Detect SharpHound File Modifications
Description
The following analytic detects the creation of files typically associated with SharpHound, a reconnaissance tool used for gathering domain and trust data. It leverages file modification events from the Endpoint.Filesystem data model, focusing on default file naming patterns like *_BloodHound.zip
and various JSON files. This activity is significant as it indicates potential domain enumeration, which is a precursor to more targeted attacks. If confirmed malicious, an attacker could gain detailed insights into the domain structure, facilitating lateral movement and privilege escalation.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2024-05-15
- Author: Michael Haag, Splunk
- ID: 42b4b438-beed-11eb-ba1d-acde48001122
Annotations
ATT&CK
Kill Chain Phase
- Exploitation
NIST
- DE.CM
CIS20
- CIS 10
CVE
Search
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| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*bloodhound.zip", "*_computers.json", "*_gpos.json", "*_domains.json", "*_users.json", "*_groups.json", "*_ous.json", "*_containers.json") by Filesystem.file_create_time Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.user
| `drop_dm_object_name(Filesystem)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `detect_sharphound_file_modifications_filter`
Macros
The SPL above uses the following Macros:
detect_sharphound_file_modifications_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
- file_path
- dest
- file_name
- process_id
- file_create_time
How To Implement
To successfully implement this search you need to be ingesting information on file modifications that include the name of the process, and file, responsible for the changes from your endpoints into the Endpoint
datamodel in the Filesystem
node.
Known False Positives
False positives should be limited as the analytic is specific to a filename with extension .zip. Filter as needed.
Associated Analytic Story
RBA
Risk Score | Impact | Confidence | Message |
---|---|---|---|
24.0 | 30 | 80 | Potential SharpHound file modifications identified on $dest$ |
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
- https://attack.mitre.org/software/S0521/
- https://thedfirreport.com/?s=bloodhound
- https://github.com/BloodHoundAD/BloodHound/tree/master/Collectors
- https://github.com/BloodHoundAD/SharpHound3
- https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md#atomic-test-2—run-bloodhound-from-local-disk
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
source | version: 4