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.

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
Linux Auditd Syscall Linux icon Linux 'linux:audit' '/var/log/audit/audit.log'

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

- MITRE ATT&CK
+ Kill Chain Phases
+ NIST
+ CIS
- Threat Actors
ID Technique Tactic
T1033 System Owner/User Discovery Discovery
KillChainPhase.EXPLOITAITON
NistCategory.DE_AE
Cis18Value.CIS_10
APT19
APT3
APT32
APT37
APT38
APT39
APT41
Aquatic Panda
Chimera
Dragonfly
Earth Lusca
FIN10
FIN7
FIN8
GALLIUM
Gamaredon Group
HAFNIUM
HEXANE
Ke3chang
Lazarus Group
LuminousMoth
Magic Hound
Moonstone Sleet
MuddyWater
OilRig
Patchwork
Sandworm Team
Sidewinder
Stealth Falcon
Threat Group-3390
Tropic Trooper
Volt Typhoon
Windshift
Winter Vivern
Wizard Spider
ZIRCONIUM

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

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
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: 2