splunk_escuAnomaly

Linux Auditd Data Transfer Size Limits Via Split

The following analytic detects suspicious data transfer activities that involve the use of the `split` syscall, potentially indicating an attempt to evade detection by breaking large files into smaller parts. Attackers may use this technique to bypass size-based security controls, facilitating the covert exfiltration of sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall, this analytic helps identify potential data exfiltration attempts, allowing security teams to intervene and prevent the unauthorized transfer of critical information from the network.

MITRE ATT&CK

T1030

Detection Query

`linux_auditd` execve_command = "*split*" AND execve_command = "*-b *"
  | rename host as dest
  | rename comm as process_name
  | rename exe as process
  | stats count min(_time) as firstTime max(_time) as lastTime
    BY argc execve_command dest
  | `security_content_ctime(firstTime)`
  | `security_content_ctime(lastTime)`
  | `linux_auditd_data_transfer_size_limits_via_split_filter`

Author

Teoderick Contreras, Splunk

Created

2026-03-10

Data Sources

Linux Auditd Execve

References

https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html

Tags

Linux Living Off The LandLinux Privilege EscalationLinux Persistence TechniquesCompromised Linux HostHellcat Ransomware

Raw Content

name: Linux Auditd Data Transfer Size Limits Via Split
id: 4669561d-3bbd-44e3-857c-0e3c6ef2120c
version: 8
date: '2026-03-10'
author: Teoderick Contreras, Splunk
status: production
type: Anomaly
description: The following analytic detects suspicious data transfer activities that involve the use of the `split` syscall, potentially indicating an attempt to evade detection by breaking large files into smaller parts. Attackers may use this technique to bypass size-based security controls, facilitating the covert exfiltration of sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall, this analytic helps identify potential data exfiltration attempts, allowing security teams to intervene and prevent the unauthorized transfer of critical information from the network.
data_source:
    - Linux Auditd Execve
search: |-
    `linux_auditd` execve_command = "*split*" AND execve_command = "*-b *"
      | rename host as dest
      | rename comm as process_name
      | rename exe as process
      | stats count min(_time) as firstTime max(_time) as lastTime
        BY argc execve_command dest
      | `security_content_ctime(firstTime)`
      | `security_content_ctime(lastTime)`
      | `linux_auditd_data_transfer_size_limits_via_split_filter`
how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consists of 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.
references:
    - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
drilldown_searches:
    - name: View the detection results for - "$dest$"
      search: '%original_detection_search% | search  dest = "$dest$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$dest$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
rba:
    message: A [$execve_command$] event occurred on host - [$dest$] to split a file.
    risk_objects:
        - field: dest
          type: system
          score: 20
    threat_objects: []
tags:
    analytic_story:
        - Linux Living Off The Land
        - Linux Privilege Escalation
        - Linux Persistence Techniques
        - Compromised Linux Host
        - Hellcat Ransomware
    asset_type: Endpoint
    mitre_attack_id:
        - T1030
    product:
        - Splunk Enterprise
        - Splunk Enterprise Security
        - Splunk Cloud
    security_domain: endpoint
tests:
    - name: True Positive Test
      attack_data:
        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1030/linux_auditd_split_b_exec/auditd_execve_split.log
          source: auditd
          sourcetype: auditd