The following analytic detects potential exploitation attempts against Microsoft SharePoint Server vulnerability CVE-2023-29357. This vulnerability pertains to an elevation of privilege due to improper handling of authentication tokens. By monitoring for suspicious activities related to SharePoint Server, the analytic identifies attempts to exploit this vulnerability. If a true positive is detected, it indicates a serious security breach where an attacker might have gained privileged access to the SharePoint environment, potentially leading to data theft or other malicious activities.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Web
- Last Updated: 2023-09-27
- Author: Michael Haag, Gowthamaraj Rajendran, Splunk
- ID: fcf4bd3f-a79f-4b7a-83bf-2692d60b859d
Kill Chain Phase
- CIS 13
1 2 3 4 5 6 | tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/_api/web/siteusers*","/_api/web/currentuser*") Web.status=200 Web.http_method=GET by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `microsoft_sharepoint_server_elevation_of_privilege_filter`
The SPL above uses the following Macros:
microsoft_sharepoint_server_elevation_of_privilege_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
This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Microsoft SharePoint.
Known False Positives
False positives may occur if there are legitimate activities that mimic the exploitation pattern. It's recommended to review the context of the alerts and adjust the analytic parameters to better fit the specific environment.
Associated Analytic Story
|45.0||90||50||Possible exploitation of CVE-2023-29357 against $dest$ from $src$.|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
source | version: 1