Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects potential remote shell behavior where a process named libgcrypt establishes an outbound connection on TCP port 9999 and spawns a shell (such as sh, bash, dash, zsh, or ash) within a short time window.
Detects potential remote shell behavior where a process named libgcrypt establishes an outbound connection on TCP port 9999 and spawns a shell (such as sh, bash, dash, zsh, or ash) within a short time window.
This detection identifies potential abuse of SSH "ProxyCommand" or "LocalCommand" by monitoring for suspicious process execution patterns.
Specifically, it looks for instances where ssh.exe (as a parent process) containing "ProxyCommand" or "LocalCommand" in its arguments spawns potentially malicious child processes like mshta, powershell, wscript, or cscript, or processes containing "http" in their command line.
This technique can be used by attackers to execute arbitrary commands through SSH proxy configurations, potentially enabling command & control activities or remote code execution. The detection focuses on commonly abused Windows scripting engines and web requests that may indicate malicious activity when spawned through SSH proxy commands.
Specifically, it looks for instances where ssh.exe (as a parent process) containing "ProxyCommand" or "LocalCommand" in its arguments spawns potentially malicious child processes like mshta, powershell, wscript, or cscript, or processes containing "http" in their command line.
This technique can be used by attackers to execute arbitrary commands through SSH proxy configurations, potentially enabling command & control activities or remote code execution. The detection focuses on commonly abused Windows scripting engines and web requests that may indicate malicious activity when spawned through SSH proxy commands.
The following analytic detects attempts to stop security-related services on an endpoint, which may indicate malicious activity. It leverages data from Endpoint Detection and Response (EDR) agents, specifically searching for processes involving the "sc.exe" or "net.exe" command with the "stop" parameter or the PowerShell "Stop-Service" cmdlet. This activity is significant because disabling security services can undermine the organization's security posture, potentially leading to unauthorized access, data exfiltration, or further attacks like malware installation or privilege escalation. If confirmed malicious, this behavior could compromise the endpoint and the entire network, necessitating immediate investigation and response.
The following analytic detects the execution of programs initiated by forfiles.exe. This command is typically used to run commands on multiple files, often within batch scripts. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where forfiles.exe is the parent process. This activity is significant because forfiles.exe can be exploited to bypass command line execution protections, making it a potential vector for malicious activity. If confirmed malicious, this could allow attackers to execute arbitrary commands, potentially leading to unauthorized access or further system compromise.
The following analytic detects the execution of curl commands with insecure flags within the Cisco Isovalent environment. It identifies this activity by monitoring process execution logs for curl commands that use the -k or --insecure flags. This behavior is significant for a SOC as it could allow an attacker to bypass SSL/TLS verification, potentially exposing the Kubernetes infrastructure to man-in-the-middle attacks. If confirmed malicious, this activity could lead to data interception, service disruptions, or unauthorized access to sensitive information.
The following analytic detects the execution of hh.exe (HTML Help) using InfoTech Storage Handlers to load Windows script code from a Compiled HTML Help (CHM) file. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because it can be used to execute malicious scripts embedded within CHM files, potentially leading to code execution. If confirmed malicious, this technique could allow an attacker to execute arbitrary code, escalate privileges, or persist within the environment.
The following analytic identifies the abuse of Regsvr32.exe to proxy execution of malicious code, specifically detecting the loading of "scrobj.dll" by Regsvr32.exe. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events and command-line executions. This activity is significant because Regsvr32.exe is a trusted, signed Microsoft binary, often used in "Squiblydoo" attacks to bypass application control mechanisms. If confirmed malicious, this technique could allow an attacker to execute arbitrary code, potentially leading to system compromise and persistent access.
The following analytic detects the usage of the SharpHound binary by identifying its original filename, `SharpHound.exe`, and the process name. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process metadata and command-line executions. SharpHound is a tool used for Active Directory enumeration, often by attackers during the reconnaissance phase. If confirmed malicious, this activity could allow an attacker to map out the network, identify high-value targets, and plan further attacks, potentially leading to privilege escalation and lateral movement within the environment.
The following analytic detects the execution of `powershell.exe` running the `Get-ADDefaultDomainPasswordPolicy` cmdlet, which is used to retrieve the password policy in a Windows domain.
This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions.
Monitoring this activity is crucial as it can indicate attempts by adversaries to gather information about domain policies for situational awareness and Active Directory discovery.
If confirmed malicious, this activity could lead to further reconnaissance and potential exploitation of domain security settings.
This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions.
Monitoring this activity is crucial as it can indicate attempts by adversaries to gather information about domain policies for situational awareness and Active Directory discovery.
If confirmed malicious, this activity could lead to further reconnaissance and potential exploitation of domain security settings.
The following analytic detects the execution of `powershell.exe` with command-line arguments used to enumerate domain users via the `Get-DomainUser` command. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions mapped to the `Processes` node of the `Endpoint` data model. This activity is significant as it indicates potential reconnaissance efforts by adversaries or Red Teams using PowerView for Active Directory discovery. If confirmed malicious, this could allow attackers to gain situational awareness and identify valuable targets within the domain, potentially leading to further exploitation.
The following analytic detects the execution of `powershell.exe` with the `Get-LocalUser` commandlet, which is used to query local user accounts. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. Monitoring this activity is significant because adversaries and Red Teams may use it to enumerate local users for situational awareness and Active Directory discovery. If confirmed malicious, this activity could allow attackers to identify potential targets for further exploitation or privilege escalation within the environment.
The following analytic detects the use of Impacket's `wmiexec.py` tool for lateral movement by identifying specific command-line parameters. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes spawned by `wmiprvse.exe` with command-line patterns indicative of Impacket usage. This activity is significant as Impacket tools are commonly used by adversaries for remote code execution and lateral movement within a network. If confirmed malicious, this could allow attackers to execute arbitrary commands on remote systems, potentially leading to further compromise and data exfiltration.
The following analytic detects suspicious file and directory discovery activities, which may indicate an attacker's effort to locate sensitive documents and files on a compromised system. This behavior often precedes data exfiltration, as adversaries seek to identify valuable or confidential information for theft. By identifying unusual or unauthorized attempts to browse or enumerate files and directories, this analytic helps security teams detect potential reconnaissance or preparatory actions by an attacker, enabling timely intervention to prevent data breaches or unauthorized access.
The following analytic detects the suspicious use of the whoami command, which may indicate an attacker trying to gather information about the current user account on a compromised system. The whoami command is commonly used to verify user privileges and identity, especially during initial stages of an attack to assess the level of access. By monitoring for unusual or unauthorized executions of whoami, this analytic helps in identifying potential reconnaissance activities, enabling security teams to take action before the attacker escalates privileges or conducts further malicious operations.
The following analytic detects the use of the curl command with specific switches (-F, --form, --upload-file, -T, -d, --data, --data-raw, -I, --head) to upload AWS credentials or configuration files to a remote destination. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process details. This activity is significant as it may indicate an attempt to exfiltrate sensitive AWS credentials, a technique known to be used by the TeamTNT group. If confirmed malicious, this could lead to unauthorized access and potential compromise of AWS resources.
The following analytic detects the use of the 'dd' command to overwrite files 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 adversaries often use the 'dd' command to destroy or irreversibly overwrite files, disrupting system availability and services. If confirmed malicious, this behavior could lead to data destruction, making recovery difficult and potentially causing significant operational disruptions.
The following analytic detects the behavior of decoding base64-encoded data and passing it to a Linux shell. Additionally, it mitigates the potential damage and protects the organization's systems and data.The detection is made by searching for specific commands in the Splunk query, namely "base64 -d" and "base64 --decode", within the Endpoint.Processes data model. The analytic also includes a filter for Linux shells. The detection is important because it indicates the presence of malicious activity since Base64 encoding is commonly used to obfuscate malicious commands or payloads, and decoding it can be a step in running those commands. It suggests that an attacker is attempting to run malicious commands on a Linux system to gain unauthorized access, for data exfiltration, or perform other malicious actions.
The following analytic detects the use of 'curl' and 'wget' commands within a Linux environment. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, user information, and command-line executions. This activity is significant as 'curl' and 'wget' are commonly used for downloading files, which can indicate potential ingress of malicious tools. If confirmed malicious, this activity could lead to unauthorized code execution, data exfiltration, or further compromise of the system. Monitoring and tuning this detection helps identify and differentiate between normal and potentially harmful usage.
The following 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 identifies potential enumeration of local network configuration on Linux systems.
It detects this activity by monitoring processes such as "arp," "ifconfig," "ip," "netstat," "firewall-cmd," "ufw," "iptables," "ss," and "route" within a 30-minute window.
This behavior is significant as it often indicates reconnaissance efforts by adversaries to gather network information for subsequent attacks.
If confirmed malicious, this activity could enable attackers to map the network, identify vulnerabilities, and plan further exploitation or lateral movement within the environment.
It detects this activity by monitoring processes such as "arp," "ifconfig," "ip," "netstat," "firewall-cmd," "ufw," "iptables," "ss," and "route" within a 30-minute window.
This behavior is significant as it often indicates reconnaissance efforts by adversaries to gather network information for subsequent attacks.
If confirmed malicious, this activity could enable attackers to map the network, identify vulnerabilities, and plan further exploitation or lateral movement within the environment.
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