Linux Suspicious Redis Activity
Description
The following analytic detects Redis processes spawning a system shell command. This can indicate exploitation activity of a redis server to gain code execution.
Query · spl
| tstats `security_content_summariesonly`
count min(_time) as firstTime
max(_time) as lastTime
FROM datamodel=Endpoint.Processes WHERE
(
Processes.parent_process_name IN (
"redis-sentinel",
"redis-server"
)
OR
Processes.parent_process_path = "*/bin/redis*"
)
Processes.process_name IN (
"awk", "bash", "curl", "dash", "gawk", "id", "ifconfig",
"lua", "nc", "ncat", "netcat", "openssl", "perl", "php",
"python", "python2", "python3", "ruby", "sh", "socat",
"wget", "whoami", "zsh"
)
BY Processes.action Processes.dest Processes.original_file_name
Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid
Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path
Processes.process Processes.process_current_directory Processes.process_exec
Processes.process_guid Processes.process_hash Processes.process_id
Processes.process_integrity_level Processes.process_name Processes.process_path
Processes.user Processes.user_id Processes.vendor_product
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `linux_suspicious_redis_activity_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
- Legitimate administrative scripts or automated maintenance tasks may trigger this detection when managing Redis configurations or performing system operations. Filter based on known administrative activity and approved management tools.
Analyst notes
Known false positives: Legitimate administrative scripts or automated maintenance tasks may trigger this detection when managing Redis configurations or performing system operations. Filter based on known administrative activity and approved management tools.