Detection: Windows Known Abused DLL Created

Description

The following analytic identifies the creation of Dynamic Link Libraries (DLLs) with a known history of exploitation in atypical locations. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and filesystem events. This activity is significant as it may indicate DLL search order hijacking or sideloading, techniques used by attackers to execute arbitrary code, maintain persistence, or escalate privileges. If confirmed malicious, this activity could allow attackers to blend in with legitimate operations, posing a severe threat to system integrity and security.

 1
 2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name!="unknown" Processes.process_name=* Processes.process_guid!=null by _time span=1h Processes.dest Processes.user Processes.process_guid Processes.process_name Processes.process Processes.parent_process Processes.parent_process_name 
 3| `drop_dm_object_name(Processes)` 
 4| join max=0 process_guid dest [
 5| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\users\\*","*\\Windows\Temp\\*","*\\programdata\\*") Filesystem.file_name="*.dll" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid 
 6| `drop_dm_object_name(Filesystem)` 
 7| lookup hijacklibs_loaded library AS file_name OUTPUT islibrary, ttp, comment as desc 
 8| lookup hijacklibs_loaded library AS file_name excludes as file_path OUTPUT islibrary as excluded 
 9| search islibrary = TRUE AND excluded != TRUE 
10| stats latest(*) as * by dest process_guid ] 
11| where isnotnull(file_name) 
12| `security_content_ctime(firstTime)` 
13| `security_content_ctime(lastTime)` 
14|  `windows_known_abused_dll_created_filter`

Data Source

Name Platform Sourcetype Source Supported App
Sysmon EventID 1 Windows icon Windows 'xmlwineventlog' 'XmlWinEventLog:Microsoft-Windows-Sysmon/Operational' N/A

Macros Used

Name Value
security_content_ctime convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)
windows_known_abused_dll_created_filter search *
windows_known_abused_dll_created_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
T1574.001 DLL Search Order Hijacking Defense Evasion
T1574.002 DLL Side-Loading Persistence
T1574 Hijack Execution Flow Privilege Escalation
KillChainPhase.EXPLOITAITON
KillChainPhase.INSTALLATION
NistCategory.DE_AE
Cis18Value.CIS_10
APT41
Aquatic Panda
BackdoorDiplomacy
Cinnamon Tempest
Evilnum
RTM
Threat Group-3390
Tonto Team
Whitefly
menuPass
APT19
APT3
APT32
APT41
BRONZE BUTLER
BlackTech
Chimera
Cinnamon Tempest
Earth Lusca
FIN13
GALLIUM
Higaisa
Lazarus Group
LuminousMoth
MuddyWater
Mustang Panda
Naikon
Patchwork
SideCopy
Sidewinder
Threat Group-3390
Tropic Trooper
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 Risk Event True
This configuration file applies to all detections of type anomaly. These detections will use Risk Based Alerting.

Implementation

The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the Processes and Filesystem nodes of the Endpoint data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.

Known False Positives

This analytic may flag instances where DLLs are loaded by user mode programs for entirely legitimate and benign purposes. It is important for users to be aware that false positives are not only possible but likely, and that careful tuning of this analytic is necessary to distinguish between malicious activity and normal, everyday operations of applications. This may involve adjusting thresholds, whitelisting known good software, or incorporating additional context from other security tools and logs to reduce the rate of false positives.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
The file [$file_name$] was written to an unusual location by [$process_name$] on [$dest$]. 10 40 25
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 XmlWinEventLog:Microsoft-Windows-Sysmon/Operational XmlWinEventLog
Integration ✅ Passing Dataset XmlWinEventLog:Microsoft-Windows-Sysmon/Operational XmlWinEventLog

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