Detection: Linux Node Privilege Escalation

Updated Date: 2024-08-14 ID: 2e58a4ff-398f-42f4-8fd0-e01ebfe2a8ce Author: Gowthamaraj Rajendran, Splunk Type: Anomaly Product: Splunk Enterprise Security

Description

The following analytic identifies the execution of Node.js with elevated privileges using sudo, specifically when spawning child processes. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include specific Node.js commands. This activity is significant because running Node.js as a superuser without dropping privileges can allow unauthorized access to the file system and potential privilege escalation. If confirmed malicious, this could enable an attacker to maintain privileged access, execute arbitrary code, and compromise sensitive data within the environment.

Search

1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*sudo*node*" AND Processes.process="*-e*" AND Processes.process="*child_process.spawn*" AND Processes.process="*stdio*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid 
3| `drop_dm_object_name(Processes)` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| `linux_node_privilege_escalation_filter`

Data Source

Name	Platform	Sourcetype	Source	Supported App
Sysmon for Linux EventID 1	Linux	`'sysmon:linux'`	`'Syslog:Linux-Sysmon/Operational'`	N/A

Macros Used

Name	Value
security_content_ctime	`convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)`
linux_node_privilege_escalation_filter	`search *`

linux_node_privilege_escalation_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

ID	Technique	Tactic
T1548.003	Sudo and Sudo Caching	Defense Evasion
T1548	Abuse Elevation Control Mechanism	Privilege Escalation

KillChainPhase.EXPLOITAITON

NistCategory.DE_AE

Cis18Value.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 Risk Event	True

This configuration file applies to all detections of type anomaly. These detections will use Risk Based Alerting.

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

False positives are present based on automated tooling or system administrative usage. Filter as needed.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message	Risk Score	Impact	Confidence
An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$	40	80	50

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

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: 3