Detection: Log4Shell JNDI Payload Injection with Outbound Connection

Updated Date: 2024-05-16 ID: 69afee44-5c91-11ec-bf1f-497c9a704a72 Author: Jose Hernandez Type: Anomaly Product: Splunk Enterprise Security

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.

 1
 2| from datamodel Web.Web 
 3| rex field=_raw max_match=0 "[jJnNdDiI]{4}(\:
 4|\%3A
 5|\/
 6|\%2F)(?<proto>\w+)(\:\/\/
 7|\%3A\%2F\%2F)(\$\{.*?\}(\.)?)?(?<affected_host>[a-zA-Z0-9\.\-\_\$]+)" 
 8| join affected_host type=inner [
 9| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic.All_Traffic by All_Traffic.dest 
10| `drop_dm_object_name(All_Traffic)` 
11| `security_content_ctime(firstTime)` 
12| `security_content_ctime(lastTime)` 
13| rename dest AS affected_host] 
14| fillnull 
15| stats count by action, category, dest, dest_port, http_content_type, http_method, http_referrer, http_user_agent, site, src, url, url_domain, user 
16| `log4shell_jndi_payload_injection_with_outbound_connection_filter`

Data Source

Name Platform Sourcetype Source Supported App
N/A N/A N/A N/A N/A

Macros Used

Name Value
security_content_ctime convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)
log4shell_jndi_payload_injection_with_outbound_connection_filter search *
log4shell_jndi_payload_injection_with_outbound_connection_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Annotations

- MITRE ATT&CK
+ Kill Chain Phases
+ NIST
+ CIS
- Threat Actors
ID Technique Tactic
T1190 Exploit Public-Facing Application Initial Access
T1133 External Remote Services Initial Access
KillChainPhase.DELIVERY
KillChainPhase.INSTALLATION
NistCategory.DE_AE
Cis18Value.CIS_10
APT28
APT29
APT39
APT41
APT5
Axiom
BackdoorDiplomacy
BlackTech
Blue Mockingbird
Cinnamon Tempest
Dragonfly
Earth Lusca
FIN13
FIN7
Fox Kitten
GALLIUM
GOLD SOUTHFIELD
HAFNIUM
Ke3chang
Kimsuky
Magic Hound
Moses Staff
MuddyWater
Rocke
Sandworm Team
Threat Group-3390
ToddyCat
Volatile Cedar
Volt Typhoon
menuPass
APT18
APT28
APT29
APT41
Akira
Chimera
Dragonfly
FIN13
FIN5
GALLIUM
GOLD SOUTHFIELD
Ke3chang
Kimsuky
LAPSUS$
Leviathan
OilRig
Sandworm Team
Scattered Spider
TeamTNT
Threat Group-3390
Wizard Spider

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting Value
Disabled true
Cron Schedule 0 * * * *
Earliest Time -70m@m
Latest Time -10m@m
Schedule Window auto
Creates Risk Event True
This configuration file applies to all detections of type anomaly. These detections will use Risk Based Alerting.

Implementation

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

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
CVE-2021-44228 Log4Shell triggered for host $dest$ 15 50 30
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

References

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation ✅ Passing N/A N/A N/A
Unit ✅ Passing Dataset nginx nginx:plus:kv
Integration ✅ Passing Dataset nginx nginx:plus:kv

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: GitHub | Version: 2