Query Details
id: a1b2c3d4-1010-4a11-9c01-0123456789b0
name: Copilot Studio - Jailbreak / model-probing markers
description: |
Raises an incident when an inbound Copilot Studio user message contains
jailbreak-framework or model-fingerprinting markers - known jailbreak
persona names (DAN, STAN, "do anything now"), unrestricted-mode framing,
or repeated "which model / version are you" probing. This is distinct
from generic prompt injection: it targets the safety layer and the
model identity specifically.
Reads inbound turns from AppEvents (Name == "BotMessageReceived") with
the prompt text in Properties.text (requires "Log sensitive properties"
on the agent's Application Insights settings).
severity: Medium
requiredDataConnectors:
- connectorId: ApplicationInsights
dataTypes:
- AppEvents
queryFrequency: PT1H
queryPeriod: PT1H
triggerOperator: gt
triggerThreshold: 0
enabled: true
tactics:
- DefenseEvasion
- Discovery
relevantTechniques:
- T1562
- T1082
query: |
let jailbreakMarkers = dynamic([
"dan mode", "do anything now", "stan mode", "developer mode enabled",
"unrestricted mode", "no restrictions", "without any filter",
"ignore your guidelines", "ignore safety", "bypass safety",
"hypothetical response", "opposite mode", "evil mode"
]);
let modelProbes = dynamic([
"which model are you", "what model are you", "what version are you",
"are you gpt", "are you based on", "what llm", "which llm",
"what is your underlying model", "what ai model"
]);
AppEvents
| where Name == "BotMessageReceived"
| extend
ConvId = tostring(Properties["conversationId"]),
ChannelId = tostring(Properties["channelId"]),
Text = tolower(tostring(Properties["text"]))
| where isnotempty(Text)
| extend
Jailbreak = Text has_any (jailbreakMarkers),
ModelProbe = Text has_any (modelProbes)
| where Jailbreak or ModelProbe
| extend Signal = case(Jailbreak and ModelProbe, "JailbreakAndModelProbe",
Jailbreak, "Jailbreak",
"ModelProbe")
| extend AccountName = iff(isempty(UserId), "unknown-agent", UserId)
| project
TimeGenerated, Signal, AccountName, ConvId, ChannelId,
Text = substring(tostring(Properties["text"]), 0, 1024),
SessionId, ClientIP, AppVersion
| order by TimeGenerated desc
entityMappings:
- entityType: Account
fieldMappings:
- identifier: Name
columnName: AccountName
- entityType: IP
fieldMappings:
- identifier: Address
columnName: ClientIP
eventGroupingSettings:
aggregationKind: SingleAlert
incidentConfiguration:
createIncident: true
groupingConfiguration:
enabled: true
reopenClosedIncident: false
lookbackDuration: PT6H
matchingMethod: Selected
groupByEntities:
- Account
groupByAlertDetails: []
groupByCustomDetails: []
version: 1.0.0
kind: Scheduled
tags:
- Sentinel-As-Code
- Custom
- CopilotStudio
- AI
- Jailbreak
- ModelFingerprinting
This query is designed to monitor and raise alerts for specific types of messages received by a Copilot Studio application. Here's a simplified breakdown:
Purpose: The query detects when a user message contains certain keywords or phrases that suggest attempts to bypass security measures (jailbreak) or probe the identity of the AI model being used.
Keywords: It looks for phrases related to "jailbreaking" (like "do anything now" or "unrestricted mode") and "model probing" (like "which model are you" or "are you gpt").
Data Source: It analyzes messages from the AppEvents data, specifically those labeled as "BotMessageReceived".
Alert Trigger: If a message contains any of the specified jailbreak or model probing phrases, an alert is triggered.
Severity and Frequency: The alert is marked with medium severity and the query runs every hour, checking messages from the past hour.
Incident Creation: When an alert is triggered, an incident is created, grouping alerts by the user account involved.
Output: The query outputs details such as the time the message was received, the type of alert (jailbreak, model probe, or both), the user account, conversation ID, channel ID, and a snippet of the message text.
Additional Details: The query includes settings for incident management and tags for categorization.
Overall, this query helps in identifying and responding to potential security threats or unauthorized attempts to interact with the AI model in ways that could compromise its intended functionality.

David Alonso
Released: June 8, 2026
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