Detection: Adobe ColdFusion Unauthenticated Arbitrary File Read

Updated Date: 2024-09-30 ID: 695aceae-21db-4e7f-93ac-a52e39d02b93 Author: Michael Haag, Splunk Type: TTP Product: Splunk Enterprise Security

Description

The following analytic detects potential exploitation of the Adobe ColdFusion vulnerability, CVE-2023-26360, which allows unauthenticated arbitrary file read. It monitors web requests to the "/cf_scripts/scripts/ajax/ckeditor/*" path using the Web datamodel, focusing on specific ColdFusion paths to differentiate malicious activity from normal traffic. This activity is significant due to the vulnerability's high CVSS score of 9.8, indicating severe risk. If confirmed malicious, it could lead to unauthorized data access, further attacks, or severe operational disruptions, necessitating immediate investigation.

1
2| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/cf_scripts/scripts/ajax/ckeditor/*") Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype 
3| `drop_dm_object_name("Web")` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| `adobe_coldfusion_unauthenticated_arbitrary_file_read_filter`

Data Source

Name Platform Sourcetype Source
Suricata N/A 'suricata' 'suricata'

Macros Used

Name Value
security_content_ctime convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)
adobe_coldfusion_unauthenticated_arbitrary_file_read_filter search *
adobe_coldfusion_unauthenticated_arbitrary_file_read_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
KillChainPhase.DELIVERY
NistCategory.DE_CM
Cis18Value.CIS_13
APT28
APT29
APT39
APT41
APT5
Agrius
Axiom
BackdoorDiplomacy
BlackTech
Blue Mockingbird
Cinnamon Tempest
Dragonfly
Earth Lusca
Ember Bear
FIN13
FIN7
Fox Kitten
GALLIUM
GOLD SOUTHFIELD
HAFNIUM
INC Ransom
Ke3chang
Kimsuky
Magic Hound
Moses Staff
MuddyWater
Play
Rocke
Sandworm Team
Threat Group-3390
ToddyCat
Volatile Cedar
Volt Typhoon
Winter Vivern
menuPass

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 Notable Yes
Rule Title %name%
Rule Description %description%
Notable Event Fields user, dest
Creates Risk Event True
This configuration file applies to all detections of type TTP. These detections will use Risk Based Alerting and generate Notable Events.

Implementation

This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto.

Known False Positives

In the wild, we have observed three different types of attempts that could potentially trigger false positives if the HTTP status code is not in the query. Please check this github gist for the specific URIs : https://gist.github.com/patel-bhavin/d10830f3f375a2397233f6a4fe38d5c9 . These could be legitimate requests depending on the context of your organization. Therefore, it is recommended to modify the analytic as needed to suit your specific environment.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
Possible exploitation of CVE-2023-26360 against $dest$. 45 90 50
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 suricata suricata
Integration ✅ Passing Dataset suricata suricata

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: 3