This search is to detect an anomaly event of non-firefox process accessing the files in profile folder. This folder contains all the sqlite database of the firefox browser related to users login, history, cookies and etc. Most of the RAT, trojan spy as well as FIN7 jssloader try to parse the those sqlite database to collect information on the compromised host. This SACL Event (4663) need to be enabled to tthe firefox profile directory to be eable to use this. Since you monitoring this access to the folder a noise coming from firefox need to be filter and also sqlite db browser and explorer .exe to make this detection more stable.
- Type: Anomaly
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2021-09-15
- Author: Teoderick Contreras, Splunk
- ID: e6fc13b0-1609-11ec-b533-acde48001122
Kill Chain Phase
1 2 3 4 5 `wineventlog_security` EventCode=4663 NOT (process_name IN ("*\\firefox.exe", "*\\explorer.exe", "*sql*")) Object_Name="*\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles*" | stats count min(_time) as firstTime max(_time) as lastTime by Object_Name Object_Type process_name Access_Mask Accesses process_id EventCode dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `non_firefox_process_access_firefox_profile_dir_filter`
The SPL above uses the following Macros:
non_firefox_process_access_firefox_profile_dir_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
To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
Known False Positives
other browser not listed related to firefox may catch by this rule.
Associated Analytic Story
|35.0||50||70||a non firefox browser process $process_name$ accessing $Object_Name$|
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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