Class: Trojan
A malicious program designed to electronically spy on the user’s activities (intercept keyboard input, take screenshots, capture a list of active applications, etc.). The collected information is sent to the cybercriminal by various means, including email, FTP, and HTTP (by sending data in a request).Read more
Platform: Win32
Win32 is an API on Windows NT-based operating systems (Windows XP, Windows 7, etc.) that supports execution of 32-bit applications. One of the most widespread programming platforms in the world.Family: Matanbuchus
No family descriptionExamples
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Tactics and Techniques: Mitre*
Adversaries may acquire credentials from web browsers by reading files specific to the target browser.(Citation: Talos Olympic Destroyer 2018) Web browsers commonly save credentials such as website usernames and passwords so that they do not need to be entered manually in the future. Web browsers typically store the credentials in an encrypted format within a credential store; however, methods exist to extract plaintext credentials from web browsers.
For example, on Windows systems, encrypted credentials may be obtained from Google Chrome by reading a database file, AppDataLocalGoogleChromeUser DataDefaultLogin Data and executing a SQL query: SELECT action_url, username_value, password_value FROM logins;. The plaintext password can then be obtained by passing the encrypted credentials to the Windows API function CryptUnprotectData, which uses the victim’s cached logon credentials as the decryption key.(Citation: Microsoft CryptUnprotectData April 2018)
Adversaries have executed similar procedures for common web browsers such as FireFox, Safari, Edge, etc.(Citation: Proofpoint Vega Credential Stealer May 2018)(Citation: FireEye HawkEye Malware July 2017) Windows stores Internet Explorer and Microsoft Edge credentials in Credential Lockers managed by the Windows Credential Manager.
Adversaries may also acquire credentials by searching web browser process memory for patterns that commonly match credentials.(Citation: GitHub Mimikittenz July 2016)
After acquiring credentials from web browsers, adversaries may attempt to recycle the credentials across different systems and/or accounts in order to expand access. This can result in significantly furthering an adversary’s objective in cases where credentials gained from web browsers overlap with privileged accounts (e.g. domain administrator).
Adversaries may acquire credentials from web browsers by reading files specific to the target browser.(Citation: Talos Olympic Destroyer 2018) Web browsers commonly save credentials such as website usernames and passwords so that they do not need to be entered manually in the future. Web browsers typically store the credentials in an encrypted format within a credential store; however, methods exist to extract plaintext credentials from web browsers.
For example, on Windows systems, encrypted credentials may be obtained from Google Chrome by reading a database file, AppDataLocalGoogleChromeUser DataDefaultLogin Data and executing a SQL query: SELECT action_url, username_value, password_value FROM logins;. The plaintext password can then be obtained by passing the encrypted credentials to the Windows API function CryptUnprotectData, which uses the victim’s cached logon credentials as the decryption key.(Citation: Microsoft CryptUnprotectData April 2018)
Adversaries have executed similar procedures for common web browsers such as FireFox, Safari, Edge, etc.(Citation: Proofpoint Vega Credential Stealer May 2018)(Citation: FireEye HawkEye Malware July 2017) Windows stores Internet Explorer and Microsoft Edge credentials in Credential Lockers managed by the Windows Credential Manager.
Adversaries may also acquire credentials by searching web browser process memory for patterns that commonly match credentials.(Citation: GitHub Mimikittenz July 2016)
After acquiring credentials from web browsers, adversaries may attempt to recycle the credentials across different systems and/or accounts in order to expand access. This can result in significantly furthering an adversary’s objective in cases where credentials gained from web browsers overlap with privileged accounts (e.g. domain administrator).
Adversaries may employ various time-based methods to detect and avoid virtualization and analysis environments. This may include enumerating time-based properties, such as uptime or the system clock, as well as the use of timers or other triggers to avoid a virtual machine environment (VME) or sandbox, specifically those that are automated or only operate for a limited amount of time.
Adversaries may employ various time-based evasions, such as delaying malware functionality upon initial execution using programmatic sleep commands or native system scheduling functionality (ex: Scheduled Task/Job). Delays may also be based on waiting for specific victim conditions to be met (ex: system time, events, etc.) or employ scheduled Multi-Stage Channels to avoid analysis and scrutiny.(Citation: Deloitte Environment Awareness)
Benign commands or other operations may also be used to delay malware execution. Loops or otherwise needless repetitions of commands, such as Pings, may be used to delay malware execution and potentially exceed time thresholds of automated analysis environments.(Citation: Revil Independence Day)(Citation: Netskope Nitol) Another variation, commonly referred to as API hammering, involves making various calls to Native API functions in order to delay execution (while also potentially overloading analysis environments with junk data).(Citation: Joe Sec Nymaim)(Citation: Joe Sec Trickbot)
Adversaries may also use time as a metric to detect sandboxes and analysis environments, particularly those that attempt to manipulate time mechanisms to simulate longer elapses of time. For example, an adversary may be able to identify a sandbox accelerating time by sampling and calculating the expected value for an environment’s timestamp before and after execution of a sleep function.(Citation: ISACA Malware Tricks)
Adversaries may employ various time-based methods to detect and avoid virtualization and analysis environments. This may include enumerating time-based properties, such as uptime or the system clock, as well as the use of timers or other triggers to avoid a virtual machine environment (VME) or sandbox, specifically those that are automated or only operate for a limited amount of time.
Adversaries may employ various time-based evasions, such as delaying malware functionality upon initial execution using programmatic sleep commands or native system scheduling functionality (ex: Scheduled Task/Job). Delays may also be based on waiting for specific victim conditions to be met (ex: system time, events, etc.) or employ scheduled Multi-Stage Channels to avoid analysis and scrutiny.(Citation: Deloitte Environment Awareness)
Benign commands or other operations may also be used to delay malware execution. Loops or otherwise needless repetitions of commands, such as Pings, may be used to delay malware execution and potentially exceed time thresholds of automated analysis environments.(Citation: Revil Independence Day)(Citation: Netskope Nitol) Another variation, commonly referred to as API hammering, involves making various calls to Native API functions in order to delay execution (while also potentially overloading analysis environments with junk data).(Citation: Joe Sec Nymaim)(Citation: Joe Sec Trickbot)
Adversaries may also use time as a metric to detect sandboxes and analysis environments, particularly those that attempt to manipulate time mechanisms to simulate longer elapses of time. For example, an adversary may be able to identify a sandbox accelerating time by sampling and calculating the expected value for an environment’s timestamp before and after execution of a sleep function.(Citation: ISACA Malware Tricks)
Adversaries may attempt to get a listing of security software, configurations, defensive tools, and sensors that are installed on a system or in a cloud environment. This may include things such as firewall rules and anti-virus. Adversaries may use the information from Security Software Discovery during automated discovery to shape follow-on behaviors, including whether or not the adversary fully infects the target and/or attempts specific actions.
Example commands that can be used to obtain security software information are netsh, reg query with Reg, dir with cmd, and Tasklist, but other indicators of discovery behavior may be more specific to the type of software or security system the adversary is looking for. It is becoming more common to see macOS malware perform checks for LittleSnitch and KnockKnock software.
Adversaries may also utilize cloud APIs to discover the configurations of firewall rules within an environment.(Citation: Expel IO Evil in AWS) For example, the permitted IP ranges, ports or user accounts for the inbound/outbound rules of security groups, virtual firewalls established within AWS for EC2 and/or VPC instances, can be revealed by the DescribeSecurityGroups action with various request parameters. (Citation: DescribeSecurityGroups – Amazon Elastic Compute Cloud)
* © 2024 The MITRE Corporation. This work is reproduced and distributed with the permission of The MITRE Corporation.