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Description

The following analytic identifies a process command line related to the discovery of possible password or credentials in the registry. This technique is being abused by adversaries or post exploitation tools like winpeas to steal credentials in the registry in the targeted host. Registry can contain several sensitive information like username and credentials that can be used for privilege escalation, persistence or even in lateral movement. This Anomaly detection can be a good pivot to detect a suspicious process querying a registry related to password or private keys.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Endpoint
  • Last Updated: 2022-11-30
  • Author: Teoderick Contreras, Splunk
  • ID: a8b3124e-2278-4b73-ae9c-585117079fb2

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1552.002 Credentials in Registry Credential Access
T1552 Unsecured Credentials Credential Access
Kill Chain Phase
  • Exploitation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_reg` AND Processes.process = "* query *" AND Processes.process IN ("*\\Software\\ORL\\WinVNC3\\Password*", "*\\SOFTWARE\\RealVNC\\WinVNC4 /v password*", "*\\CurrentControlSet\\Services\\SNMP*", "*\\Software\\TightVNC\\Server*", "*\\Software\\SimonTatham\\PuTTY\\Sessions*", "*\\Software\\OpenSSH\\Agent\\Keys*", "*password*") by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `windows_credentials_in_registry_reg_query_filter`

Macros

The SPL above uses the following Macros:

:information_source: windows_credentials_in_registry_reg_query_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
  • Processes.dest
  • Processes.user
  • Processes.parent_process_name
  • Processes.parent_process
  • Processes.original_file_name
  • Processes.process_name
  • Processes.process
  • Processes.process_id
  • Processes.parent_process_path
  • Processes.process_path
  • Processes.parent_process_id
  • Processes.parent_process_guid
  • Processes.process_guid

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

unknown

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
25.0 50 50 reg query commandline $process$ in $dest$

: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

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