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Description

The following analytic identifies potential exploitation of Windows Exchange servers via ProxyShell or ProxyNotShell vulnerabilities, followed by post-exploitation activities such as running nltest, Cobalt Strike, Mimikatz, and adding new users. It leverages data from multiple analytic stories, requiring at least five distinct sources to trigger, thus reducing noise. This activity is significant as it indicates a high likelihood of an active compromise, potentially leading to unauthorized access, privilege escalation, and persistent threats within the environment. If confirmed malicious, attackers could gain control over the Exchange server, exfiltrate data, and maintain long-term access.

  • Type: Correlation
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Risk
  • Last Updated: 2024-05-21
  • Author: Michael Haag, Splunk
  • ID: c32fab32-6aaf-492d-bfaf-acbed8e50cdf

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1190 Exploit Public-Facing Application Initial Access
T1133 External Remote Services Persistence, Initial Access
Kill Chain Phase
  • Delivery
  • Installation
NIST
  • DE.AE
CIS20
  • CIS 13
CVE 


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| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.analyticstories) as analyticstories values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count dc(All_Risk.analyticstories) as dc_analyticstories from datamodel=Risk.All_Risk where All_Risk.analyticstories IN ("ProxyNotShell","ProxyShell") OR (All_Risk.analyticstories IN ("ProxyNotShell","ProxyShell") AND All_Risk.analyticstories="Cobalt Strike") All_Risk.risk_object_type="system" by _time span=1h All_Risk.risk_object All_Risk.risk_object_type 
| `drop_dm_object_name(All_Risk)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| where source_count >=5 
| `proxyshell_proxynotshell_behavior_detected_filter`

Macros

The SPL above uses the following Macros:

:information_source: proxyshell_proxynotshell_behavior_detected_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.

  • All_Risk.analyticstories
  • All_Risk.risk_object_type
  • All_Risk.risk_object
  • All_Risk.annotations.mitre_attack.mitre_tactic
  • source

How To Implement

To implement this correlation, you will need to enable ProxyShell, ProxyNotShell and Cobalt Strike analytic stories (the anaytics themselves) and ensure proper data is being collected for Web and Endpoint datamodels. Run the correlation rule seperately to validate it is not triggering too much or generating incorrectly. Validate by running ProxyShell POC code and Cobalt Strike behavior.

Known False Positives

False positives will be limited, however tune or modify the query as needed.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
81.0 90 90 ProxyShell or ProxyNotShell activity has been identified on $risk_object$.

:information_source: 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

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: 2