Detect malicious requests to exploit JBoss servers
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.
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
NIST
- DE.CM
CIS20
- CIS 13
CVE
Search
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| 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:
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 |
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