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Description

The following analytic identifies potential exploitation of Log4Shell CVE-2021-44228 by correlating multiple MITRE ATT&CK tactics detected in risk events. It leverages Splunk's risk data model to calculate the distinct count of MITRE ATT&CK tactics from Log4Shell-related detections. This activity is significant because it indicates a high probability of exploitation if two or more distinct tactics are observed. If confirmed malicious, this activity could lead to initial payload delivery, callback to a malicious server, and post-exploitation activities, potentially resulting in unauthorized access, lateral movement, and further compromise of the affected systems.

  • Type: Correlation
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Risk
  • Last Updated: 2024-05-26
  • Author: Jose Hernandez, Splunk
  • ID: 9be30d80-3a39-4df9-9102-64a467b24eac

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1105 Ingress Tool Transfer Command And Control
T1190 Exploit Public-Facing Application Initial Access
T1059 Command and Scripting Interpreter Execution
T1133 External Remote Services Persistence, Initial Access
Kill Chain Phase
  • Command and Control
  • Delivery
  • Installation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Log4Shell CVE-2021-44228" All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic 
| `drop_dm_object_name(All_Risk)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| where source_count >= 2 
| `log4shell_cve_2021_44228_exploitation_filter`

Macros

The SPL above uses the following Macros:

:information_source: log4shell_cve-2021-44228_exploitation_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
  • All_Risk.analyticstories
  • All_Risk.risk_object_type
  • All_Risk.risk_object
  • All_Risk.annotations.mitre_attack.mitre_tactic
  • source

How To Implement

To implement this correlation search a user needs to enable all detections in the Log4Shell Analytic Story and confirm it is generation risk events. A simple search index=risk analyticstories="Log4Shell CVE-2021-44228" should contain events.

Known False Positives

There are no known false positive for this search, but it could contain false positives as multiple detections can trigger and not have successful exploitation.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
63.0 90 70 Log4Shell Exploitation detected against $risk_object$.

: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: 4