Sample rules
Log4Shell CVE-2021-44228 Exploitation
- source: splunk
- technicques:
- T1105
- T1190
- T1059
- T1133
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.
Detection logic
| 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`
Living Off The Land Detection
- source: splunk
- technicques:
- T1105
- T1190
- T1059
- T1133
Description
The following correlation identifies a distinct amount of analytics associated with the Living Off The Land analytic story that identify potentially suspicious behavior.
Detection logic
| 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="Living Off The Land" 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 >= 5
| `living_off_the_land_filter`