:warning: THIS IS A EXPERIMENTAL DETECTION

This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.

Try in Splunk Security Cloud

Description

This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts.

  • Type: Anomaly

  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2018-12-06
  • Author: Jose Hernandez, Splunk
  • ID: 6ed33786-5e87-4f55-b62c-cb5f1168b831

Annotations

ATT&CK
Kill Chain Phase
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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`stream_http` http_method=POST http_content_length>1 
| regex form_data="(?i)java\.lang\.(?:runtime
|processbuilder)" 
| rename src_ip as src 
| stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent by src, dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_java_classes_filter`

Macros

The SPL above uses the following Macros:

:information_source: suspicious_java_classes_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
  • http_method
  • http_content_length
  • src_ip
  • url
  • status
  • http_user_agent
  • src
  • dest

How To Implement

In order to properly run this search, Splunk needs to ingest data from your web-traffic appliances that serve or sit in the path of your Struts application servers. This can be accomplished by indexing data from a web proxy, or by using network traffic-analysis tools, such as Splunk Stream or Bro.

Known False Positives

There are no known false positives.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
25.0 50 50 tbd

: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: 1