Detection rules › Splunk

Log4Shell CVE-2021-44228 Exploitation

Status
production
Severity
informational
Group by
All_Risk.annotations.mitre_attack.mitre_tactic, All_Risk.risk_object, All_Risk.risk_object_type
Author
Jose Hernandez, Splunk
Source
github.com/splunk/security_content

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.

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.

MITRE ATT&CK coverage

Rule body

name: Log4Shell CVE-2021-44228 Exploitation
id: 9be30d80-3a39-4df9-9102-64a467b24eac
version: 11
creation_date: '2022-01-27'
modification_date: '2026-05-13'
author: Jose Hernandez, Splunk
status: production
type: Correlation
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.
data_source: []
search: |-
    | 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`
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.
references:
    - https://research.splunk.com/stories/log4shell_cve-2021-44228/
    - https://www.splunk.com/en_us/blog/security/simulating-detecting-and-responding-to-log4shell-with-splunk.html
finding:
    title: Log4Shell CVE-2021-44228 Exploitation detected - $risk_object$
    entity:
        field: risk_object
        type: system
        score: 0
analytic_story:
    - Log4Shell CVE-2021-44228
    - CISA AA22-320A
asset_type: Endpoint
mitre_attack_id:
    - T1105
    - T1190
    - T1059
    - T1133
product:
    - Splunk Enterprise
    - Splunk Enterprise Security
    - Splunk Cloud
category: endpoint
security_domain: endpoint

Stages and Predicates

Stage 1: tstats

| 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

Stage 2: search

| `drop_dm_object_name(All_Risk)`

Stage 3: search

| `security_content_ctime(firstTime)`

Stage 4: search

| `security_content_ctime(lastTime)`

Stage 5: where

| where source_count >= 2

Stage 6: search

| `log4shell_cve_2021_44228_exploitation_filter`

Indicators

These rows show field, operator, and value matches.