Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
587 detections
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Detects the execution of JavaScript or JSC files using the Node.js binary (node.exe). This activity can be suspicious as adversaries have been observed abusing Node.js to disguise malware, evade security defenses, and maintain persistence. While Node.js is commonly used, unusual occurrences of this activity in an environment warrant immediate investigation.
Detects potentially suspicious inline JavaScript execution using the Node.js binary. The rule specifically looks for the execution of 'node.exe' combined with command-line arguments that contain keywords indicative of malicious activity, such as 'http' (for network communication), 'execSync' or 'spawn' (for executing system commands), 'fs' (for file system operations), 'path', and 'zlib' (often used in unpacking or data manipulation). This combination suggests an attempt to execute arbitrary code or perform system interactions via Node.js.
Malicious NPM Package Version Download
Cortex XDR
This rule detects the download or installation of known malicious NPM package versions by monitoring process command-line arguments for specific package names and versions. It targets common package managers like npm, yarn, and pnpm, as well as node.exe, to identify instances where a compromised or intentionally malicious package is being introduced into a development or production environment.
Detection for shai-hulud supply chain attack. All npm packages. [Why not externaldata()? 'cause it is computationally expensive, and it may not run)
This analytic detects the configuration of TFTP services on Cisco IOS devices that could be used to exfiltrate sensitive configuration files. Threat actors like Static Tundra have been observed configuring TFTP servers to make device configuration files accessible for exfiltration after gaining initial access. The detection specifically looks for commands that expose critical configuration files such as startup-config, running-config, and other sensitive system information through TFTP. This activity is particularly concerning as it may represent an attempt to steal credentials, network topology information, and other sensitive data stored in device configurations.
The analytic detects when a Windows RDP client attempts to execute an RDP file from a temporary directory, downloads directory, or Outlook directories. This detection is significant as it can indicate an attempt for an adversary to deliver a .rdp file, which may be leveraged by attackers to control or exfiltrate data. If confirmed malicious, this activity could lead to unauthorized access, data theft, or further lateral movement within the network.
The analytic identifies the execution of the native mimikatz.exe binary on Windows systems, including instances where the binary is renamed. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and original file names. This activity is significant because Mimikatz is a widely used tool for extracting authentication credentials, posing a severe security risk. If confirmed malicious, this activity could allow attackers to obtain sensitive credentials, escalate privileges, and move laterally within the network, leading to potential data breaches and system compromise.
Detects Windows systems invoking rundll32.exe to execute DLLs associated with compromised npm package versions of eslint-config-prettier (10.1.6–10.1.9). These versions were published without corresponding GitHub commits and may initiate callbacks to FirebaseDotSu, indicating potential command-and-control activity.
Rundll32.exe is a built-in Windows utility that enables execution of DLL files by invoking specific functions exported from them. While it is used legitimately across various system operations, attackers frequently exploit it to execute malicious code because it is a trusted binary and less likely to trigger security alerts. This technique is recognized in the MITRE ATT and CK framework under the identifier T1218.011 as a method for defense evasion.
In typical usage scenarios, Node.js operates as a JavaScript runtime for developing applications and executing server-side scripts. It does not ordinarily engage with DLL files or initiate their execution through rundll32.exe.. When Node.js is observed invoking rundll32 to launch a DLL, this represents highly unusual behavior and may signal a compromise or malware deployment. Such activity warrants investigation, especially when linked to suspicious network callbacks or unverified software packages distributed through npm.
In typical usage scenarios, Node.js operates as a JavaScript runtime for developing applications and executing server-side scripts. It does not ordinarily engage with DLL files or initiate their execution through rundll32.exe.. When Node.js is observed invoking rundll32 to launch a DLL, this represents highly unusual behavior and may signal a compromise or malware deployment. Such activity warrants investigation, especially when linked to suspicious network callbacks or unverified software packages distributed through npm.
KQL Query
This analytic detects suspicious modifications to Unix shell configuration files, such as `.bashrc` or `.profile`, which may indicate an attempt to alter system behavior or gain unauthorized access. Unauthorized changes to these files can be used to execute malicious commands, escalate privileges, or hide malicious activities. By monitoring for unusual or unauthorized modifications to shell configuration files, this analytic helps identify potential security threats, allowing security teams to respond quickly and mitigate risks.
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 the suspicious auditd service stop. This behavior is critical for a SOC to monitor because it may indicate attempts to gain unauthorized access or maintain control over a system. Such actions could be signs of malicious activity. If confirmed, this could lead to serious consequences, including a compromised system, unauthorized access to sensitive data, or even a wider breach affecting the entire network. Detecting and responding to these signs early is essential to prevent potential security incidents.
The analytic detects cloud API calls executed by user roles that have not previously run these commands. It leverages the Change data model in Splunk to identify commands executed by users with the user_type of AssumedRole and a status of success. This activity is significant because new commands from different user roles can indicate potential malicious activity or unauthorized actions. If confirmed malicious, this behavior could lead to unauthorized access, data breaches, or other damaging outcomes by exploiting new or unmonitored commands within the cloud environment.
The analytic detects the creation of cloud compute instances using previously unseen image IDs. It leverages cloud infrastructure logs to identify new image IDs that have not been observed before. This activity is significant because it may indicate unauthorized or suspicious activity, such as the deployment of malicious payloads or unauthorized access to sensitive information. If confirmed malicious, this could lead to data breaches, unauthorized access, or further compromise of the cloud environment. Immediate investigation is required to determine the legitimacy of the instance creation and to mitigate potential threats.
The following analytic detects the suspicious sysmon service stop. This behavior is critical for a SOC to monitor because it may indicate attempts to gain unauthorized access or maintain control over a system. Such actions could be signs of malicious activity. If confirmed, this could lead to serious consequences, including a compromised system, unauthorized access to sensitive data, or even a wider breach affecting the entire network. Detecting and responding to these signs early is essential to prevent potential security incidents.
This analytic detects Lumma Stealer download attempts using Cisco Secure Firewall Intrusion Events. It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where Snort signatures with specific IDs have been triggered. If confirmed malicious, this behavior could indicate an active infection of Lumma Stealer.
This analytic aggregates and summarizes alerts from Microsoft Defender for Endpoint. It extracts details such as source, file name, severity, process command line, IP address, registry key, signature, description, and unique ID. The detection is not intended to identify new activity from raw data but rather to leverage existing Microsoft Defender for Endpoint alerts for correlation and risk-based alerting. It filters out alerts with a 'clean' verdict and dynamically maps MITRE techniques and assigns risk scores based on the alert's severity.
The following analytic identifies when a new device is enrolled on an Okta account. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity Cloud to detect the creation of new device enrollments. This activity is significant as it may indicate a legitimate user settinging up a new device or an adversary adding a device to maintain unauthorized access. If confirmed malicious, this could lead to potential account takeover, unauthorized access, and persistent control over the compromised Okta account. Monitoring this behavior is crucial for detecting and mitigating unauthorized access attempts.
This correlation rule identifies when a user exceeds a predefined risk threshold based on multiple suspicious Okta activities within a 24-hour period. It aggregates risk events from "Suspicious Okta Activity," "Okta Account Takeover," and "Okta MFA Exhaustion" analytic stories. The detection is significant as it highlights potentially compromised user accounts exhibiting multiple tactics, techniques, and procedures (TTPs). If confirmed malicious, this activity could indicate a serious security breach, allowing attackers to gain unauthorized access, escalate privileges, or persist within the environment.
The following analytic identifies suspicious command-line parameters associated with the use of Impacket's smbexec.py for lateral movement. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns indicative of Impacket tool usage. This activity is significant as both Red Teams and adversaries use Impacket for remote code execution and lateral movement. If confirmed malicious, this activity could allow attackers to execute commands on remote endpoints, potentially leading to unauthorized access, data exfiltration, or further compromise of the network.
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