Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects the execution of the BCP utility to export data from a Microsoft SQL Server database. Attackers may use this method to extract malware previously saved into a database column or table, or to exfiltrate sensitive data from the database.
KQL Query from file: Hunting VEILDrive C2
Detects the invocation of MS SQL transactions that are destructive towards table or database data, such as "DROP TABLE" or "DROP DATABASE".
This YARA rule detects the ChaosRAT backdoor by identifying specific byte sequences related to its network communication, system information gathering, and file download capabilities. The rule includes three distinct versions of these functionalities, indicating evolution or different variants of the malware. Detection relies on the presence of these unique byte patterns within executable files, specifically ELF files as indicated by the magic byte check.
Detects the execution of the hdiutil utility with the 'create' argument, indicating the creation of a disk image on macOS. This activity can be associated with data staging or exfiltration techniques.
Detects the execution of the BCP utility in order to export data from the database.
Attackers were seen saving their malware to a database column or table and then later extracting it via "bcp.exe" into a file.
Attackers were seen saving their malware to a database column or table and then later extracting it via "bcp.exe" into a file.
SPL Query from file: Beavertail Exfiltration over TCP
The following analytic identifies anomalous GetObject API activity in AWS, indicating potential data exfiltration attempts. It leverages AWS CloudTrail logs and uses the `anomalydetection` command to detect unusual patterns in the frequency of GetObject API calls by analyzing fields such as "count," "user_type," and "user_arn" within a 10-minute window. This activity is significant as it may indicate unauthorized data access or exfiltration from S3 buckets. If confirmed malicious, attackers could exfiltrate sensitive data, leading to data breaches and compliance violations.
Detects an executable initiating a network connection to "ngrok" tunneling domains.
Attackers were seen using this "ngrok" in order to store their second stage payloads and malware.
While communication with such domains can be legitimate, often times is a sign of either data exfiltration by malicious actors or additional download.
Attackers were seen using this "ngrok" in order to store their second stage payloads and malware.
While communication with such domains can be legitimate, often times is a sign of either data exfiltration by malicious actors or additional download.
The following analytic identifies anomalous network traffic volumes between Kubernetes workloads or between a workload and external sources. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares recent network metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the last hour with the average over the past 30 days to identify significant deviations. This activity is significant as unexpected spikes may indicate unauthorized data transfers or lateral movement. If confirmed malicious, it could lead to data exfiltration or compromise of additional services, potentially resulting in data breaches.
This analytic identifies anomalously high outbound network activity from processes within Kubernetes containerized workloads. It compares recent network metrics (TCP/UDP bytes, new sockets, packets) over the last hour against the average metrics from the past 30 days. Significant deviations may indicate data exfiltration, process modification, or container compromise, potentially leading to unauthorized data transfer or further attacks within the containerized environment.
The analytic identifies anomalous inbound network traffic volumes from processes within containerized workloads. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares recent metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the last hour with the average over the past 30 days. This activity is significant as it may indicate unauthorized data reception, potential breaches, vulnerability exploitation, or malware propagation. If confirmed malicious, it could lead to command and control installation, data integrity damage, container escape, and further environment compromise.
This analytic detects new UDP communication between a source and destination workload pair within a Kubernetes cluster. It uses Network Performance Monitoring (NPM) metrics from an OTEL collector via Splunk Observability Cloud. The detection compares network activity over the last hour with the past 30 days to identify new inter-workload communication. Such changes in network behavior can indicate potential security threats or anomalies, including unauthorized connections that may lead to data breaches, privilege escalation, lateral movement, or disruption of critical services.
This analytic identifies newly observed TCP communication between source and destination workloads within a Kubernetes cluster. It leverages Network Performance Monitoring metrics to compare recent network activity with historical data (past 30 days) to detect new inter-workload communications. Such new connections can indicate changes in application behavior or potential security threats, including unauthorized access, lateral movement, privilege escalation, data exfiltration, or disruption of critical services.
Detects the execution of the BCP utility in order to export data from the database.
Attackers were seen saving their malware to a database column or table and then later extracting it via "bcp.exe" into a file.
Attackers were seen saving their malware to a database column or table and then later extracting it via "bcp.exe" into a file.
Detects network connections to known malicious domains and IPs associated with the Discord Invite Loop Hole campaign, including payload hosting, C2, and data exfiltration via Discord webhooks.
Detects network connections to Cloudflared tunnels domains initiated by a process on the system.
Attackers can abuse that feature to establish a reverse shell or persistence on a machine.
Attackers can abuse that feature to establish a reverse shell or persistence on a machine.
Detects an executable in the Windows folder accessing github.com
Detects network connections to BTunnels domains initiated by a process on the system.
Attackers can abuse that feature to establish a reverse shell or persistence on a machine.
Attackers can abuse that feature to establish a reverse shell or persistence on a machine.
Detects network connections to Visual Studio Code tunnel domains initiated by a process on a system. Attackers can abuse that feature to establish a reverse shell or persistence on a machine.
Detects an executable initiating a network connection to "ngrok" domains.
Attackers were seen using this "ngrok" in order to store their second stage payloads and malware.
While communication with such domains can be legitimate, often times is a sign of either data exfiltration by malicious actors or additional download.
Attackers were seen using this "ngrok" in order to store their second stage payloads and malware.
While communication with such domains can be legitimate, often times is a sign of either data exfiltration by malicious actors or additional download.
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