Detection: ProxyShell ProxyNotShell Behavior Detected

Updated Date: 2024-11-15 ID: c32fab32-6aaf-492d-bfaf-acbed8e50cdf Author: Michael Haag, Splunk Type: Correlation Product: Splunk Enterprise Security

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.

Search

1
2| 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 
3| `drop_dm_object_name(All_Risk)` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)`
6| where source_count >=5 
7| `proxyshell_proxynotshell_behavior_detected_filter`

Data Source

No data sources specified for this detection.

Macros Used

Name	Value
security_content_ctime	`convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)`
proxyshell_proxynotshell_behavior_detected_filter	`search *`

proxyshell_proxynotshell_behavior_detected_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
T1190	Exploit Public-Facing Application	Initial Access
T1133	External Remote Services	Initial Access

Delivery

Installation

DE.AE

CIS 13

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	False

This configuration file applies to all detections of type Correlation. These correlations will generate Notable Events.

Implementation

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

References

Detection Testing

Test Type	Status	Dataset	Source	Sourcetype
Validation	✅ Passing	N/A	N/A	N/A
Unit	✅ Passing	Dataset	`proxyshell`	`stash`
Integration	✅ Passing	Dataset	`proxyshell`	`stash`

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: 4