Name:Windows Local LLM Framework Execution id:a3f8e2c9-7d4b-4e1f-9c6a-2b5d8f3e1a7c version:3 date:None author:Rod Soto, Nasreddine Bencherchali, 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:
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 drilldown_searches:
: analytic_story:['Suspicious Local LLM Frameworks']
