FodHelper UAC Bypass
Description
The following analytic detects the execution of fodhelper.exe, which is known to exploit a User Account Control (UAC) bypass by leveraging specific registry keys. The detection method uses Endpoint Detection and Response (EDR) telemetry to identify when fodhelper.exe spawns a child process and accesses the registry keys. This activity is significant because it indicates a potential privilege escalation attempt by an attacker. If confirmed malicious, the attacker could execute commands with elevated privileges, leading to unauthorized system changes and potential full system compromise.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2024-05-15
- Author: Michael Haag, Splunk
- ID: 909f8fd8-7ac8-11eb-a1f3-acde48001122
Annotations
ATT&CK
Kill Chain Phase
- Exploitation
NIST
- DE.CM
CIS20
- CIS 10
CVE
Search
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| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=fodhelper.exe by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `fodhelper_uac_bypass_filter`
Macros
The SPL above uses the following Macros:
fodhelper_uac_bypass_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Required fields
List of fields required to use this analytic.
- _time
- Processes.parent_process_name
- Processes.dest
- Processes.user
- Processes.parent_process
- Processes.process_name
- Processes.process
- Processes.process_id
- Processes.parent_process_id
How To Implement
The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the Processes
node of the Endpoint
data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
Known False Positives
Limited to no false positives are expected.
Associated Analytic Story
RBA
Risk Score | Impact | Confidence | Message |
---|---|---|---|
81.0 | 90 | 90 | Suspicious registy keys added by process fodhelper.exe with a parent_process of $parent_process_name$ that has been executed on $dest$ by $user$. |
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
Reference
- https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/
- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1548.002/T1548.002.md
- https://github.com/gushmazuko/WinBypass/blob/master/FodhelperBypass.ps1
- https://attack.mitre.org/techniques/T1548/002/
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: 3