Detection: Linux Auditd Shred Overwrite Command

Updated Date: 2024-09-04 ID: ce2bde4d-a1d4-4452-8c87-98440e5adfb3 Author: Teoderick Contreras, Splunk Type: TTP Product: Splunk Enterprise Security

Description

The following analytic detects the execution of the 'shred' command on a Linux machine, which is used to overwrite files to make them unrecoverable. It leverages data from Linux Auditd, focusing on process names and command-line arguments. This activity is significant because the 'shred' command can be used in destructive attacks, such as those seen in the Industroyer2 malware targeting energy facilities. If confirmed malicious, this activity could lead to the permanent destruction of critical files, severely impacting system integrity and data availability.

Search

1`linux_auditd` `linux_auditd_normalized_proctitle_process` 
2| rename host as dest 
3| where LIKE (process_exec, "%shred%") AND (LIKE (process_exec, "%-n%") OR LIKE (process_exec, "%-z%") OR LIKE (process_exec, "%-u%") OR LIKE (process_exec, "%-s%")) 
4| stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle normalized_proctitle_delimiter dest 
5| `security_content_ctime(firstTime)`
6| `security_content_ctime(lastTime)`
7| `linux_auditd_shred_overwrite_command_filter`

Data Source

Name	Platform	Sourcetype	Source	Supported App
Linux Auditd Proctitle	Linux	`'linux:audit'`	`'/var/log/audit/audit.log'`	N/A

Macros Used

Name	Value
linux_auditd	`sourcetype="linux:audit"`
linux_auditd_shred_overwrite_command_filter	`search *`

linux_auditd_shred_overwrite_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

To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed

Known False Positives

Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message	Risk Score	Impact	Confidence
A [$process_exec$] event occurred on host - [$dest$] to overwrite files using the shred utility.	81	90	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	`/var/log/audit/audit.log`	`linux:audit`
Integration	✅ Passing	Dataset	`/var/log/audit/audit.log`	`linux:audit`

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