Ollama Possible Memory Exhaustion Resource Abuse

name: Ollama Possible Memory Exhaustion Resource Abuse
id: ca96297f-e82e-4749-8cc9-d1ab555abb57
version: 4
creation_date: '2025-10-13'
modification_date: '2026-05-13'
author: Rod Soto
status: experimental
type: Anomaly
description: Detects abnormal memory allocation patterns and excessive runner operations in Ollama that may indicate resource exhaustion attacks, memory abuse through malicious model loading, or attempts to degrade system performance by overwhelming GPU/CPU resources. Adversaries may deliberately load multiple large models, trigger repeated model initialization cycles, or exploit memory allocation mechanisms to exhaust available system resources, causing denial of service conditions or degrading performance for legitimate users.
data_source:
    - Ollama Server
search: '`ollama_server` ("*llama_kv_cache*" OR "*compute buffer*" OR "*llama runner started*" OR "*loaded runners*") | rex field=_raw "count=(?<runner_count>\d+)" | rex field=_raw "size\s*=\s*(?<memory_mb>[\d\.]+)\s+MiB" | rex field=_raw "started in\s*(?<load_time>[\d\.]+)\s*seconds" | rex field=_raw "source=(?<code_source>[^\s]+)" | bin _time span=5m | stats count as operations, sum(runner_count) as total_runners, dc(code_source) as unique_sources, values(code_source) as code_sources, avg(memory_mb) as avg_memory, max(memory_mb) as max_memory, sum(memory_mb) as total_memory, avg(load_time) as avg_load_time, max(load_time) as max_load_time by _time, host | where operations > 5 OR total_runners > 0 OR max_memory > 400 OR total_memory > 500 | eval avg_memory=round(avg_memory, 2) | eval max_memory=round(max_memory, 2) | eval total_memory=round(total_memory, 2) | eval avg_load_time=round(avg_load_time, 2) | eval severity=case( max_memory > 500 OR total_memory > 1000, "critical", max_memory > 400 OR operations > 20, "high", operations > 10, "medium", 1=1, "low" ) | eval attack_type="Resource Exhaustion / Memory Abuse" | sort -_time | table _time, host, operations, total_runners, unique_sources, avg_memory, max_memory, total_memory, avg_load_time, max_load_time, severity, attack_type | `ollama_possible_memory_exhaustion_resource_abuse_filter`'
how_to_implement: 'Ingest Ollama logs via Splunk TA-ollama add-on by configuring file monitoring inputs pointed to your Ollama server log directories (sourcetype: ollama:server), or enable HTTP Event Collector (HEC) for real-time API telemetry and prompt analytics (sourcetypes: ollama:api, ollama:prompts). CIM compatibility using the Web datamodel for standardized security detections.'
known_false_positives: Legitimate high-volume production workloads processing multiple concurrent requests, users loading large language models (7B+ parameters) that naturally require substantial memory allocation, simultaneous multi-model deployments during system scaling, batch processing operations, or initial system startup sequences may generate similar memory allocation patterns during normal operations.
references:
    - https://github.com/rosplk/ta-ollama
drilldown_searches:
    - name: View the detection results for - "$host$"
      search: '%original_detection_search% | search  "$host = "$host$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$host$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$") | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
      earliest_offset: 7d
      latest_offset: "0"
intermediate_findings:
    entities:
        - field: host
          type: system
          score: 20
          message: Potential resource exhaustion attack detected on $host$ with $operations$ memory operations in 5 minutes, utilizing $max_memory$ MiB peak memory and $total_runners$ runners, indicating possible attempts to exhaust system resources through excessive model loading or memory abuse.
analytic_story:
    - Suspicious Ollama Activities
asset_type: Web Application
mitre_attack_id:
    - T1499
product:
    - Splunk Enterprise
    - Splunk Enterprise Security
    - Splunk Cloud
category: application
security_domain: endpoint
tests:
    - name: True Positive Test
      attack_data:
        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/ollama/server.log
          sourcetype: ollama:server
          source: server.log
      test_type: experimental
      description: This test is a legacy experimental test and may not be accurate.