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ljstella
85 lines
3.4 KiB
YAML
85 lines
3.4 KiB
YAML
name: Linux Sqlite3 Privilege Escalation
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id: ab75dbb7-c3ba-4689-9c1b-8d2717bdcba1
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version: 3
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date: '2024-08-14'
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author: Gowthamaraj Rajendran, Splunk
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status: production
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type: Anomaly
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description: The following analytic detects the execution of the sqlite3 command with
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elevated privileges, which can be exploited for privilege escalation. It leverages
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Endpoint Detection and Response (EDR) telemetry to identify instances where sqlite3
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is used in conjunction with shell commands and sudo. This activity is significant
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because it indicates a potential attempt to gain root access, which could lead to
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full system compromise. If confirmed malicious, an attacker could execute arbitrary
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commands as root, leading to unauthorized access, data exfiltration, or further
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lateral movement within the network.
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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="*sqlite3*"
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AND Processes.process="*.shell*" AND Processes.process="*sudo*" by Processes.dest
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Processes.user Processes.parent_process_name Processes.process_name Processes.process
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Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
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| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_sqlite3_privilege_escalation_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: False positives may be present, filter as needed.
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references:
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- https://gtfobins.github.io/gtfobins/sqlite3/
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- https://manpages.ubuntu.com/manpages/trusty/en/man1/sqlite3.1.html
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tags:
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analytic_story:
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- Linux Privilege Escalation
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- Linux Living Off The Land
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asset_type: Endpoint
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confidence: 50
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impact: 60
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message: An instance of $parent_process_name$ spawning $process_name$ was identified
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on endpoint $dest$
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mitre_attack_id:
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- T1548.003
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- T1548
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observable:
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- name: dest
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type: Hostname
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role:
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- Victim
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- name: parent_process_name
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type: Process
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role:
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- Attacker
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- name: process_name
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type: Process
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role:
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- Attacker
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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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required_fields:
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- _time
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- Processes.dest
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- Processes.user
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- Processes.parent_process_name
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- Processes.process_name
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- Processes.process
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- Processes.process_id
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- Processes.parent_process_id
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risk_score: 30
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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/T1548/sqlite3/sysmon_linux.log
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source: Syslog:Linux-Sysmon/Operational
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sourcetype: sysmon:linux
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update_timestamp: true
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