: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 is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Web
  • Last Updated: 2017-09-23
  • Author: Bhavin Patel, Splunk
  • ID: c8bff7a4-11ea-4416-a27d-c5bca472913d

Annotations

ATT&CK
Kill Chain Phase
  • Delivery
NIST
  • ID.RA
  • PR.PT
  • PR.IP
  • DE.AE
  • PR.MA
  • DE.CM
CIS20
  • CIS 12
  • CIS 4
  • CIS 18
CVE
1
2
3
4
5
6
7
8
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest 
| search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 
| `drop_dm_object_name("Web")` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| table src, dest_ip, http_method, url, firstTime, lastTime 
| `detect_malicious_requests_to_exploit_jboss_servers_filter`

Macros

The SPL above uses the following Macros:

:information_source: detect_malicious_requests_to_exploit_jboss_servers_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
  • Web.http_method
  • Web.url
  • Web.url_length
  • Web.src
  • Web.dest

How To Implement

You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model

Known False Positives

No known false positives for this detection.

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