ID | Technique | Tactic |
---|---|---|
T1033 | System Owner/User Discovery | Discovery |
Detection: Linux Auditd Whoami User Discovery
Description
The following analytic detects the suspicious use of the whoami command, which may indicate an attacker trying to gather information about the current user account on a compromised system. The whoami command is commonly used to verify user privileges and identity, especially during initial stages of an attack to assess the level of access. By monitoring for unusual or unauthorized executions of whoami, this analytic helps in identifying potential reconnaissance activities, enabling security teams to take action before the attacker escalates privileges or conducts further malicious operations.
Search
1`linux_auditd` type=SYSCALL comm=whoami OR exe= "*/whoami"
2| rename host as dest
3| stats count min(_time) as firstTime max(_time) as lastTime by comm exe SYSCALL UID ppid pid dest success
4| `security_content_ctime(firstTime)`
5| `security_content_ctime(lastTime)`
6| `linux_auditd_whoami_user_discovery_filter`
Data Source
Name | Platform | Sourcetype | Source | Supported App |
---|---|---|---|---|
Linux Auditd Syscall | Linux | 'linux:audit' |
'/var/log/audit/audit.log' |
N/A |
Macros Used
Name | Value |
---|---|
linux_auditd | sourcetype="linux:audit" |
linux_auditd_whoami_user_discovery_filter | search * |
linux_auditd_whoami_user_discovery_filter
is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Annotations
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 |
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 SYSCALL - [$comm$] event was executed on host - [$dest$] to discover virtual disk files and directories. | 25 | 50 | 50 |
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