Application Window Discovery

Application Window Discovery [T1010]

Information

• Name: Application Window Discovery

• ID: T1010

• Tactics: TA0007

Introduction

Application Window Discovery is a tactic categorized under the MITRE ATT&CK framework as part of the Discovery technique (T1010). Attackers leverage this technique to identify currently open windows and running applications on compromised systems. By enumerating active application windows, adversaries can gain valuable insights into user activities, installed software, and potential opportunities for further exploitation or lateral movement.

Deep Dive Into Technique

Application Window Discovery involves enumerating active windows or applications running on a compromised host. Attackers typically perform this enumeration using built-in operating system utilities, scripting languages, or custom-built malware.

Common execution methods and mechanisms include:

• Native Operating System Commands:

  • Windows:

    • Tasklist (tasklist /v)

    • PowerShell commands (Get-Process, Get-Window)

  • Linux:

    • wmctrl -l

    • xwininfo

    • xdotool

• Custom Scripts and Malware:

  • Attackers often use scripts in languages such as PowerShell, Python, or VBScript to automate enumeration tasks.

  • Malware payloads may include functionality to periodically enumerate active windows and report back to attacker-controlled servers.

• API Calls:

  • Windows API functions such as EnumWindows(), GetWindowText(), and GetForegroundWindow() provide attackers with detailed information on open windows.

  • Linux and Unix-based systems may utilize X11 libraries and related APIs to query window states and titles.

Real-world procedures typically involve attackers enumerating windows to:

• Identify sensitive applications (such as banking software, email clients, or password managers).

• Determine user behavior and active sessions.

• Identify security-related tools or monitoring applications running on the targeted host.

When this Technique is Usually Used

Attackers commonly employ Application Window Discovery at various stages of a cyberattack, including:

• Initial Reconnaissance:

  • Gathering intelligence about the user's active sessions and currently running applications.

  • Identifying potential vulnerabilities based on the running applications.

• Privilege Escalation and Credential Access:

  • Determining if administrative or sensitive applications (e.g., password managers, administrative consoles) are active.

  • Identifying opportunities to capture credentials from active applications.

• Lateral Movement:

  • Discovering shared applications or remote desktop sessions that could facilitate lateral movement within the network.

• Data Collection and Exfiltration:

  • Monitoring active windows to determine optimal timing for data theft or exfiltration.

  • Identifying sensitive documents or applications that contain valuable data.

How this Technique is Usually Detected

Organizations can detect Application Window Discovery through various detection methods, tools, and indicators of compromise (IoCs):

• Endpoint Detection and Response (EDR) Tools:

  • Monitoring and alerting on suspicious API calls (e.g., EnumWindows(), GetForegroundWindow()).

  • Detecting unusual usage patterns of built-in utilities (e.g., tasklist, PowerShell enumeration scripts).

• Process Monitoring and Logging:

  • Analyzing logs for abnormal execution of enumeration commands or scripts.

  • Identifying unexpected processes or scripts querying active window information.

• Behavioral Analytics and Anomaly Detection:

  • Detecting deviations from normal user behavior, such as an unusual number of window enumeration queries.

  • Identifying scripts or processes running outside typical administrative or user contexts.

• Specific Indicators of Compromise (IoCs):

  • Suspicious scripts or binaries executing window enumeration commands.

  • Unusual log entries related to window enumeration APIs.

  • Unexpected network activity following enumeration events, indicating potential data exfiltration.

Why it is Important to Detect This Technique

Early detection of Application Window Discovery is crucial for several reasons:

• Prevention of Further Exploitation:

  • Detecting enumeration activities allows security teams to intervene before attackers escalate privileges or move laterally within the network.

• Protection of Sensitive Information:

  • Enumerating active windows can reveal sensitive applications and data, such as financial software, password managers, or confidential documents. Early detection prevents unauthorized access and data theft.

• Reducing Attack Dwell Time:

  • Identifying enumeration activities early in the attack lifecycle reduces the attacker's dwell time, minimizing potential damages and remediation costs.

• Maintaining Operational Integrity:

  • Enumeration of active windows can indicate an attacker attempting to identify and disrupt critical business processes or operations. Early detection helps maintain operational continuity and stability.

Examples

Real-world examples of Application Window Discovery include:

• APT29 (Cozy Bear):

  • Attack Scenario:

    • Utilized PowerShell scripts to enumerate active windows and running applications on compromised hosts.

  • Tools Used:

    • Custom PowerShell scripts leveraging Get-Process and window enumeration APIs.

  • Impact:

    • Successfully identified sensitive applications and administrative tools, facilitating lateral movement and credential harvesting.

• FIN7 Cybercrime Group:

  • Attack Scenario:

    • Deployed malware payloads capable of enumerating active window titles and capturing screenshots of targeted applications.

  • Tools Used:

    • Customized malware variants utilizing Windows API calls (EnumWindows(), GetWindowText()) and screenshot capturing.

  • Impact:

    • Collected sensitive financial information, credentials, and facilitated subsequent financial fraud activities.

• TrickBot Malware:

  • Attack Scenario:

    • Enumerated active windows to detect banking applications and browser windows during user sessions.

  • Tools Used:

    • TrickBot modules leveraging Windows APIs and scripts to identify active banking applications.

  • Impact:

    • Successfully targeted banking credentials, leading to financial loss and unauthorized transactions.

• Red Team Assessments and Penetration Tests:

  • Attack Scenario:

    • Security professionals frequently enumerate active windows to identify sensitive applications during authorized penetration tests.

  • Tools Used:

    • Built-in OS utilities (e.g., tasklist), PowerShell scripts, and custom enumeration tools.

  • Impact:

    • Demonstrated potential risks associated with active window enumeration, highlighting the importance of monitoring and detection mechanisms.