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Description

The following analytic detects Log4Shell JNDI payload injections via outbound connections. It identifies suspicious LDAP lookup functions in web logs, such as ${jndi:ldap://PAYLOAD_INJECTED}, and correlates them with network traffic to known malicious IP addresses. This detection leverages the Web and Network_Traffic data models in Splunk. Monitoring this activity is crucial as it targets vulnerabilities in Java web applications using log4j, potentially leading to remote code execution. If confirmed malicious, attackers could gain unauthorized access, execute arbitrary code, and compromise sensitive data within the affected environment.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Network_Traffic, Web
  • Last Updated: 2024-05-16
  • Author: Jose Hernandez
  • ID: 69afee44-5c91-11ec-bf1f-497c9a704a72

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1190 Exploit Public-Facing Application Initial Access
T1133 External Remote Services Persistence, Initial Access
Kill Chain Phase
  • Delivery
  • Installation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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| from datamodel Web.Web 
| rex field=_raw max_match=0 "[jJnNdDiI]{4}(\:
|\%3A
|\/
|\%2F)(?<proto>\w+)(\:\/\/
|\%3A\%2F\%2F)(\$\{.*?\}(\.)?)?(?<affected_host>[a-zA-Z0-9\.\-\_\$]+)" 
| join affected_host type=inner [
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic.All_Traffic by All_Traffic.dest 
| `drop_dm_object_name(All_Traffic)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| rename dest AS affected_host] 
| fillnull 
| stats count by action, category, dest, dest_port, http_content_type, http_method, http_referrer, http_user_agent, site, src, url, url_domain, user 
| `log4shell_jndi_payload_injection_with_outbound_connection_filter`

Macros

The SPL above uses the following Macros:

:information_source: log4shell_jndi_payload_injection_with_outbound_connection_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.

  • action
  • category
  • dest
  • dest_port
  • http_content_type
  • http_method
  • http_referrer
  • http_user_agent
  • site
  • src
  • url
  • url_domain
  • user

How To Implement

This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache or Splunk for Nginx.

Known False Positives

If there is a vulnerablility scannner looking for log4shells this will trigger, otherwise likely to have low false positives.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
15.0 50 30 CVE-2021-44228 Log4Shell triggered for host $dest$

: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