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https://github.com/splunk/security_content
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pyth0n1c
81 lines
3.9 KiB
YAML
81 lines
3.9 KiB
YAML
name: Linux Shred Overwrite Command
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id: c1952cf1-643c-4965-82de-11c067cbae76
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version: 4
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date: '2024-11-13'
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author: Teoderick Contreras, Splunk
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status: production
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type: TTP
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description: The following analytic detects the execution of the 'shred' command on
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a Linux machine, which is used to overwrite files to make them unrecoverable. It
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leverages data from Endpoint Detection and Response (EDR) agents, focusing on process
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names and command-line arguments. This activity is significant because the 'shred'
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command can be used in destructive attacks, such as those seen in the Industroyer2
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malware targeting energy facilities. If confirmed malicious, this activity could
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lead to the permanent destruction of critical files, severely impacting system integrity
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and data availability.
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data_source:
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- Sysmon for Linux EventID 1
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search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
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as lastTime from datamodel=Endpoint.Processes where Processes.process_name =shred
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AND Processes.process IN ("*-n*", "*-u*", "*-z*", "*-s*") by Processes.parent_process_name
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Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
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Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)`
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| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_shred_overwrite_command_filter`'
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how_to_implement: The detection is based on data that originates from Endpoint Detection
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and Response (EDR) agents. These agents are designed to provide security-related
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telemetry from the endpoints where the agent is installed. To implement this search,
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you must ingest logs that contain the process GUID, process name, and parent process.
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Additionally, you must ingest complete command-line executions. These logs must
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be processed using the appropriate Splunk Technology Add-ons that are specific to
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the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
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data model. Use the Splunk Common Information Model (CIM) to normalize the field
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names and speed up the data modeling process.
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known_false_positives: Administrator or network operator can use this application
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for automation purposes. Please update the filter macros to remove false positives.
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references:
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- https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/
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- https://cert.gov.ua/article/39518
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drilldown_searches:
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- name: View the detection results for - "$dest$"
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search: '%original_detection_search% | search dest = "$dest$"'
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earliest_offset: $info_min_time$
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latest_offset: $info_max_time$
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- name: View risk events for the last 7 days for - "$dest$"
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search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
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starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime
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values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
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as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
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as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
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| `security_content_ctime(lastTime)`'
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earliest_offset: $info_min_time$
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latest_offset: $info_max_time$
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rba:
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message: A possible shred overwrite command $process$ executed on $dest$
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risk_objects:
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- field: dest
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type: system
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score: 49
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threat_objects: []
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tags:
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analytic_story:
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- Industroyer2
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- AwfulShred
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- Linux Privilege Escalation
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- Data Destruction
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- Linux Persistence Techniques
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asset_type: Endpoint
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mitre_attack_id:
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- T1485
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product:
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- Splunk Enterprise
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- Splunk Enterprise Security
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- Splunk Cloud
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security_domain: endpoint
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tests:
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- name: True Positive Test
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attack_data:
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- data:
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https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/rm_shred_critical_dir/sysmon_linux.log
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source: Syslog:Linux-Sysmon/Operational
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sourcetype: sysmon:linux
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