Linux Medusa Rootkit
Description
This detection identifies file creation events associated with the installation of the Medusa rootkit, a userland LD_PRELOAD-based rootkit known for deploying shared objects, loader binaries, and configuration files into specific system directories. These files typically facilitate process hiding, credential theft, and backdoor access. Monitoring for such file creation patterns enables early detection of rootkit deployment before full compromise.
Query · spl
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem
WHERE Filesystem.file_path IN ("*/lib/libseconf", "*.backup_ld.so", "*.boot.sh", "*.logpam", "*sshpass.txt", "*sshpass2.txt", "*/lib/libdsx.so", "*rkload", "*/lib/libseconf/local.txt", "*/lib/locate/local.txt", "*/var/log/remote.txt", "*/lib/libseconf/.pts", "*/lib/locate /.pts", "*/libseconf/.ports")
BY Filesystem.action Filesystem.dest Filesystem.file_access_time
Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time
Filesystem.file_name Filesystem.file_path Filesystem.file_acl
Filesystem.file_size Filesystem.process_guid Filesystem.process_id
Filesystem.user Filesystem.vendor_product
| `drop_dm_object_name(Filesystem)`
| `security_content_ctime(lastTime)`
| `security_content_ctime(firstTime)`
| `linux_medusa_rootkit_filter`
Implementation guide
The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the Processes node of the Endpoint data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
Known false positives
- Little to no false positives in most environments. Tune as needed.
Analyst notes
Known false positives: Little to no false positives in most environments. Tune as needed.