3.8 KiB
title, excerpt, categories, last_modified_at, toc, toc_label, tags
| title | excerpt | categories | last_modified_at | toc | toc_label | tags | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Github Commit In Develop | Trusted Relationship |
|
2021-09-01 | true |
|
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Description
This search is to detect a pushed or commit to develop branch. This is to avoid unwanted modification to develop without a review to the changes. Ideally in terms of devsecops the changes made in a branch and do a PR for review. of course in some cases admin of the project may did a changes directly to master branch
- Type: Anomaly
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud, Dev Sec Ops Analytics
- Datamodel:
- Last Updated: 2021-09-01
- Author: Teoderick Contreras, Splunk
- ID: f3030cb6-0b02-11ec-8f22-acde48001122
ATT&CK
| ID | Technique | Tactic |
|---|---|---|
| T1199 | Trusted Relationship | Initial Access |
Search
`github` branches{}.name = main OR branches{}.name = develop
| stats count min(_time) as firstTime max(_time) as lastTime by commit.author.html_url commit.commit.author.email commit.author.login commit.commit.message repository.pushed_at commit.commit.committer.date
| eval phase="code"
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `github_commit_in_develop_filter`
Macros
The SPL above uses the following Macros:
Note that github_commit_in_develop_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Required field
- _time
How To Implement
To successfully implement this search, you need to be ingesting logs related to github logs having the fork, commit, push metadata that can be use to monitor the changes in a github project.
Known False Positives
admin can do changes directly to develop branch
Associated Analytic story
Kill Chain Phase
- Exploitation
RBA
| Risk Score | Impact | Confidence | Message |
|---|---|---|---|
| 9.0 | 30 | 30 | suspicious commit by commit.commit.author.email to develop branch |
Note that risk score is calculated base on the following formula: (Impact * Confidence)/100
Reference
Test Dataset
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 | version: 1