splunk_escuAnomaly

MacOS Data Chunking

The following analytic detects suspicious data chunking activities that involve the use of split or dd, 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 these commands, 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

| tstats `security_content_summariesonly`
  count min(_time) as firstTime
        max(_time) as lastTime

from datamodel=Endpoint.Processes where

(
    Processes.process = "dd *"
     Processes.process = "* if=*"
)
OR
(
    Processes.process = "*split *"
    Processes.process="* -b *"
)

by Processes.dest Processes.original_file_name Processes.parent_process_id
   Processes.process Processes.process_exec Processes.process_guid
   Processes.process_hash Processes.process_id
   Processes.process_current_directory Processes.process_name
   Processes.process_path Processes.user
   Processes.user_id Processes.vendor_product

| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `macos_data_chunking_filter`

Author

Raven Tait, Splunk

Created

2026-04-15

Data Sources

Osquery Results

References

Tags

MacOS Post-Exploitation

Raw Content

name: MacOS Data Chunking
id: 7f1c8bed-9bd4-40b0-a1df-c262cbade0fc
version: 2
date: '2026-04-15'
author: Raven Tait, Splunk
status: production
type: Anomaly
description: |-
    The following analytic detects suspicious data chunking activities that involve the use of split or dd, 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 these commands, 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:
    - Osquery Results
search: |-
    | tstats `security_content_summariesonly`
      count min(_time) as firstTime
            max(_time) as lastTime

    from datamodel=Endpoint.Processes where

    (
        Processes.process = "dd *"
         Processes.process = "* if=*"
    )
    OR
    (
        Processes.process = "*split *"
        Processes.process="* -b *"
    )

    by Processes.dest Processes.original_file_name Processes.parent_process_id
       Processes.process Processes.process_exec Processes.process_guid
       Processes.process_hash Processes.process_id
       Processes.process_current_directory Processes.process_name
       Processes.process_path Processes.user
       Processes.user_id Processes.vendor_product

    | `drop_dm_object_name(Processes)`
    | `security_content_ctime(firstTime)`
    | `security_content_ctime(lastTime)`
    | `macos_data_chunking_filter`
how_to_implement: |-
    This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery.
    Also the [TA-OSquery](https://splunkbase.splunk.com/app/8574) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the data models.
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://osquery.readthedocs.io/en/stable/deployment/process-auditing/
    - https://ss64.com/mac/dd.html
    - https://ss64.com/mac/split.html
drilldown_searches:
    - name: View the detection results for - "$user$" and "$dest$"
      search: '%original_detection_search% | search  user = "$user$" dest = "$dest$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$user$" and "$dest$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") | 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: 7d
      latest_offset: "0"
rba:
    message: A file was split on $dest$ by $user$ via $process$
    risk_objects:
        - field: user
          type: user
          score: 20
        - field: dest
          type: system
          score: 20
    threat_objects:
        - field: process
          type: process
tags:
    analytic_story:
        - MacOS Post-Exploitation
    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/osquery_data_chunking/osquery.log
          source: osquery
          sourcetype: osquery:results