Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects the execution of specific 'net group' and 'net localgroup' commands that are commonly used for Active Directory enumeration. These commands are often leveraged by adversaries to discover domain-level groups, local administrator groups, and domain administrator groups, which can help them identify privileged accounts and plan further attacks. The rule is designed to flag multiple occurrences of these commands within a 15-minute window on a single host, as a single execution might be benign, but repeated executions suggest reconnaissance activity. The detection logic specifically looks for 'net localgroup administrators', 'net group /dom', and 'net group Domain Admins /dom', optionally preceded by 'cmd /c'.
This rule identifies process creations or file creations where the associated file has been tagged as an 'exploit' by VirusTotal. It leverages enrichment from a VirusTotal integration to flag potentially malicious activity related to exploit execution.
This rule detects the launch of processes whose executable files are identified as malicious by Google Safe Browsing and exhibit low prevalence within the environment. Specifically, it looks for process launch events where the SHA256 hash of the executable matches a hash flagged as 'CRITICAL' severity by Google Safe Browsing. Additionally, the rule filters for executables that have been observed on a maximum of 3 distinct machines over a 10-day rolling period, indicating low prevalence and a higher likelihood of being a targeted or novel threat.
Detects the creation of a remote thread in sensitive processes indicative of code injection techniques, such as APC or thread injection.
Executable downloaded by Microsoft Excel by user with GCP entity relationship
The following analytic detects suspicious use of the `rmmod` utility for kernel module removal, which may indicate an attacker attempt to unload critical or security-related kernel modules. The `rmmod` command is used to remove modules from the Linux kernel, and unauthorized use can be a tactic to disable security features, conceal malicious activities, or disrupt system operations. By monitoring for unusual or unauthorized `rmmod` activity, this analytic helps identify potential tampering with kernel modules, enabling security teams to take proactive measures to protect system integrity and security.
The following analytic identifies processes referencing plist files that determine which applications are re-opened when a user reboots their MacOS machine. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent processes related to "com.apple.loginwindow." This activity is significant because it can indicate attempts to persist across reboots, a common tactic used by attackers to maintain access. If confirmed malicious, this could allow an attacker to execute code or maintain persistence on the affected system, potentially leading to further compromise.
The analytic detects `spoolsv.exe` writing a `.dll` file, which is unusual behavior and may indicate exploitation of vulnerabilities like CVE-2021-34527 (PrintNightmare). This detection leverages the Endpoint datamodel, specifically monitoring process and filesystem events to identify `.dll` file creation within the `\spool\drivers\x64\` path. This activity is significant as it may signify an attacker attempting to execute malicious code via the Print Spooler service. If confirmed malicious, this could lead to unauthorized code execution and potential system compromise. Immediate endpoint isolation and further investigation are recommended.
The following analytic identifies processes running from newly seen paths within a Kubernetes environment. It leverages process metrics collected via an OTEL collector and hostmetrics receiver, and data is pulled from Splunk Observability Cloud using the Splunk Infrastructure Monitoring Add-on. This detection compares processes observed in the last hour with those seen over the previous 30 days. This activity is significant as it may indicate unauthorized changes, compromised nodes, or the introduction of malicious software. If confirmed malicious, it could lead to unauthorized process execution, control over critical resources, data exfiltration, privilege escalation, or malware introduction within the Kubernetes cluster.
The following analytic identifies the execution of common Linux processes used for elevation control, such as `chmod`, `chown`, and `setuid`. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because these processes are often abused by adversaries to gain persistence or escalate privileges on compromised hosts. If confirmed malicious, this behavior could allow attackers to modify file attributes, change file ownership, or set user IDs, potentially leading to unauthorized access and control over critical system resources.
This analytic identifies a process attempting to delete its own executable file using `cmd.exe` and the `del` command. This behavior is often associated with defense evasion techniques employed by malware, such as Clop ransomware, to remove its executable file after execution or under specific conditions. Detecting such activity is crucial for identifying malicious actors trying to evade detection and complicate incident response efforts.
The analytic identifies shell activity (sh, bash, csh, tcsh) within the Kubernetes privilege scope on a worker node. It leverages process metrics from an OTEL collector hostmetrics receiver, specifically process.cpu.utilization and process.memory.utilization, pulled from Splunk Observability Cloud. This activity is significant as unauthorized shell processes can indicate potential security threats, providing attackers an entry point to compromise the node and the entire Kubernetes cluster. If confirmed malicious, this activity could lead to data theft, service disruption, privilege escalation, lateral movement, and further attacks, severely compromising the cluster's security and integrity.
The analytic detects the execution of the 'apt-get' command with elevated privileges using 'sudo' on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it indicates a user may be attempting to escalate privileges to root, which could lead to unauthorized system control. If confirmed malicious, an attacker could gain root access, allowing them to execute arbitrary commands, install or remove software, and potentially compromise the entire system.
This analytic detects suspicious activity involving named pipes, specifically focusing on processes known to be abused by Qakbot. It identifies the creation or connection to randomly generated named pipes in GUID format, which is a characteristic behavior of Qakbot malware for inter-process communication after code injection. This activity is indicative of a potential Qakbot infection, which could lead to unauthorized data access and exfiltration.
The analytic identifies shell activity within the Kubernetes privilege scope on a worker node, specifically when shell processes are consuming CPU resources. It leverages process metrics from an OTEL collector hostmetrics receiver, pulled from Splunk Observability Cloud via the Splunk Infrastructure Monitoring Add-on, focusing on process.cpu.utilization and process.memory.utilization. This activity is significant as unauthorized shell processes can indicate a security threat, potentially compromising the node and the entire Kubernetes cluster. If confirmed malicious, attackers could gain full control over the host's resources, leading to data theft, service disruption, privilege escalation, and further attacks within the cluster.
The analytic identifies the use of the 'kmod' process to list kernel modules on a Linux system. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. While listing kernel modules is not inherently malicious, it can be a precursor to loading unauthorized modules using 'insmod'. If confirmed malicious, this activity could allow an attacker to load kernel modules, potentially leading to privilege escalation, persistence, or other malicious actions within the system.
The following analytic detects an anomaly where an svchost.exe process is spawned by a parent process other than the standard services.exe. In a typical Windows environment, svchost.exe is a system process that hosts Windows service DLLs, and is expected to be a child of services.exe. A process deviation from this hierarchy may indicate suspicious behavior, such as malicious code attempting to masquerade as a legitimate system process or evade detection. It is essential to investigate the parent process and associated behavior for further signs of compromise or unauthorized activity.
The analytic detects the Java process writing a .jsp file to disk, which may indicate a web shell being deployed. This activity is significant because web shells can provide attackers with remote control over the compromised server, leading to further exploitation. If confirmed malicious, this could allow unauthorized access, data exfiltration, or further compromise of the affected system, posing a severe security risk.
The following analytic identifies a non-lsass.exe process making an outbound connection on port 88, which is typically used by the Kerberos authentication protocol. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and network traffic logs. This activity is significant because, under normal circumstances, only the lsass.exe process should interact with the Kerberos Distribution Center. If confirmed malicious, this behavior could indicate an adversary attempting to abuse the Kerberos protocol, potentially leading to unauthorized access or lateral movement within the network.
This analytic detects executables running from public or temporary locations that are communicating over Windows domain authentication ports/protocols such as LDAP (389), LDAPS (636), and Kerberos (88). It leverages network traffic data to identify processes originating from user-controlled directories. This activity is significant because legitimate applications rarely run from these locations and attempt domain authentication, making it a potential indicator of compromise. If confirmed malicious, attackers could leverage this to access domain resources, potentially leading to further exploitation and lateral movement within the network.
This detection rule identifies the execution of WinPEAS (Windows Privilege Escalation Awesome Script), a post-exploitation reconnaissance tool used to discover privilege escalation paths on Windows systems. WinPEAS performs a wide range of local enumeration checks, including service misconfigurations, permission issues, token privileges, and more. Its usage is commonly observed during red team assessments and by adversaries seeking to elevate privileges after gaining initial access. The rule looks for common WinPEAS executable names, specific command-line arguments used by WinPEAS for various enumeration tasks, and patterns indicative of downloading WinPEAS PowerShell scripts from GitHub.
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