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Description

The following analytic detects the execution of the sqlite3 command with elevated privileges, which can be exploited for privilege escalation. It leverages Endpoint Detection and Response (EDR) telemetry to identify instances where sqlite3 is used in conjunction with shell commands and sudo. This activity is significant because it indicates a potential attempt to gain root access, which could lead to full system compromise. If confirmed malicious, an attacker could execute arbitrary commands as root, leading to unauthorized access, data exfiltration, or further lateral movement within the network.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Endpoint
  • Last Updated: 2024-08-14
  • Author: Gowthamaraj Rajendran, Splunk
  • ID: ab75dbb7-c3ba-4689-9c1b-8d2717bdcba1

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1548.003 Sudo and Sudo Caching Privilege Escalation, Defense Evasion
T1548 Abuse Elevation Control Mechanism Privilege Escalation, Defense Evasion
Kill Chain Phase
  • Exploitation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*sqlite3*" AND Processes.process="*.shell*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `linux_sqlite3_privilege_escalation_filter`

Macros

The SPL above uses the following Macros:

:information_source: linux_sqlite3_privilege_escalation_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required fields

List of fields required to use this analytic.

  • _time
  • Processes.dest
  • Processes.user
  • Processes.parent_process_name
  • Processes.process_name
  • Processes.process
  • Processes.process_id
  • Processes.parent_process_id

How To Implement

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

False positives may be present, filter as needed.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
30.0 60 50 An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Reference

Test Dataset

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 | version: 3