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T1059.003 | Command and Scripting Interpreter: Windows Command Shell | Adversaries may abuse the Windows command shell for execution. The Windows command shell ([cmd](https://attack.mitre.org/software/S0106)) is the primary command prompt on Windows systems. The Windows command prompt can be used to control almost any aspect of a system, with various permission levels required for different subsets of commands. The command prompt can be invoked remotely via [Remote Services](https://attack.mitre.org/techniques/T1021) such as [SSH](https://attack.mitre.org/techniques/T1021/004).(Citation: SSH in Windows)
Batch files (ex: .bat or .cmd) also provide the shell with a list of sequential commands to run, as well as normal scripting operations such as conditionals and loops. Common uses of batch files include long or repetitive tasks, or the need to run the same set of commands on multiple systems.
Adversaries may leverage [cmd](https://attack.mitre.org/software/S0106) to execute various commands and payloads. Common uses include [cmd](https://attack.mitre.org/software/S0106) to execute a single command, or abusing [cmd](https://attack.mitre.org/software/S0106) interactively with input and output forwarded over a command and control channel. | 09 March 2020 | enterprise-attack | Execution | Usage of the Windows command shell may be common on administrator, developer, or power user systems depending on job function. If scripting is restricted for normal users, then any attempt to enable scripts running on a system would be considered suspicious. If scripts are not commonly used on a system, but enabled, scripts running out of cycle from patching or other administrator functions are suspicious. Scripts should be captured from the file system when possible to determine their actions and intent.
Scripts are likely to perform actions with various effects on a system that may generate events, depending on the types of monitoring used. Monitor processes and command-line arguments for script execution and subsequent behavior. Actions may be related to network and system information Discovery, Collection, or other scriptable post-compromise behaviors and could be used as indicators of detection leading back to the source script. |
T1201 | Password Policy Discovery | Adversaries may attempt to access detailed information about the password policy used within an enterprise network or cloud environment. Password policies are a way to enforce complex passwords that are difficult to guess or crack through [Brute Force](https://attack.mitre.org/techniques/T1110). This information may help the adversary to create a list of common passwords and launch dictionary and/or brute force attacks which adheres to the policy (e.g. if the minimum password length should be 8, then not trying passwords such as 'pass123'; not checking for more than 3-4 passwords per account if the lockout is set to 6 as to not lock out accounts).
Password policies can be set and discovered on Windows, Linux, and macOS systems via various command shell utilities such as <code>net accounts (/domain)</code>, <code>Get-ADDefaultDomainPasswordPolicy</code>, <code>chage -l <username></code>, <code>cat /etc/pam.d/common-password</code>, and <code>pwpolicy getaccountpolicies</code> (Citation: Superuser Linux Password Policies) (Citation: Jamf User Password Policies). Adversaries may also leverage a [Network Device CLI](https://attack.mitre.org/techniques/T1059/008) on network devices to discover password policy information (e.g. <code>show aaa</code>, <code>show aaa common-criteria policy all</code>).(Citation: US-CERT-TA18-106A)
Password policies can be discovered in cloud environments using available APIs such as <code>GetAccountPasswordPolicy</code> in AWS (Citation: AWS GetPasswordPolicy). | 18 April 2018 | enterprise-attack | Discovery | Monitor logs and processes for tools and command line arguments that may indicate they're being used for password policy discovery. Correlate that activity with other suspicious activity from the originating system to reduce potential false positives from valid user or administrator activity. Adversaries will likely attempt to find the password policy early in an operation and the activity is likely to happen with other Discovery activity. |
T1564.011 | Hide Artifacts: Ignore Process Interrupts | Adversaries may evade defensive mechanisms by executing commands that hide from process interrupt signals. Many operating systems use signals to deliver messages to control process behavior. Command interpreters often include specific commands/flags that ignore errors and other hangups, such as when the user of the active session logs off.(Citation: Linux Signal Man) These interrupt signals may also be used by defensive tools and/or analysts to pause or terminate specified running processes.
Adversaries may invoke processes using `nohup`, [PowerShell](https://attack.mitre.org/techniques/T1059/001) `-ErrorAction SilentlyContinue`, or similar commands that may be immune to hangups.(Citation: nohup Linux Man)(Citation: Microsoft PowerShell SilentlyContinue) This may enable malicious commands and malware to continue execution through system events that would otherwise terminate its execution, such as users logging off or the termination of its C2 network connection.
Hiding from process interrupt signals may allow malware to continue execution, but unlike [Trap](https://attack.mitre.org/techniques/T1546/005) this does not establish [Persistence](https://attack.mitre.org/tactics/TA0003) since the process will not be re-invoked once actually terminated. | 24 August 2023 | enterprise-attack | Defense Evasion | null |
T1574.006 | Hijack Execution Flow: Dynamic Linker Hijacking | Adversaries may execute their own malicious payloads by hijacking environment variables the dynamic linker uses to load shared libraries. During the execution preparation phase of a program, the dynamic linker loads specified absolute paths of shared libraries from environment variables and files, such as <code>LD_PRELOAD</code> on Linux or <code>DYLD_INSERT_LIBRARIES</code> on macOS. Libraries specified in environment variables are loaded first, taking precedence over system libraries with the same function name.(Citation: Man LD.SO)(Citation: TLDP Shared Libraries)(Citation: Apple Doco Archive Dynamic Libraries) These variables are often used by developers to debug binaries without needing to recompile, deconflict mapped symbols, and implement custom functions without changing the original library.(Citation: Baeldung LD_PRELOAD)
On Linux and macOS, hijacking dynamic linker variables may grant access to the victim process's memory, system/network resources, and possibly elevated privileges. This method may also evade detection from security products since the execution is masked under a legitimate process. Adversaries can set environment variables via the command line using the <code>export</code> command, <code>setenv</code> function, or <code>putenv</code> function. Adversaries can also leverage [Dynamic Linker Hijacking](https://attack.mitre.org/techniques/T1574/006) to export variables in a shell or set variables programmatically using higher level syntax such Python’s <code>os.environ</code>.
On Linux, adversaries may set <code>LD_PRELOAD</code> to point to malicious libraries that match the name of legitimate libraries which are requested by a victim program, causing the operating system to load the adversary's malicious code upon execution of the victim program. <code>LD_PRELOAD</code> can be set via the environment variable or <code>/etc/ld.so.preload</code> file.(Citation: Man LD.SO)(Citation: TLDP Shared Libraries) Libraries specified by <code>LD_PRELOAD</code> are loaded and mapped into memory by <code>dlopen()</code> and <code>mmap()</code> respectively.(Citation: Code Injection on Linux and macOS)(Citation: Uninformed Needle) (Citation: Phrack halfdead 1997)(Citation: Brown Exploiting Linkers)
On macOS this behavior is conceptually the same as on Linux, differing only in how the macOS dynamic libraries (dyld) is implemented at a lower level. Adversaries can set the <code>DYLD_INSERT_LIBRARIES</code> environment variable to point to malicious libraries containing names of legitimate libraries or functions requested by a victim program.(Citation: TheEvilBit DYLD_INSERT_LIBRARIES)(Citation: Timac DYLD_INSERT_LIBRARIES)(Citation: Gabilondo DYLD_INSERT_LIBRARIES Catalina Bypass) | 13 March 2020 | enterprise-attack | Defense Evasion, Persistence, Privilege Escalation | Monitor for changes to environment variables and files associated with loading shared libraries such as <code>LD_PRELOAD</code> and <code>DYLD_INSERT_LIBRARIES</code>, as well as the commands to implement these changes.
Monitor processes for unusual activity (e.g., a process that does not use the network begins to do so). Track library metadata, such as a hash, and compare libraries that are loaded at process execution time against previous executions to detect differences that do not correlate with patching or updates. |
T1590.004 | Gather Victim Network Information: Network Topology | Adversaries may gather information about the victim's network topology that can be used during targeting. Information about network topologies may include a variety of details, including the physical and/or logical arrangement of both external-facing and internal network environments. This information may also include specifics regarding network devices (gateways, routers, etc.) and other infrastructure.
Adversaries may gather this information in various ways, such as direct collection actions via [Active Scanning](https://attack.mitre.org/techniques/T1595) or [Phishing for Information](https://attack.mitre.org/techniques/T1598). Information about network topologies may also be exposed to adversaries via online or other accessible data sets (ex: [Search Victim-Owned Websites](https://attack.mitre.org/techniques/T1594)).(Citation: DNS Dumpster) Gathering this information may reveal opportunities for other forms of reconnaissance (ex: [Search Open Technical Databases](https://attack.mitre.org/techniques/T1596) or [Search Open Websites/Domains](https://attack.mitre.org/techniques/T1593)), establishing operational resources (ex: [Acquire Infrastructure](https://attack.mitre.org/techniques/T1583) or [Compromise Infrastructure](https://attack.mitre.org/techniques/T1584)), and/or initial access (ex: [External Remote Services](https://attack.mitre.org/techniques/T1133)). | 02 October 2020 | enterprise-attack | Reconnaissance | Much of this activity may have a very high occurrence and associated false positive rate, as well as potentially taking place outside the visibility of the target organization, making detection difficult for defenders.
Detection efforts may be focused on related stages of the adversary lifecycle, such as during Initial Access. |
T1041 | Exfiltration Over C2 Channel | Adversaries may steal data by exfiltrating it over an existing command and control channel. Stolen data is encoded into the normal communications channel using the same protocol as command and control communications. | 31 May 2017 | enterprise-attack | Exfiltration | Analyze network data for uncommon data flows (e.g., a client sending significantly more data than it receives from a server). Processes utilizing the network that do not normally have network communication or have never been seen before are suspicious. Analyze packet contents to detect communications that do not follow the expected protocol behavior for the port that is being used. (Citation: University of Birmingham C2) |
T1048.003 | Exfiltration Over Alternative Protocol: Exfiltration Over Unencrypted Non-C2 Protocol | Adversaries may steal data by exfiltrating it over an un-encrypted network protocol other than that of the existing command and control channel. The data may also be sent to an alternate network location from the main command and control server.(Citation: copy_cmd_cisco)
Adversaries may opt to obfuscate this data, without the use of encryption, within network protocols that are natively unencrypted (such as HTTP, FTP, or DNS). This may include custom or publicly available encoding/compression algorithms (such as base64) as well as embedding data within protocol headers and fields. | 15 March 2020 | enterprise-attack | Exfiltration | Analyze network data for uncommon data flows (e.g., a client sending significantly more data than it receives from a server). Processes utilizing the network that do not normally have network communication or have never been seen before are suspicious. Analyze packet contents to detect communications that do not follow the expected protocol behavior for the port that is being used. (Citation: University of Birmingham C2)
For network infrastructure devices, collect AAA logging to monitor for `copy` commands being run to exfiltrate configuration files to non-standard destinations over unencrypted protocols such as TFTP. |
T1592.001 | Gather Victim Host Information: Hardware | Adversaries may gather information about the victim's host hardware that can be used during targeting. Information about hardware infrastructure may include a variety of details such as types and versions on specific hosts, as well as the presence of additional components that might be indicative of added defensive protections (ex: card/biometric readers, dedicated encryption hardware, etc.).
Adversaries may gather this information in various ways, such as direct collection actions via [Active Scanning](https://attack.mitre.org/techniques/T1595) (ex: hostnames, server banners, user agent strings) or [Phishing for Information](https://attack.mitre.org/techniques/T1598). Adversaries may also compromise sites then include malicious content designed to collect host information from visitors.(Citation: ATT ScanBox) Information about the hardware infrastructure may also be exposed to adversaries via online or other accessible data sets (ex: job postings, network maps, assessment reports, resumes, or purchase invoices). Gathering this information may reveal opportunities for other forms of reconnaissance (ex: [Search Open Websites/Domains](https://attack.mitre.org/techniques/T1593) or [Search Open Technical Databases](https://attack.mitre.org/techniques/T1596)), establishing operational resources (ex: [Develop Capabilities](https://attack.mitre.org/techniques/T1587) or [Obtain Capabilities](https://attack.mitre.org/techniques/T1588)), and/or initial access (ex: [Compromise Hardware Supply Chain](https://attack.mitre.org/techniques/T1195/003) or [Hardware Additions](https://attack.mitre.org/techniques/T1200)). | 02 October 2020 | enterprise-attack | Reconnaissance | Internet scanners may be used to look for patterns associated with malicious content designed to collect host hardware information from visitors.(Citation: ThreatConnect Infrastructure Dec 2020)(Citation: ATT ScanBox)
Much of this activity may have a very high occurrence and associated false positive rate, as well as potentially taking place outside the visibility of the target organization, making detection difficult for defenders. Detection efforts may be focused on related stages of the adversary lifecycle, such as during Initial Access. |
T1568.002 | Dynamic Resolution: Domain Generation Algorithms | Adversaries may make use of Domain Generation Algorithms (DGAs) to dynamically identify a destination domain for command and control traffic rather than relying on a list of static IP addresses or domains. This has the advantage of making it much harder for defenders to block, track, or take over the command and control channel, as there potentially could be thousands of domains that malware can check for instructions.(Citation: Cybereason Dissecting DGAs)(Citation: Cisco Umbrella DGA)(Citation: Unit 42 DGA Feb 2019)
DGAs can take the form of apparently random or “gibberish” strings (ex: istgmxdejdnxuyla.ru) when they construct domain names by generating each letter. Alternatively, some DGAs employ whole words as the unit by concatenating words together instead of letters (ex: cityjulydish.net). Many DGAs are time-based, generating a different domain for each time period (hourly, daily, monthly, etc). Others incorporate a seed value as well to make predicting future domains more difficult for defenders.(Citation: Cybereason Dissecting DGAs)(Citation: Cisco Umbrella DGA)(Citation: Talos CCleanup 2017)(Citation: Akamai DGA Mitigation)
Adversaries may use DGAs for the purpose of [Fallback Channels](https://attack.mitre.org/techniques/T1008). When contact is lost with the primary command and control server malware may employ a DGA as a means to reestablishing command and control.(Citation: Talos CCleanup 2017)(Citation: FireEye POSHSPY April 2017)(Citation: ESET Sednit 2017 Activity) | 10 March 2020 | enterprise-attack | Command and Control | Detecting dynamically generated domains can be challenging due to the number of different DGA algorithms, constantly evolving malware families, and the increasing complexity of the algorithms. There is a myriad of approaches for detecting a pseudo-randomly generated domain name, including using frequency analysis, Markov chains, entropy, proportion of dictionary words, ratio of vowels to other characters, and more.(Citation: Data Driven Security DGA) CDN domains may trigger these detections due to the format of their domain names. In addition to detecting a DGA domain based on the name, another more general approach for detecting a suspicious domain is to check for recently registered names or for rarely visited domains.
Machine learning approaches to detecting DGA domains have been developed and have seen success in applications. One approach is to use N-Gram methods to determine a randomness score for strings used in the domain name. If the randomness score is high, and the domains are not whitelisted (CDN, etc), then it may be determined if a domain is related to a legitimate host or DGA.(Citation: Pace University Detecting DGA May 2017) Another approach is to use deep learning to classify domains as DGA-generated.(Citation: Elastic Predicting DGA) |