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.

Query · spl

| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\users\\*","*\\Windows\Temp\\*","*\\programdata\\*") Filesystem.file_name="*.dll" by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | lookup hijacklibs_loaded library AS file_name OUTPUT islibrary, ttp, comment as desc | lookup hijacklibs_loaded library AS file_name excludes as file_path OUTPUT islibrary as excluded | search islibrary = TRUE AND excluded != TRUE | where isnotnull(file_name) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |  `windows_known_abused_dll_created_filter`

Implementation guide

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.

Analyst notes

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.

Raw source Windows Known Abused DLL Created · SPL
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Published by splunk/security_content ↗, licensed under Apache 2.0 ↗. Reproduced here unmodified.
name: Windows Known Abused DLL Created
id: ea91651a-772a-4b02-ac3d-985b364a5f07
version: 11
creation_date: '2024-03-20'
modification_date: '2026-05-13'
author: Steven Dick
status: production
type: Anomaly
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.
data_source:
    - Sysmon EventID 11
search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\users\\*","*\\Windows\Temp\\*","*\\programdata\\*") Filesystem.file_name="*.dll" by Filesystem.action Filesystem.dest Filesystem.file_access_time Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time Filesystem.file_name Filesystem.file_path Filesystem.file_acl Filesystem.file_size Filesystem.process_guid Filesystem.process_id Filesystem.user Filesystem.vendor_product | `drop_dm_object_name(Filesystem)` | lookup hijacklibs_loaded library AS file_name OUTPUT islibrary, ttp, comment as desc | lookup hijacklibs_loaded library AS file_name excludes as file_path OUTPUT islibrary as excluded | search islibrary = TRUE AND excluded != TRUE | where isnotnull(file_name) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |  `windows_known_abused_dll_created_filter`'
how_to_implement: 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.
references:
    - https://attack.mitre.org/techniques/T1574/002/
    - https://hijacklibs.net/api/
    - https://wietze.github.io/blog/hijacking-dlls-in-windows
    - https://github.com/olafhartong/sysmon-modular/pull/195/files
drilldown_searches:
    - name: View the detection results for - "$dest$" and "$user$"
      search: '%original_detection_search% | search  dest = "$dest$" user = "$user$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$dest$" and "$user$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
      earliest_offset: 7d
      latest_offset: "0"
intermediate_findings:
    entities:
        - field: dest
          type: system
          score: 20
          message: The file [$file_name$] was written to an unusual location on [$dest$].
        - field: user
          type: user
          score: 20
          message: The file [$file_name$] was written to an unusual location on [$dest$].
threat_objects:
    - field: file_name
      type: file_name
analytic_story:
    - Windows Defense Evasion Tactics
    - Living Off The Land
asset_type: Endpoint
mitre_attack_id:
    - T1574.001
product:
    - Splunk Enterprise
    - Splunk Enterprise Security
    - Splunk Cloud
category: endpoint
security_domain: endpoint
tests:
    - name: True Positive Test
      attack_data:
        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/hijacklibs/hijacklibs_sysmon.log
          source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
          sourcetype: XmlWinEventLog
      test_type: unit

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