leverages heuristics, machine learning, and real-time monitoring to identify anomalous patterns indicative of an attack.

Suspicious Process Behavior
  • Implementation: Use Endpoint Detection and Response (EDR) tools to monitor and block processes exhibiting unusual behavior, such as privilege escalation attempts.
  • Use Case: An attacker uses a known vulnerability to spawn a privileged process from a user-level application. The endpoint tool detects the abnormal parent-child process relationship and blocks the action.
Unauthorized File Access
  • Implementation: Leverage Data Loss Prevention (DLP) or endpoint tools to block processes attempting to access sensitive files without proper authorization.
  • Use Case: A process tries to read or modify a sensitive file located in a restricted directory, such as /etc/shadow on Linux or the SAM registry hive on Windows. The endpoint tool identifies this anomalous behavior and prevents it.
Abnormal API Calls
  • Implementation: Implement runtime analysis tools to monitor API calls and block those associated with malicious activities.
  • Use Case: A process dynamically injects itself into another process to hijack its execution. The endpoint detects the abnormal use of APIs like OpenProcess and WriteProcessMemory and terminates the offending process.
Exploit Prevention
  • Implementation: Use behavioral exploit prevention tools to detect and block exploits attempting to gain unauthorized access.
  • Use Case: A buffer overflow exploit is launched against a vulnerable application. The endpoint detects the anomalous memory write operation and halts the process.
M1049Antivirus/Antimalware

Antivirus/Antimalware solutions utilize signatures, heuristics, and behavioral analysis to detect, block, and remediate malicious software, including viruses, trojans, ransomware, and spyware. These solutions continuously monitor endpoints and systems for known malicious patterns and suspicious behaviors that indicate compromise. Antivirus/Antimalware software should be deployed across all devices, with automated updates to ensure protection against the latest threats.

Signature-Based Detection
  • Implementation: Use predefined signatures to identify known malware based on unique patterns such as file hashes, byte sequences, or command-line arguments. This method is effective against known threats.
  • Use Case: When malware like "Emotet" is detected, its signature (such as a specific file hash) matches a known database of malicious software, triggering an alert and allowing immediate quarantine of the infected file.
Heuristic-Based Detection
  • Implementation: Deploy heuristic algorithms that analyze behavior and characteristics of files and processes to identify potential malware, even if it doesn’t match a known signature.
  • Use Case: If a program attempts to modify multiple critical system files or initiate suspicious network communications, heuristic analysis may flag it as potentially malicious, even if no specific malware signature is available.
Behavioral Detection (Behavior Prevention)
  • Implementation: Use behavioral analysis to detect patterns of abnormal activities, such as unusual system calls, unauthorized file encryption, or attempts to escalate privileges.
  • Use Case: Behavioral analysis can detect ransomware attacks early by identifying behavior like mass file encryption, even before a specific ransomware signature has been identified.
Real-Time Scanning
  • Implementation: Enable real-time scanning to automatically inspect files and network traffic for signs of malware as they are accessed, downloaded, or executed.
  • Use Case: When a user downloads an email attachment, the antivirus solution scans the file in real-time, checking it against both signatures and heuristics to detect any malicious content before it can be opened.
Cloud-Assisted Threat Intelligence
  • Implementation: Use cloud-based threat intelligence to ensure the antivirus solution can access the latest malware definitions and real-time threat feeds from a global database of emerging threats.
  • Use Case: Cloud-assisted antivirus solutions quickly identify newly discovered malware by cross-referencing against global threat databases, providing real-time protection against zero-day attacks.
Tools for Implementation
  • Endpoint Security Platforms: Use solutions such as EDR for comprehensive antivirus/antimalware protection across all systems.
  • Centralized Management: Implement centralized antivirus management consoles that provide visibility into threat activity, enable policy enforcement, and automate updates.
  • Behavioral Analysis Tools: Leverage solutions with advanced behavioral analysis capabilities to detect malicious activity patterns that don’t rely on known signatures.

Detection Coverage

0/6 layers
Coverage across standard detection surfaces. Rows marked none have no rule of that type mapped. Some are real blind spots worth closing; others are simply not applicable to this technique (e.g. YARA matches malware files, not network behaviour).
Behavioral / log (Sigma) none
Analytics (MITRE CAR) none
Runtime / container (Falco) none
File / malware (YARA) none
Network (Suricata/Snort) none
Vuln scan (Nuclei) none

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NIST 800-53CM-07, SC-07, SI-03, SI-04, SI-10
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