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Description

The following analytic detects potential exploitation attempts of the Log4Shell vulnerability (CVE-2021-44228) by analyzing HTTP headers for specific patterns. It leverages the Web Datamodel and evaluates various indicators such as the presence of {jndi:, environment variables, and common URI paths. This detection is significant as Log4Shell allows remote code execution, posing a severe threat to systems. If confirmed malicious, attackers could gain unauthorized access, execute arbitrary code, and potentially compromise sensitive data, leading to extensive damage and data breaches.

  • Type: Hunting
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Web
  • Last Updated: 2024-05-26
  • Author: Michael Haag, Splunk
  • ID: 158b68fa-5d1a-11ec-aac8-acde48001122

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1190 Exploit Public-Facing Application Initial Access
T1133 External Remote Services Persistence, Initial Access
Kill Chain Phase
  • Delivery
  • Installation
NIST
  • DE.AE
CIS20
  • CIS 13
CVE 


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| from datamodel Web.Web 
| eval jndi=if(match(_raw, "(\{
|%7B)[jJnNdDiI]{4}:"),4,0) 
| eval jndi_fastmatch=if(match(_raw, "[jJnNdDiI]{4}"),2,0) 
| eval jndi_proto=if(match(_raw,"(?i)jndi:(ldap[s]?
|rmi
|dns
|nis
|iiop
|corba
|nds
|http
|https):"),5,0) 
| eval all_match = if(match(_raw, "(?i)(%(25){0,}20
|\s)*(%(25){0,}24
|\$)(%(25){0,}20
|\s)*(%(25){0,}7B
|{)(%(25){0,}20
|\s)*(%(25){0,}(6A
|4A)
|J)(%(25){0,}(6E
|4E)
|N)(%(25){0,}(64
|44)
|D)(%(25){0,}(69
|49)
|I)(%(25){0,}20
|\s)*(%(25){0,}3A
|:)[\w\%]+(%(25){1,}3A
|:)(%(25){1,}2F
|\/)[^\n]+"),5,0) 
| eval env_var = if(match(_raw, "env:") OR match(_raw, "env:AWS_ACCESS_KEY_ID") OR match(_raw, "env:AWS_SECRET_ACCESS_KEY"),5,0) 
| eval uridetect = if(match(_raw, "(?i)Basic\/Command\/Base64
|Basic\/ReverseShell
|Basic\/TomcatMemshell
|Basic\/JBossMemshell
|Basic\/WebsphereMemshell
|Basic\/SpringMemshell
|Basic\/Command
|Deserialization\/CommonsCollectionsK
|Deserialization\/CommonsBeanutils
|Deserialization\/Jre8u20\/TomcatMemshell
|Deserialization\/CVE_2020_2555\/WeblogicMemshell
|TomcatBypass
|GroovyBypass
|WebsphereBypass"),4,0) 
| eval keywords = if(match(_raw,"(?i)\$\{ctx\:loginId\}
|\$\{map\:type\}
|\$\{filename\}
|\$\{date\:MM-dd-yyyy\}
|\$\{docker\:containerId\}
|\$\{docker\:containerName\}
|\$\{docker\:imageName\}
|\$\{env\:USER\}
|\$\{event\:Marker\}
|\$\{mdc\:UserId\}
|\$\{java\:runtime\}
|\$\{java\:vm\}
|\$\{java\:os\}
|\$\{jndi\:logging/context-name\}
|\$\{hostName\}
|\$\{docker\:containerId\}
|\$\{k8s\:accountName\}
|\$\{k8s\:clusterName\}
|\$\{k8s\:containerId\}
|\$\{k8s\:containerName\}
|\$\{k8s\:host\}
|\$\{k8s\:labels.app\}
|\$\{k8s\:labels.podTemplateHash\}
|\$\{k8s\:masterUrl\}
|\$\{k8s\:namespaceId\}
|\$\{k8s\:namespaceName\}
|\$\{k8s\:podId\}
|\$\{k8s\:podIp\}
|\$\{k8s\:podName\}
|\$\{k8s\:imageId\}
|\$\{k8s\:imageName\}
|\$\{log4j\:configLocation\}
|\$\{log4j\:configParentLocation\}
|\$\{spring\:spring.application.name\}
|\$\{main\:myString\}
|\$\{main\:0\}
|\$\{main\:1\}
|\$\{main\:2\}
|\$\{main\:3\}
|\$\{main\:4\}
|\$\{main\:bar\}
|\$\{name\}
|\$\{marker\}
|\$\{marker\:name\}
|\$\{spring\:profiles.active[0]
|\$\{sys\:logPath\}
|\$\{web\:rootDir\}
|\$\{sys\:user.name\}"),4,0) 
| eval obf = if(match(_raw, "(\$
|%24)[^ /]*({
|%7b)[^ /]*(j
|%6a)[^ /]*(n
|%6e)[^ /]*(d
|%64)[^ /]*(i
|%69)[^ /]*(:
|%3a)[^ /]*(:
|%3a)[^ /]*(/
|%2f)"),5,0) 
| eval lookups = if(match(_raw, "(?i)({
|%7b)(main
|sys
|k8s
|spring
|lower
|upper
|env
|date
|sd)"),4,0)  
| addtotals fieldname=Score, jndi, jndi_proto, env_var, uridetect, all_match, jndi_fastmatch, keywords, obf, lookups 
| where Score > 2 
| stats values(Score) by  jndi, jndi_proto, env_var, uridetect, all_match, jndi_fastmatch, keywords, lookups, obf, dest, src, http_method, _raw 
| `hunting_for_log4shell_filter`

Macros

The SPL above uses the following Macros:

:information_source: hunting_for_log4shell_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
  • _raw

How To Implement

Out of the box, the Web datamodel is required to be pre-filled. However, tested was performed against raw httpd access logs. Change the first line to any dataset to pass the regex's against.

Known False Positives

It is highly possible you will find false positives, however, the base score is set to 2 for any jndi found in raw logs. tune and change as needed, include any filtering.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
40.0 80 50 Hunting for Log4Shell exploitation has occurred.

: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: 2