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Potential Denial of Azure OpenAI ML Service

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Detects patterns indicative of Denial-of-Service (DoS) attacks on machine learning (ML) models, focusing on unusually high volume and frequency of requests or patterns of requests that are known to cause performance degradation or service disruption, such as large input sizes or rapid API calls.

Rule type: esql

Rule indices: None

Severity: medium

Risk score: 47

Runs every: 10m

Searches indices from: now-60m (Date Math format, see also Additional look-back time)

Maximum alerts per execution: 100

References:

Tags:

  • Domain: LLM
  • Data Source: Azure OpenAI
  • Data Source: Azure Event Hubs
  • Use Case: Denial of Service
  • Mitre Atlas: T0029

Version: 1

Rule authors:

  • Elastic

Rule license: Elastic License v2

Setup

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Setup

For more information on streaming events, see the Azure OpenAI documentation:

https://learn.microsoft.com/en-us/azure/azure-monitor/essentials/stream-monitoring-data-event-hubs

Rule query

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from logs-azure_openai.logs-*
// truncate the timestamp to a 1-minute window
| eval target_time_window = DATE_TRUNC(1 minutes, @timestamp)
| where azure.open_ai.operation_name == "ChatCompletions_Create"
| keep azure.open_ai.properties.request_length, azure.resource.name, cloud.account.id,target_time_window
| stats count = count(), avg_request_size = avg(azure.open_ai.properties.request_length) by target_time_window, azure.resource.name
| where count >= 10 and avg_request_size >= 5000
| sort count desc

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