ID | Technique | Tactic |
---|---|---|
T1003.003 | NTDS | Credential Access |
T1003 | OS Credential Dumping | Credential Access |
Detection: SecretDumps Offline NTDS Dumping Tool
Description
The following analytic detects the potential use of the secretsdump.py tool to dump NTLM hashes from a copy of ntds.dit and the SAM, SYSTEM, and SECURITY registry hives. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns and process names associated with secretsdump.py. This activity is significant because it indicates an attempt to extract sensitive credential information offline, which is a common post-exploitation technique. If confirmed malicious, this could allow an attacker to obtain NTLM hashes, facilitating further lateral movement and potential privilege escalation within the network.
Search
1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "python*.exe" Processes.process = "*.py*" Processes.process = "*-ntds*" (Processes.process = "*-system*" OR Processes.process = "*-sam*" OR Processes.process = "*-security*" OR Processes.process = "*-bootkey*") by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid
3| `drop_dm_object_name(Processes)`
4| `security_content_ctime(firstTime)`
5| `security_content_ctime(lastTime)`
6| `secretdumps_offline_ntds_dumping_tool_filter`
Data Source
Name | Platform | Sourcetype | Source | Supported App |
---|---|---|---|---|
CrowdStrike ProcessRollup2 | N/A | 'crowdstrike:events:sensor' |
'crowdstrike' |
N/A |
Macros Used
Name | Value |
---|---|
security_content_ctime | convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$) |
secretdumps_offline_ntds_dumping_tool_filter | search * |
secretdumps_offline_ntds_dumping_tool_filter
is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Annotations
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 |
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
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
Risk Based Analytics (RBA)
Risk Message | Risk Score | Impact | Confidence |
---|---|---|---|
A secretdump process $process_name$ with secretdump commandline $process$ to dump credentials in host $dest$ | 80 | 80 | 100 |
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