Detection rules › Splunk

Windows Password Managers Discovery

Status
production
Severity
low
Group by
IntegrityLevel, command_line, computer_name, event_action, original_file_name, parent_command_line, parent_process_guid, parent_process_id, parent_process_name, process_guid, process_hash, process_id, process_name, user, user_id, vendor_product
Author
Teoderick Contreras, Splunk
Source
github.com/splunk/security_content

The following analytic identifies command-line activity that searches for files related to password manager software, such as ".kdbx" and "credential". It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because attackers often target password manager databases to extract stored credentials, which can be used for further exploitation. If confirmed malicious, this behavior could lead to unauthorized access to sensitive information, enabling attackers to escalate privileges, move laterally, or exfiltrate critical data.

MITRE ATT&CK coverage

Event coverage

Rule body splunk

name: Windows Password Managers Discovery
id: a3b3bc96-1c4f-4eba-8218-027cac739a48
version: 13
creation_date: '2022-12-06'
modification_date: '2026-05-13'
author: Teoderick Contreras, Splunk
status: production
type: Anomaly
description: The following analytic identifies command-line activity that searches for files related to password manager software, such as "*.kdbx*" and "*credential*". It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because attackers often target password manager databases to extract stored credentials, which can be used for further exploitation. If confirmed malicious, this behavior could lead to unauthorized access to sensitive information, enabling attackers to escalate privileges, move laterally, or exfiltrate critical data.
data_source:
    - Sysmon EventID 1
    - Windows Event Log Security 4688
    - CrowdStrike ProcessRollup2
search: |-
    | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
      WHERE Processes.process = "*dir *"
        OR
        Processes.process = "*findstr*"
        AND
        Processes.process IN ( "*.kdbx*", "*credential*", "*key3.db*","*pass*", "*cred*", "*key4.db*", "*accessTokens*", "*access_tokens*", "*.htpasswd*", "*Ntds.dit*")
      BY Processes.action Processes.dest Processes.original_file_name
         Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid
         Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path
         Processes.process Processes.process_exec Processes.process_guid
         Processes.process_hash Processes.process_id Processes.process_integrity_level
         Processes.process_name Processes.process_path Processes.user
         Processes.user_id Processes.vendor_product
    | `drop_dm_object_name(Processes)`
    | `security_content_ctime(firstTime)`
    | `security_content_ctime(lastTime)`
    | `windows_password_managers_discovery_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` node 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: No false positives have been identified at this time.
references:
    - https://attack.mitre.org/techniques/T1555/005/
    - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS
    - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/
drilldown_searches:
    - name: View the detection results for - "$dest$"
      search: '%original_detection_search% | search  dest = "$dest$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$dest$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") | 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: a process with commandline $process$ that can retrieve information related to password manager databases on $dest$
analytic_story:
    - Windows Post-Exploitation
    - Prestige Ransomware
    - Scattered Spider
    - Scattered Lapsus$ Hunters
asset_type: Endpoint
mitre_attack_id:
    - T1555.005
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/malware/winpeas/winpeas_search_pwd_db/dir-db-sysmon.log
          source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
          sourcetype: XmlWinEventLog
      test_type: unit

Stages and Predicates

Stage 1: tstats

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
  WHERE Processes.process = "*dir *"
    OR
    Processes.process = "*findstr*"
    AND
    Processes.process IN ( "*.kdbx*", "*credential*", "*key3.db*","*pass*", "*cred*", "*key4.db*", "*accessTokens*", "*access_tokens*", "*.htpasswd*", "*Ntds.dit*")
  BY Processes.action Processes.dest Processes.original_file_name
     Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid
     Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path
     Processes.process Processes.process_exec Processes.process_guid
     Processes.process_hash Processes.process_id Processes.process_integrity_level
     Processes.process_name Processes.process_path Processes.user
     Processes.user_id Processes.vendor_product

Stage 2: search

| `drop_dm_object_name(Processes)`

Stage 3: search

| `security_content_ctime(firstTime)`

Stage 4: search

| `security_content_ctime(lastTime)`

Stage 5: search

| `windows_password_managers_discovery_filter`

Indicators

Each row is a field, operator, and value that the rule matches. The corpus column counts how many other rules in the catalog look for the same combination: high numbers point to widely-used, community-vetted indicators. Blank or 1 shows that the indicator is specific to this rule.

FieldKindValues
Processes.processeq
  • "*dir *" corpus 8 (sigma 5, splunk 2, chronicle 1)
  • "*findstr*" corpus 8 (sigma 6, splunk 2)
Processes.processin
  • "*.htpasswd*"
  • "*.kdbx*"
  • "*Ntds.dit*" corpus 2 (sigma 1, elastic 1)
  • "*accessTokens*"
  • "*access_tokens*"
  • "*cred*"
  • "*credential*"
  • "*key3.db*" corpus 2 (sigma 1, panther 1)
  • "*key4.db*" corpus 2 (sigma 1, panther 1)
  • "*pass*" corpus 2 (sigma 2)