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Description

This correlation find exploitation of Log4Shell CVE-2021-44228 against systems using detections from Splunk Security Content Analytic Story. It does this by calculating the distinct count of MITRE ATT&CK tactics from Log4Shell detections fired. If the count is larger than 2 or more distinct MITRE ATT&CK tactics we assume high problability of exploitation. The Analytic story breaks down into 3 major phases of a Log4Shell exploitation, specifically> Initial Payload delivery eg. ${jndi:ldap://PAYLOAD_INJECTED} Call back to malicious LDAP server eg. Exploit.class Post Exploitation Activity/Lateral Movement using Powershell or similar T1562.001 Each of these phases fall into different MITRE ATT&CK Tactics (Initial Access, Execution, Command and Control), by looking into 2 or more phases showing up in detections triggerd is how this correlation search finds exploitation. If we get a notable from this correlation search the best way to triage it is by investigating the affected systems against Log4Shell exploitation using Splunk SOAR playbooks.

  • Type: Correlation
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Risk
  • Last Updated: 2022-09-09
  • 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
Kill Chain Phase
  • Reconnaissance
  • Exploitation
NIST
  • DE.CM
CIS20
  • CIS 3
  • CIS 5
  • CIS 16
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: 3