Detection: Linux Data Destruction Command

Updated Date: 2024-05-27 ID: b11d3979-b2f7-411b-bb1a-bd00e642173b Author: Teoderick Contreras, Splunk Type: TTP Product: Splunk Enterprise Security

Description

The following analytic detects the execution of a Unix shell command designed to wipe root directories on a Linux host. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on the 'rm' command with force recursive deletion and the '--no-preserve-root' option. This activity is significant as it indicates potential data destruction attempts, often associated with malware like Awfulshred. If confirmed malicious, this behavior could lead to severe data loss, system instability, and compromised integrity of the affected Linux host. Immediate investigation and response are crucial to mitigate potential damage.

Search

1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "rm"  AND Processes.process IN ("* -rf*", "* -fr*") AND Processes.process = "* --no-preserve-root" 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| `linux_data_destruction_command_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_data_destruction_command_filter	`search *`

linux_data_destruction_command_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
T1485	Data Destruction	Impact

KillChainPhase.ACTIONS_ON_OBJECTIVES

NistCategory.DE_CM

Cis18Value.CIS_10

APT38

Gamaredon Group

LAPSUS$

Lazarus Group

Sandworm Team

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

unknown

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message	Risk Score	Impact	Confidence
a $process_name$ execute rm command with --no-preserve-root parmeter that can wipe root files in $dest$	90	100	90

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: 2