Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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The following analytic detects the deletion of Linux service unit configuration files by suspicious processes. It leverages Endpoint Detection and Response (EDR) telemetry, focusing on processes executing the 'rm' command targeting '.service' files. This activity is significant as it may indicate malware attempting to disable critical services or security products, a common defense evasion tactic. If confirmed malicious, this behavior could lead to service disruption, security tool incapacitation, or complete system compromise, severely impacting the integrity and availability of the affected Linux host.
The analytic detects the use of the 'make' command with elevated privileges to execute system commands as root, potentially leading to a root shell. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include 'make', '--eval', and 'sudo'. This activity is significant because it indicates a possible privilege escalation attempt, allowing a user to gain root access. If confirmed malicious, an attacker could achieve full control over the system, execute arbitrary commands, and compromise the entire environment.
The analytic detects suspicious access or modification of the sshd_config file on Linux systems. It focuses on command-line executions involving processes like "cat," "nano," "vim," and "vi" accessing the sshd_config file. Unauthorized changes to sshd_config can allow threat actors to redirect port connections or use unauthorized keys, potentially compromising the system, leading to unauthorized access, privilege escalation, or persistent backdoor access.
The following analytic detects the execution of a command to enable all SysRq functions on a Linux system, a technique associated with the AwfulShred malware. It leverages Endpoint Detection and Response (EDR) data to identify processes executing the command to pipe bitmask '1' to /proc/sys/kernel/sysrq. This activity is significant as it can indicate an attempt to manipulate kernel system requests, which is uncommon and potentially malicious. If confirmed, this could allow an attacker to reboot the system or perform other critical actions, leading to system instability or further compromise.
This analytic detects a sharp increase in the number of files written to a specific host. It leverages the Endpoint.Filesystem data model, focusing on 'created' actions and comparing current file write counts against historical averages and standard deviations. This activity is significant as a sudden spike in file writes can indicate malicious activities such as ransomware encryption or data exfiltration. If confirmed malicious, this behavior could lead to significant data loss, system compromise, or further propagation of malware within the network.
Detects the execution of known attacker tools, specifically those associated with the IronSharpPack toolset. This rule identifies process launches where common command and scripting interpreters (cmd.exe, python, ipy.exe, powershell.exe, pwsh.exe, wsl.exe) are used to execute scripts or binaries with names indicative of IronSharpPack components. The presence of these tools in an unauthorized environment suggests malicious activity, often related to offensive security operations, post-exploitation, or privilege escalation.
Detects programs that connect to known malware callback ports based on threat intelligence reports. This rule specifically targets Linux systems and filters out connections to common local IP ranges to reduce false positives.
This YARA rule detects the Linux AutoColor backdoor by identifying specific byte patterns related to its core functionalities. These functionalities include installing library implants, self-deletion mechanisms, executing local files, and network proxy communication. The rule specifically targets ELF (Executable and Linkable Format) files, indicating it's designed for Linux executables.
Detects the execution of known attacker tools, including but not limited to those in the IronSharpPack toolset. These tools are commonly used for offensive security operations and may indicate malicious activity if observed in unauthorized environments.
Detects programs that connect to known malware callback ports based on threat intelligence reports.
This YARA rule detects the Sshdinjector backdoor on Linux systems. It identifies specific byte patterns associated with the backdoor's functionalities, including killing and restarting daemons, network communication, reading the /etc/shadow file (for credential dumping), and listing running services.
Yara rule that detects AutoColor backdoor.
This rule detects the Linux GobRAT backdoor by identifying specific byte patterns associated with its functions for getting local IP addresses, MAC addresses, handling TCP network communication, and managing telnet tasks. The rule specifically targets ELF executable files.
This rule detects the Linux_Backdoor_WolfsBane malware based on specific byte patterns found in its code. The patterns correspond to functions for loading embedded libraries, decrypting embedded libraries, and removing the backdoor. The rule specifically targets ELF executables.
Yara rule that detects Sshdinjector backdoor.
Yara rule that detects GobRAT backdoor.
Yara rule that detects WolfsBane backdoor.
Detects the creation of Python path configuration files (.pth) within Python library folders on Linux systems. These files can be maliciously used for code execution and persistence, as modules referenced by them are executed at every Python startup (v3.5+), regardless of whether they are imported by the calling script. The rule specifically looks for files ending with '.pth' in typical Python site-packages directories.
Detects command line patterns associated with the use of Pnscan for sending and receiving binary data across the network. This behavior has been identified in a Linux malware campaign targeting Docker, Apache Hadoop, Redis, and Confluence and was previously used by the threat actor known as TeamTNT.
Detects command line patterns associated with the use of Pnscan for sending and receiving binary data across the network.
This behavior has been identified in a Linux malware campaign targeting Docker, Apache Hadoop, Redis, and Confluence and was previously used by the threat actor known as TeamTNT
This behavior has been identified in a Linux malware campaign targeting Docker, Apache Hadoop, Redis, and Confluence and was previously used by the threat actor known as TeamTNT
Detects instances where an attacker attempts to interact with or exploit a REDIS service running on an OpenCanary honeypot. This indicates reconnaissance or an attempt to gain unauthorized access or extract data from the emulated Redis service.
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