Windows Local LLM Framework Execution

name: Windows Local LLM Framework Execution
id: a3f8e2c9-7d4b-4e1f-9c6a-2b5d8f3e1a7c
version: 1
date: '2025-11-20'
author: Rod Soto, Splunk
status: production
type: Hunting
description: |
    The following analytic detects execution of unauthorized local LLM frameworks (Ollama, LM Studio, GPT4All, Jan, llama.cpp, KoboldCPP, Oobabooga, NutStudio) and Python-based AI/ML libraries (HuggingFace Transformers, LangChain) on Windows endpoints by leveraging process creation events.
    It identifies cases where known LLM framework executables are launched or command-line arguments reference AI/ML libraries.
    This activity is significant as it may indicate shadow AI deployments, unauthorized model inference operations, or potential data exfiltration through local AI systems.
    If confirmed malicious, this could lead to unauthorized access to sensitive data, intellectual property theft, or circumvention of organizational AI governance policies.
data_source:
    - Sysmon EventID 1
    - Windows Event Log Security 4688
    - CrowdStrike ProcessRollup2
search: |
    | tstats `security_content_summariesonly` count
        min(_time) as firstTime
        max(_time) as lastTime
    from datamodel=Endpoint.Processes
    where
        (
          Processes.process_name IN (
              "gpt4all.exe",
              "jan.exe",
              "kobold.exe",
              "koboldcpp.exe",
              "llama-run.exe",
              "llama.cpp.exe",
              "lmstudio.exe",
              "nutstudio.exe",
              "ollama.exe",
              "oobabooga.exe",
              "text-generation-webui.exe"
          )
          OR
          Processes.original_file_name IN (
              "ollama.exe",
              "lmstudio.exe",
              "gpt4all.exe",
              "jan.exe",
              "llama-run.exe",
              "koboldcpp.exe",
              "nutstudio.exe"
          )
          OR
          Processes.process IN (
              "*\\gpt4all\\*",
              "*\\jan\\*",
              "*\\koboldcpp\\*",
              "*\\llama.cpp\\*",
              "*\\lmstudio\\*",
              "*\\nutstudio\\*",
              "*\\ollama\\*",
              "*\\oobabooga\\*",
              "*huggingface*",
              "*langchain*",
              "*llama-run*",
              "*transformers*"
          )
          OR
          Processes.parent_process_name IN (
              "gpt4all.exe",
              "jan.exe",
              "kobold.exe",
              "koboldcpp.exe",
              "llama-run.exe",
              "llama.cpp.exe",
              "lmstudio.exe",
              "nutstudio.exe",
              "ollama.exe",
              "oobabooga.exe",
              "text-generation-webui.exe"
          )
        )
    by Processes.action Processes.dest Processes.original_file_name Processes.parent_process
       Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id
       Processes.parent_process_name Processes.parent_process_path Processes.process
       Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id
       Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user
       Processes.user_id Processes.vendor_product
    | `drop_dm_object_name(Processes)`
    | eval Framework=case(
        match(process_name, "(?i)ollama") OR match(process, "(?i)ollama"), "Ollama",
        match(process_name, "(?i)lmstudio") OR match(process, "(?i)lmstudio") OR match(process, "(?i)lm-studio"), "LM Studio",
        match(process_name, "(?i)gpt4all") OR match(process, "(?i)gpt4all"), "GPT4All",
        match(process_name, "(?i)kobold") OR match(process, "(?i)kobold"), "KoboldCPP",
        match(process_name, "(?i)jan") OR match(process, "(?i)jan"), "Jan AI",
        match(process_name, "(?i)nutstudio") OR match(process, "(?i)nutstudio"), "NutStudio",
        match(process_name, "(?i)llama") OR match(process, "(?i)llama"), "llama.cpp",
        match(process_name, "(?i)oobabooga") OR match(process, "(?i)oobabooga") OR match(process, "(?i)text-generation-webui"), "Oobabooga",
        match(process, "(?i)transformers") OR match(process, "(?i)huggingface"), "HuggingFace/Transformers",
        match(process, "(?i)langchain"), "LangChain",
        1=1, "Other"
    )
    | `security_content_ctime(firstTime)`
    | `security_content_ctime(lastTime)`
    | table action dest Framework original_file_name parent_process parent_process_exec
            parent_process_guid parent_process_id parent_process_name parent_process_path
            process process_exec process_guid process_hash process_id process_integrity_level
            process_name process_path user user_id vendor_product
    | `windows_local_llm_framework_execution_filter`
how_to_implement: |
    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: Legitimate development, data science, and AI/ML workflows where authorized developers, researchers, or engineers intentionally execute local LLM frameworks (Ollama, LM Studio, GPT4All, Jan, NutStudio) for model experimentation, fine-tuning, or prototyping. Python developers using HuggingFace Transformers or LangChain for legitimate AI/ML projects. Approved sandbox and lab environments where framework testing is authorized. Open-source contributors and hobbyists running frameworks for educational purposes. Third-party applications that bundle or invoke LLM frameworks as dependencies (e.g., IDE plugins, analytics tools, chatbot integrations). System administrators deploying frameworks as part of containerized services or orchestrated ML workloads. Process name keyword overlap with unrelated utilities (e.g., "llama-backup", "janimation"). Recommended tuning — baseline approved frameworks and users by role/department, exclude sanctioned dev/lab systems via the filter macro, correlate with user identity and peer group anomalies before escalating to incident response.
references:
    - https://splunkbase.splunk.com/app/8024
    - https://www.ibm.com/think/topics/shadow-ai
    - https://www.splunk.com/en_us/blog/artificial-intelligence/splunk-technology-add-on-for-ollama.html
    - https://blogs.cisco.com/security/detecting-exposed-llm-servers-shodan-case-study-on-ollama
    - https://docs.microsoft.com/en-us/sysinternals/downloads/sysmon
tags:
    analytic_story:
        - Suspicious Local LLM Frameworks
    asset_type: Endpoint
    mitre_attack_id:
        - T1543
    product:
        - Splunk Enterprise
        - Splunk Enterprise Security
        - Splunk Cloud
    security_domain: endpoint
tests:
    - name: True Positive Test - Sysmon
      attack_data:
        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/local_llms/sysmon_local_llms.log
          source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
          sourcetype: XmlWinEventLog