Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files.
- Type: TTP
Product: Splunk Behavioral Analytics
- Last Updated: 2021-12-20
- Author: Teoderick Contreras, Splunk
- ID: 028e4406-6176-11ec-aec2-acde48001122
Kill Chain Phase
- CIS 10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 $main = from source | eval timestamp = time | eval metadata_uid = metadata.uid | eval process_pid = process.pid | eval process_file = process.file | eval process_file_path = process_file.path | eval process_file_name = lower(process_file.name) | eval process_cmd_line = process.cmd_line | eval actor_user = actor.user | eval actor_user_name = actor_user.name | eval actor_process = actor.process | eval actor_process_pid = actor_process.pid | eval actor_process_file = actor_process.file | eval actor_process_file_path = actor_process_file.path | eval actor_process_file_name = actor_process_file.name | eval device_hostname = device.hostname | where process_file_name="attrib.exe" AND match(process_cmd_line, /(?i)/)=true --finding_report--
The SPL above uses the following Macros:
hiding_files_and_directories_with_attrib_exe_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
List of fields required to use this analytic.
How To Implement
You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
Known False Positives
Some applications and users may legitimately use attrib.exe to interact with the files.
Associated Analytic Story
|72.0||80||90||Attrib.exe with +h flag to hide files on $dest$ executed by $user$ is detected.|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
source | version: 2