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Description

The following query identifies suspicious .aspx created in 3 paths identified by Microsoft as known drop locations for Exchange exploitation related to HAFNIUM group and recently disclosed vulnerablity named ProxyShell. Paths include: \HttpProxy\owa\auth\, \inetpub\wwwroot\aspnet_client\, and \HttpProxy\OAB\. Upon triage, the suspicious .aspx file will likely look obvious on the surface. inspect the contents for script code inside. Identify additional log sources, IIS included, to review source and other potential exploitation. It is often the case that a particular threat is only applicable to a specific subset of systems in your environment. Typically analytics to detect those threats are written without the benefit of being able to only target those systems as well. Writing analytics against all systems when those behaviors are limited to identifiable subsets of those systems is suboptimal. Consider the case ProxyShell vulnerability on Microsoft Exchange Servers. With asset information, a hunter can limit their analytics to systems that have been identified as Exchange servers. A hunter may start with the theory that the exchange server is communicating with new systems that it has not previously. If this theory is run against all publicly facing systems, the amount of noise it will generate will likely render this theory untenable. However, using the asset information to limit this analytic to just the Exchange servers will reduce the noise allowing the hunter to focus only on the systems where this behavioral change is relevant.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Endpoint
  • Last Updated: 2021-10-05
  • Author: Michael Haag, Shannon Davis, David Dorsey, Splunk
  • ID: 8c14eeee-2af1-4a4b-bda8-228da0f4862a

ATT&CK

ID Technique Tactic
T1505 Server Software Component Persistence
T1505.003 Web Shell Persistence

| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=System  by _time span=1h Processes.process_id Processes.process_name Processes.dest 
| `drop_dm_object_name(Processes)` 
| join process_guid, _time [
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\HttpProxy\\owa\\auth\\*", "*\\inetpub\\wwwroot\\aspnet_client\\*", "*\\HttpProxy\\OAB\\*") Filesystem.file_name="*.aspx" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path 
| `drop_dm_object_name(Filesystem)` 
| fields _time dest file_create_time file_name file_path process_name process_path process] 
| dedup file_create_time 
| table dest file_create_time, file_name, file_path, process_name 
| `detect_exchange_web_shell_filter`

Associated Analytic Story

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node and Filesystem node.

Required field

  • _time
  • Filesystem.file_path
  • Filesystem.process_id
  • Filesystem.file_name
  • Filesystem.file_hash
  • Filesystem.user

Kill Chain Phase

  • Exploitation

Known False Positives

The query is structured in a way that action (read, create) is not defined. Review the results of this query, filter, and tune as necessary. It may be necessary to generate this query specific to your endpoint product.

RBA

Risk Score Impact Confidence Message
81.0 90 90 A file - $file_name$ was written to disk that is related to IIS exploitation previously performed by HAFNIUM. Review further file modifications on endpoint $dest$ by user $user$.

Reference

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