| ID | Technique | Tactic |
|---|---|---|
| T1014 | Rootkit | Defense Evasion |
| T1589.001 | Credentials | Reconnaissance |
Detection: 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.
Search
1
2| 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
3| `drop_dm_object_name(Filesystem)`
4| `security_content_ctime(lastTime)`
5| `security_content_ctime(firstTime)`
6| `linux_medusa_rootkit_filter`
Data Source
| Name | Platform | Sourcetype | Source |
|---|---|---|---|
| Sysmon for Linux EventID 11 |  Linux |
'sysmon:linux' |
'Syslog:Linux-Sysmon/Operational' |
Macros Used
| Name | Value |
|---|---|
| security_content_ctime | convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$) |
| linux_medusa_rootkit_filter | search * |
linux_medusa_rootkit_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Annotations
MITRE ATT&CK
Kill Chain Phases
NIST
CIS
Threat Actors
Exploitation
Reconnaissance
DE.CM
CIS 10
Default Configuration
This detection is configured by default in Splunk Enterprise Security to run with the following settings:
| Setting | Value |
|---|---|
| Disabled | true |
| Cron Schedule | 0 * * * * |
| Earliest Time | -70m@m |
| Latest Time | -10m@m |
| Schedule Window | auto |
| Creates Notable | Yes |
| Rule Title | %name% |
| Rule Description | %description% |
| Notable Event Fields | user, dest |
| Creates Risk Event | True |
This configuration file applies to all detections of type TTP. These detections will use Risk Based Alerting and generate Notable Events.
Implementation
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.
Associated Analytic Story
Risk Based Analytics (RBA)
Risk Message:
Medusa rootkit files were identified on endpoint $dest$.
| Risk Object | Risk Object Type | Risk Score | Threat Objects |
|---|---|---|---|
| dest | system | 62 | No Threat Objects |
References
Detection Testing
| Test Type | Status | Dataset | Source | Sourcetype |
|---|---|---|---|---|
| Validation | ✅ Passing | N/A | N/A | N/A |
| Unit | ✅ Passing | Dataset | Syslog:Linux-Sysmon/Operational |
sysmon:linux |
| Integration | ✅ Passing | Dataset | Syslog:Linux-Sysmon/Operational |
sysmon:linux |
Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI.
Alternatively you can replay a dataset into a Splunk Attack Range
Source: GitHub | Version: 1