Detection: Windows AppLocker Privilege Escalation via Unauthorized Bypass

Updated Date: 2024-03-21 ID: bca48629-7fa2-40d3-9e5d-807564504e28 Author: Michael Haag, Splunk Type: TTP Product: Splunk Enterprise Security

Description

The following analytic utilizes Windows AppLocker event logs to identify attempts to bypass application restrictions. AppLocker is a feature that allows administrators to specify which applications are permitted to run on a system. This analytic is designed to identify attempts to bypass these restrictions, which could be indicative of an attacker attempting to escalate privileges. The analytic uses EventCodes 8007, 8004, 8022, 8025, 8029, and 8040 to identify these attempts. The analytic will identify the host, full file path, and target user associated with the bypass attempt. These EventCodes are related to block events and focus on 5 attempts or more.

1`applocker` EventCode IN (8007, 8004, 8022, 8025, 8029, 8040) 
2| spath input=UserData_Xml 
3| rename RuleAndFileData.* as *, Computer as dest, TargetUser AS user 
4| stats count AS attempt_count min(_time) as firstTime max(_time) as lastTime by dest, PolicyName, RuleId, user, TargetProcessId, FilePath, FullFilePath, EventCode 
5| where attempt_count > 5 
6| sort - attempt_count 
7| lookup applockereventcodes EventCode OUTPUT Description 
8| `windows_applocker_privilege_escalation_via_unauthorized_bypass_filter`

Data Source

Name Platform Sourcetype Source Supported App
N/A N/A N/A N/A N/A

Macros Used

Name Value
applocker (source="WinEventLog:Microsoft-Windows-AppLocker/*" OR source="XmlWinEventLog:Microsoft-Windows-AppLocker/*")
windows_applocker_privilege_escalation_via_unauthorized_bypass_filter search *
windows_applocker_privilege_escalation_via_unauthorized_bypass_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
T1218 System Binary Proxy Execution Defense Evasion
KillChainPhase.EXPLOITAITON
NistCategory.DE_CM
Cis18Value.CIS_10
Lazarus Group

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 Notable Yes
Rule Title %name%
Rule Description %description%
Notable Event Fields user, dest
Creates Risk Event True
This configuration file applies to all detections of type TTP. These detections will use Risk Based Alerting and generate Notable Events.

Implementation

The analytic is designed to be run against Windows AppLocker event logs collected from endpoints with AppLocker enabled. If using Microsoft Defender for Endpoint (MDE), modify the analytic to use EventTypes/ActionTypes that match the block events for AppLocker. The analytic requires the AppLocker event logs to be ingested into Splunk.

Known False Positives

False positives are possible if legitimate users are attempting to bypass application restrictions. This could occur if a user is attempting to run an application that is not permitted by AppLocker. It is recommended to investigate the context of the bypass attempt to determine if it is malicious or not. Modify the threshold as needed to reduce false positives.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
An attempt to bypass application restrictions was detected on a host $dest$. 64 80 80
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

References

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation ✅ Passing N/A N/A N/A
Unit ✅ Passing Dataset XmlWinEventLog:Microsoft-Windows-AppLocker/MSI and Script xmlwineventlog
Integration ✅ Passing Dataset XmlWinEventLog:Microsoft-Windows-AppLocker/MSI and Script xmlwineventlog

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: 2