Detection: MacOS Data Chunking

Updated Date: 2026-05-13 ID: 7f1c8bed-9bd4-40b0-a1df-c262cbade0fc Author: Raven Tait, Splunk Type: Anomaly Product: Splunk Enterprise Security

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.

Search

 1
 2| tstats `security_content_summariesonly`
 3  count min(_time) as firstTime
 4        max(_time) as lastTime
 5
 6from datamodel=Endpoint.Processes where
 7
 8(
 9    Processes.process = "dd *"
10     Processes.process = "* if=*"
11)
12OR
13(
14    Processes.process = "*split *"
15    Processes.process="* -b *"
16)
17
18by Processes.dest Processes.original_file_name Processes.parent_process_id
19   Processes.process Processes.process_exec Processes.process_guid
20   Processes.process_hash Processes.process_id
21   Processes.process_current_directory Processes.process_name
22   Processes.process_path Processes.user
23   Processes.user_id Processes.vendor_product
24
25
26| `drop_dm_object_name(Processes)`
27
28| `security_content_ctime(firstTime)`
29
30| `security_content_ctime(lastTime)`
31
32| `macos_data_chunking_filter`

Data Source

Name	Platform	Sourcetype	Source
Osquery Results	Other	`'osquery:results'`	`'osquery'`

Macros Used

Name	Value
security_content_ctime	`convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)`
macos_data_chunking_filter	`search *`

macos_data_chunking_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
T1030	Data Transfer Size Limits	Exfiltration

Actions on Objectives

DE.AE

CIS 10

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 Finding (Notable)	No
Creates Intermediate Finding (Risk Event)	Yes

Anomaly detections generate Intermediate Findings (Risk Events). They do not generate a Finding (Notable) directly.

Implementation

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 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.

Associated Analytic Story

MacOS Post-Exploitation

Intermediate Findings

Message	Entity Field	Entity Type	Risk Score
A file was split on $dest$ by $user$ via $process$	user	user	20
A file was split on $dest$ by $user$ via $process$	dest	system	20

Threat Objects

Field	Type
process	process

References

Detection Testing

Test Type	Status	Dataset	Source	Sourcetype
Validation	✅ Passing	N/A	N/A	N/A
Unit	✅ Passing	Dataset	`osquery`	`osquery:results`
Integration	✅ Passing	Dataset	`osquery`	`osquery:results`

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: 3