THIS IS A EXPERIMENTAL DETECTION
This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.
In Splunk Enterprise versions below 8.1.13, 8.2.10, and 9.0.4, when the INGEST\_EVAL parameter is improperly formatted, it crashes splunkd. This hunting search provides the user, timing and number of times the crashing command was executed.
- Type: TTP
- Product: Splunk Enterprise
- Datamodel: Splunk_Audit
- Last Updated: 2023-02-14
- Author: Chase Franklin, Rod Soto, Splunk
- ID: 08978eca-caff-44c1-84dc-53f17def4e14
Kill Chain Phase
- Actions On Objectives
- CIS 10
1 2 3 4 5 6 | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Splunk_Audit.Search_Activity where (Search_Activity.search="*makeresults*"AND Search_Activity.search="*ingestpreview*transforms*") Search_Activity.search_type=adhoc Search_Activity.search!="*splunk_improperly_formatted_parameter_crashes_splunkd_filter*" Search_Activity.user!=splunk-system-user by Search_Activity.search, Search_Activity.info, Search_Activity.total_run_time, Search_Activity.user, Search_Activity.search_type | `drop_dm_object_name(Search_Activity)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `splunk_improperly_formatted_parameter_crashes_splunkd_filter`
The SPL above uses the following Macros:
splunk_improperly_formatted_parameter_crashes_splunkd_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
List of fields required to use this analytic.
How To Implement
Requires access to audittrail and use of Splunk_Audit.Search_Activity datamodel.
Known False Positives
This is a hunting search it should be focused on affected products, otherwise it is likely to produce false positives.
Associated Analytic Story
|100.0||100||100||An attempt to exploit ingest eval parameter was detected from $user$|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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