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Description

The following analytic identifies a Java user agent performing a GET request for a .class file from a remote site. It leverages web or proxy logs within the Web Datamodel to detect this activity. This behavior is significant as it may indicate exploitation attempts, such as those related to CVE-2021-44228 (Log4Shell). If confirmed malicious, an attacker could exploit vulnerabilities in the Java application, potentially leading to remote code execution and further compromise of the affected system.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Web
  • Last Updated: 2024-05-16
  • Author: Michael Haag, Splunk
  • ID: 8281ce42-5c50-11ec-82d2-acde48001122

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1190 Exploit Public-Facing Application Initial Access
Kill Chain Phase
  • Delivery
NIST
  • DE.CM
CIS20
  • CIS 13
CVE 


1
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| tstats count from datamodel=Web where Web.http_user_agent="*Java*" Web.http_method="GET" Web.url="*.class*" by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest 
| `drop_dm_object_name("Web")` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `java_class_file_download_by_java_user_agent_filter`

Macros

The SPL above uses the following Macros:

:information_source: java_class_file_download_by_java_user_agent_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
  • Web.http_user_agent

How To Implement

To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. For additional filtering, allow list private IP space or restrict by known good.

Known False Positives

Filtering may be required in some instances, filter as needed.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
40.0 80 50 A Java user agent $http_user_agent$ was performing a $http_method$ to retrieve a remote class file.

: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