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Merge branch '4517-bug-dac-detection_rules-help-texts-are-cut-off' of github.com:elastic/detection-rules into 4517-bug-dac-detection_rules-help-texts-are-cut-off
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‎rules/integrations/azure_openai/azure_openai_denial_of_ml_service_detection.toml‎

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[metadata]
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creation_date = "2025/02/25"
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maturity = "production"
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updated_date = "2025/02/25"
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min_stack_comments = "ES|QL rule type is still in technical preview as of 8.13, however this rule was tested successfully; integration in tech preview"
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min_stack_version = "8.13.0"
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[rule]
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author = ["Elastic"]
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description = """
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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
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of requests or patterns of requests that are known to cause performance degradation or service disruption, such as
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large input sizes or rapid API calls.
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"""
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false_positives = ["Unexpected system errors", "Legitimate spikes in usage due to business processes"]
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from = "now-60m"
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interval = "10m"
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language = "esql"
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license = "Elastic License v2"
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name = "Potential Denial of Azure OpenAI ML Service"
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references = [
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"https://genai.owasp.org/llmrisk/llm04-model-denial-of-service",
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"https://atlas.mitre.org/techniques/AML.T0029"
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]
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risk_score = 47
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rule_id = "b0450411-46e5-46d2-9b35-8b5dd9ba763e"
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setup = """## Setup
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For more information on streaming events, see the Azure OpenAI documentation:
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https://learn.microsoft.com/en-us/azure/azure-monitor/essentials/stream-monitoring-data-event-hubs
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"""
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severity = "medium"
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tags = [
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"Domain: LLM",
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"Data Source: Azure OpenAI",
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"Data Source: Azure Event Hubs",
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"Use Case: Denial of Service",
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"Mitre Atlas: T0029"
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]
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timestamp_override = "event.ingested"
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type = "esql"
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query = '''
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from logs-azure_openai.logs-*
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// truncate the timestamp to a 1-minute window
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| eval target_time_window = DATE_TRUNC(1 minutes, @timestamp)
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| where azure.open_ai.operation_name == "ChatCompletions_Create"
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| keep azure.open_ai.properties.request_length, azure.resource.name, cloud.account.id,target_time_window
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| stats count = count(), avg_request_size = avg(azure.open_ai.properties.request_length) by target_time_window, azure.resource.name
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| where count >= 10 and avg_request_size >= 5000
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| sort count desc
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'''

‎rules/integrations/azure_openai/azure_openai_insecure_output_handling_detection.toml‎

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[metadata]
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creation_date = "2025/02/25"
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maturity = "production"
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updated_date = "2025/02/25"
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min_stack_comments = "ES|QL rule type is still in experimental as of 8.13, however this rule was tested successfully; integration in experimental"
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min_stack_version = "8.13.0"
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[rule]
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author = ["Elastic"]
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description = """
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Detects when Azure OpenAI requests result in zero response length, potentially indicating issues in output handling
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that might lead to security exploits such as data leaks or code execution. This can occur in cases where the API fails
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to handle outputs correctly under certain input conditions.
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"""
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false_positives = ["Queries that are designed to expect empty responses or benign system errors"]
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from = "now-60m"
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interval = "10m"
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language = "esql"
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license = "Elastic License v2"
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name = "Azure OpenAI Insecure Output Handling"
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references = [
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"https://genai.owasp.org/llmrisk/llm02-insecure-output-handling"
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]
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risk_score = 21
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rule_id = "fb16f9ef-cb03-4234-adc2-44641f3b71ee"
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setup = """## Setup
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For more information on streaming events, see the Azure OpenAI documentation:
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https://learn.microsoft.com/en-us/azure/azure-monitor/essentials/stream-monitoring-data-event-hubs
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"""
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severity = "low"
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tags = [
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"Domain: LLM",
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"Data Source: Azure OpenAI",
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"Data Source: Azure Event Hubs",
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"Use Case: Insecure Output Handling"
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]
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timestamp_override = "event.ingested"
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type = "esql"
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query = '''
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from logs-azure_openai.logs-*
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| where azure.open_ai.properties.response_length == 0 and azure.open_ai.result_signature == "200" and azure.open_ai.operation_name == "ChatCompletions_Create"
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| keep azure.open_ai.properties.request_length, azure.open_ai.result_signature, cloud.account.id, azure.resource.name
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| stats count = count() by azure.resource.name
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| where count >= 10
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| sort count desc
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'''

‎rules/integrations/azure_openai/azure_openai_model_theft_detection.toml‎

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[metadata]
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creation_date = "2025/02/25"
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maturity = "production"
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updated_date = "2025/02/25"
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min_stack_comments = "ES|QL rule type is still in technical preview as of 8.13, however this rule was tested successfully; integration in tech preview"
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min_stack_version = "8.13.0"
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[rule]
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author = ["Elastic"]
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description = """
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Monitors for suspicious activities that may indicate theft or unauthorized duplication of machine learning (ML) models, such as
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unauthorized API calls, atypical access patterns, or large data transfers that are unusual during model interactions.
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"""
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false_positives = ["Authorized model training", "Legitimate high volume data exchanges during scheduled updates"]
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from = "now-60m"
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interval = "10m"
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language = "esql"
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license = "Elastic License v2"
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name = "Potential Azure OpenAI Model Theft"
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references = [
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"https://genai.owasp.org/llmrisk/llm10-model-theft",
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"https://atlas.mitre.org/techniques/AML.T0044"
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]
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risk_score = 47
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rule_id = "4021e78d-5293-48d3-adee-a70fa4c18fab"
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setup = """## Setup
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For more information on
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streaming events, see the Azure OpenAI documentation:
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https://learn.microsoft.com/en-us/azure/azure-monitor/essentials/stream-monitoring-data-event-hubs
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"""
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severity = "medium"
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tags = [
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"Domain: LLM",
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"Data Source: Azure OpenAI",
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"Data Source: Azure Event Hubs",
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"Use Case: Model Theft",
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"Mitre Atlas: T0044"
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]
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timestamp_override = "event.ingested"
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type = "esql"
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query = '''
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from logs-azure_openai.logs-*
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| where azure.open_ai.operation_name == "ListKey" and azure.open_ai.category == "Audit"
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| KEEP @timestamp, azure.open_ai.operation_name , azure.open_ai.category, azure.resource.group, azure.resource.name, azure.open_ai.properties.response_length
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| stats count = count(), max_data_transferred = max(azure.open_ai.properties.response_length) by azure.resource.group , azure.resource.name
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| where count >= 100 or max_data_transferred >= 1000000
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| sort count desc
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'''

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