Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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The following analytic detects the insertion of a Linux kernel module using the insmod utility. It leverages data from Linux Auditd, focusing on process execution logs that include process names and command-line details. This activity is significant as it may indicate the installation of a rootkit or malicious kernel module, potentially allowing an attacker to gain elevated privileges and bypass security detections. If confirmed malicious, this could lead to unauthorized code execution, persistent access, and severe compromise of the affected system.
The following analytic detects attempts to stop a service on Linux systems. It leverages data from Linux Auditd. This activity is significant as adversaries often stop or terminate security or critical services to disable defenses or disrupt operations, as seen in malware like Industroyer2. If confirmed malicious, this could lead to the disabling of security mechanisms, allowing attackers to persist, escalate privileges, or deploy destructive payloads, severely impacting system integrity and availability.
The following analytic detects suspicious system network configuration discovery activities, which may indicate an adversary's attempt to gather information about the network environment. Such actions typically involve commands or tools used to identify network interfaces, routing tables, and active connections. Detecting these activities is crucial, as they often precede more targeted attacks like lateral movement or data exfiltration. By identifying unusual or unauthorized network discovery efforts, this analytic helps security teams to swiftly detect and respond to potential reconnaissance operations, mitigating the risk of further compromise.
The following analytic detects the use of the Linux 'xclip' command to copy data from the clipboard. It leverages Endpoint Detection and Response (EDR) telemetry, focusing on process names and command-line arguments related to clipboard operations. This activity is significant because adversaries can exploit clipboard data to capture sensitive information such as passwords or IP addresses. If confirmed malicious, this technique could lead to unauthorized data exfiltration, compromising sensitive information and potentially aiding further attacks within the environment.
This detection identifies Docker containers that mount the host's root directory into the container filesystem.
Mounting the entire host root directory into a container effectively grants the container visibility and potential write access to all files on the host system.
If the container is running as root or with elevated capabilities (e.g., --privileged), the risk is significantly increased.
Mounting the entire host root directory into a container effectively grants the container visibility and potential write access to all files on the host system.
If the container is running as root or with elevated capabilities (e.g., --privileged), the risk is significantly increased.
The following analytic detects the use of the curl command with specific switches (-O, -sO, -ksO, --output) commonly used to download remote scripts or binaries. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant as it may indicate an attempt to download and execute potentially malicious files, often used in initial stages of an attack. If confirmed malicious, this could lead to unauthorized code execution, enabling attackers to compromise the system further.
The following analytic detects the insertion of a Linux kernel module using the insmod utility. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include process names and command-line details. This activity is significant as it may indicate the installation of a rootkit or malicious kernel module, potentially allowing an attacker to gain elevated privileges and bypass security detections. If confirmed malicious, this could lead to unauthorized code execution, persistent access, and severe compromise of the affected system.
Detects potential abuse of the Linux Magic SysRq (System Request) key by adversaries with root or sufficient privileges to manipulate or destabilize a system.
Writing to /proc/sysrq-trigger can crash the system, kill processes, or bypass standard logging.
Monitoring SysRq abuse helps detect stealthy post-exploitation activity.
Correlate with related EXECVE or PROCTITLE events to identify the process or user responsible for the access or modification.
Writing to /proc/sysrq-trigger can crash the system, kill processes, or bypass standard logging.
Monitoring SysRq abuse helps detect stealthy post-exploitation activity.
Correlate with related EXECVE or PROCTITLE events to identify the process or user responsible for the access or modification.
The following analytic detects the use of the base64 decode command on Linux systems, which is often used to deobfuscate files. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include "base64 -d" or "base64 --decode". This activity is significant as it may indicate an attempt to hide malicious payloads or scripts. If confirmed malicious, an attacker could use this technique to execute hidden code, potentially leading to unauthorized access, data exfiltration, or further system compromise.
The following analytic detects the execution of the SysReq hack to reboot a Linux system host. It leverages Endpoint Detection and Response (EDR) data to identify processes executing the command to pipe 'b' to /proc/sysrq-trigger. This activity is significant as it is an uncommon method to reboot a system and was observed in the Awfulshred malware wiper. If confirmed malicious, this technique could indicate the presence of suspicious processes and potential system compromise, leading to unauthorized reboots and disruption of services.
Detects anomalous process execution and loopback network activity associated with the Model Context Protocol (MCP) 'exec_in_session' method. This pattern indicates an adversary potentially abusing the AI coding assistant's MCP server capabilities to execute arbitrary commands as a C2 channel.
Detects suspicious post-exploitation behaviors on Citrix NetScaler appliances, specifically tracking the modification of file permissions to grant setuid/setgid bits to the shell and the termination of httpd processes often associated with webshell persistence or manipulation.
This rule detects the presence of specific SHA-256 file hashes associated with the 'VPN for X' malicious proxy extension family, which are known to be used to transform systems into nodes within a proxy network.
Detects the spawning of shells, interpreters, or system reconnaissance binaries by primary Citrix NetScaler ADC/Gateway processes (httpd, nsppe, aslearning). This behavior is strongly indicative of post-exploitation activity, such as remote command execution, often following the exploitation of vulnerabilities like CVE-2026-88771.
This rule monitors NetScaler ADC logs for indicators associated with the PitScaler campaign, specifically targeting exploitation of CVE-2026-88771 and CVE-2026-88772. It detects exploitation artifacts (DTLS handshake errors, NSPPE crashes), deployment of WHIPSHOT/SLAPSHOT malware, web shell installation, httpd.conf configuration tampering, persistence mechanisms involving privilege escalation (chmod), and connections to known malicious infrastructure.
Detects when an AI agent or assistant process attempts to access sensitive local configuration or credential files (e.g., .aws/credentials, .env, id_rsa, service account keys, .netrc) that typically fall outside the scope of its intended function. This behavior is indicative of unauthorized access, potentially due to prompt injection or malicious exploitation of the agent's file system permissions.
Detects browser extension updates that request a combination of high-risk permissions capable of account takeover (e.g., cookies, webRequest, identity) for extensions previously classified as low-risk. This pattern often indicates a supply chain compromise where a benign extension is acquired and subsequently updated with malicious capabilities.
This rule detects network communication with known malicious infrastructure (C2 IPs and URLs), presence of malicious files identified by SHA256 hashes on disk, and execution of known malicious files. It also correlates these activities with specific operator email addresses involved in the malicious campaign.
This rule identifies installations of TeamViewer software that are vulnerable to specific CVEs (CVE-2026-19743, CVE-2026-92368, CVE-2026-92369, CVE-2026-92370, CVE-2026-92371) by analyzing the 'DeviceTvmSoftwareInventory' dataset. It checks for versioning patterns against known vulnerable build numbers across Windows, Linux, and macOS platforms to pinpoint systems requiring patching.
This rule detects indicators of the PitScaler campaign targeting Citrix NetScaler ADC and Gateway appliances, exploiting vulnerabilities (CVE-2026-88771, CVE-2026-88772). It identifies the full attack chain including initial DTLS heap corruption exploitation, WHIPSHOT web shell C2 patterns, SLAPSHOT tunneling artifacts, web shell deployment files, httpd.conf PHP configuration tampering for persistence, privilege escalation via setuid /bin/sh modification, and anti-forensic log tampering.
Detects DNS lookups and outbound network connections to known command and control (C2) and staging domains associated with the STAC4924 campaign. The rule performs strict matching on domain names to ensure that subdomains are identified while avoiding false positives from partial string matches within URLs.
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