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Introduction:
Microsoft Entra now treats AI agents as first‑class identities, issuing access tokens not only to human users but also to interactive agents, autonomous agents, and agent user accounts. This shift radically expands the attack surface: autonomous agents operate without MFA prompts, often hold broad API permissions and long‑lived trust relationships, making them ideal persistence layers for attackers. As organizations deploy agents faster than they understand token exchange paths, OBO (On‑Behalf‑Of) trust chains, and conditional access gaps, security teams face an urgent need to adapt.
Learning Objectives:
- Understand how AI agent identities and token exchange paths alter the IAM threat landscape.
- Learn to detect and mitigate agent identity sprawl, shadow AI agents, and misconfigured OBO flows.
- Gain hands‑on skills to audit, harden, and monitor non‑human identities in Microsoft Entra using CLI commands, PowerShell scripts, and security tools.
You Should Know:
- Understanding AI Agent Identity and Token Exchange in Microsoft Entra
Extended explanation:
The post highlights that AI agents now receive access tokens similarly to service principals – with no MFA, no user friction, and often excessive permissions. Attackers can compromise an agent to establish persistence, pivot across resources via delegated access, or confuse token audiences. Red teams are already testing agent identity sprawl, shadow AI agents, and misconfigured OBO flows. Below is a step‑by‑step guide to enumerating and understanding these new identity types.
Step‑by‑step guide – enumerating non‑human identities in Entra:
Linux / Azure CLI:
Login to Azure
az login
List all service principals (including those used by agents)
az ad sp list --all --query "[].{DisplayName:displayName, AppId:appId, ObjectId:objectId}" --output table
List managed identities (often used for autonomous agents)
az identity list --subscription <subscription-id> --query "[].{Name:name, PrincipalId:principalId, ClientId:clientId}" --output table
Get detailed token issuance policies for a specific service principal
az ad sp show --id <object-id> --query "tokenEncryptionKeyId, samlMetadataUrl"
Windows / PowerShell (Microsoft Graph):
Connect-MgGraph -Scopes "Application.Read.All", "ServicePrincipal.Read.All"
Retrieve all service principals and filter for likely AI agents (by naming pattern)
Get-MgServicePrincipal -All | Where-Object {$_.DisplayName -match "agent|ai|bot|automation"} | Format-Table DisplayName, AppId, AccountEnabled
List app role assignments (permissions) for an agent
Get-MgServicePrincipalAppRoleAssignment -ServicePrincipalId <object-id> | Format-List
Export all non‑human identities to CSV for audit
Get-MgServicePrincipal -All | Select DisplayName, AppId, AccountEnabled, CreatedDateTime | Export-Csv -Path "NonHumanIdentities.csv" -NoTypeInformation
What this does: These commands inventory all service principals and managed identities that could be used by AI agents. The output reveals which agents exist, their permissions, and whether they are enabled – the first step to spotting sprawl.
- Detecting Agent Identity Sprawl and Shadow AI Agents
Extended explanation:
Organizations deploy agents without governance, leading to “identity sprawl” – thousands of unmonitored non‑human identities. Shadow AI agents are those created outside official processes, often with excessive permissions. Use the following to discover and flag anomalous agents.
Step‑by‑step guide – automated sprawl detection:
Linux / Azure CLI:
Find service principals created in the last 30 days (potential shadow agents)
az ad sp list --all --query "[?createdDateTime>='2026-04-12']" --output json | jq '.[] | {displayName, appId, createdDateTime}'
Check for service principals with admin consent granted (high risk)
az ad sp list --all --query "[?oauth2Permissions!=null && oauth2Permissions[].isEnabled==true]" --output table
Use jq to filter those with >10 app roles (excessive permissions)
az ad sp list --all | jq '.[] | select(.appRoles | length > 10) | {displayName, appRoleCount: (.appRoles | length)}'
Windows / PowerShell (using Graph API directly):
Get all service principals and group by creation date
$allSPs = Get-MgServicePrincipal -All
$shadowAgents = $allSPs | Where-Object {$<em>.CreatedDateTime -gt (Get-Date).AddDays(-30) -and $</em>.DisplayName -notlike "prod" -and $_.DisplayName -notlike "official"}
$shadowAgents | Select DisplayName, AppId, CreatedDateTime, Tags
Detect agents with more than 5 app role assignments
$excessivePerms = @()
foreach ($sp in $allSPs) {
$assignments = Get-MgServicePrincipalAppRoleAssignment -ServicePrincipalId $sp.Id
if ($assignments.Count -gt 5) {
$excessivePerms += [bash]@{DisplayName=$sp.DisplayName; RoleCount=$assignments.Count}
}
}
$excessivePerms | Sort-Object RoleCount -Descending
How to use it: Run these scripts weekly. Set alerts for any service principal with >5 app roles or created outside approved naming conventions. Shadow agents often have generic names like “test‑agent” or “ai‑bot‑01”.
3. Hardening OBO (On‑Behalf‑Of) Flows Against Misconfiguration
Extended explanation:
OBO flows allow an agent to request a token for a downstream service on behalf of a user. If misconfigured, an attacker who compromises the agent can impersonate any user with elevated privileges. The post mentions OBO trust chains as a critical gap. Below are commands to audit and harden OBO settings.
Step‑by‑step guide – auditing and fixing OBO misconfigurations:
Azure CLI – inspect OBO‑enabled applications:
Find applications that expose OBO scopes (oauth2Permissions)
az ad app list --all --query "[?api.oauth2PermissionScopes!=null].{AppName:displayName, Scopes:api.oauth2PermissionScopes[].value}" --output yaml
Check if an application allows implicit OBO (disallowed by default)
az ad app show --id <app-id> --query "api.requestedAccessTokenVersion"
Value 2 is better (uses v2 endpoints with better OBO controls)
PowerShell – restrict OBO to known trusted applications:
Retrieve all OBO‑enabled service principals
$allSPs = Get-MgServicePrincipal -All
$oboEnabled = $allSPs | Where-Object {$<em>.Oauth2PermissionScopes -ne $null}
$oboEnabled | ForEach-Object {
Write-Host "Agent: $($</em>.DisplayName) - OBO Scopes: $($_.Oauth2PermissionScopes | Select-Object Value)"
}
Remediation: Configure a Conditional Access policy for OBO flows via Graph API (requires CA admin)
$policyBody = @"
{
"displayName": "Restrict OBO to Trusted Agents",
"conditions": {
"applications": {"includeApplications": ["all"]},
"clientApplications": {"includeClientApplications": ["<trusted-agent-app-id>"]},
"clientAppTypes": ["mobileAppsAndDesktopClients", "exchangeActiveSync"],
"servicePrincipalRiskLevels": ["high", "medium"]
},
"grantControls": {
"operator": "OR",
"builtInControls": ["block"]
}
}
"@
Invoke-MgGraphRequest -Method POST -Uri "https://graph.microsoft.com/beta/identity/conditionalAccess/policies" -Body $policyBody
What this does: The first part lists all agents that can request tokens on behalf of users. The second shows a template to block any OBO attempt from an untrusted or high‑risk agent.
- Monitoring MCP Server Exposure and Token Audience Confusion
Extended explanation:
The post mentions “MCP server exposure” (likely referring to Microsoft Cloud Platform or a generic MCP service). Token audience confusion occurs when a token meant for one resource (audience) is accepted by another. Attackers reuse tokens across improperly validated services. Here’s how to detect and prevent this.
Step‑by‑step guide – validate token audience and monitor MCP endpoints:
Linux / inspect tokens (using jq and curl):
Decode a JWT token to check audience (aud claim)
echo "<JWT_TOKEN>" | cut -d. -f2 | base64 -d 2>/dev/null | jq '.aud'
Test if a token meant for Graph API works on another MCP endpoint
curl -X GET "https://management.azure.com/subscriptions?api-version=2020-01-01" -H "Authorization: Bearer <token_from_graph>"
If HTTP 200, audience confusion exists
Use Azure CLI to validate the token issuer (iss) and audience
az account get-access-token --resource https://graph.microsoft.com --query "token" -o tsv | \
xargs -I {} curl -s -X POST https://login.microsoftonline.com/common/oauth2/v2.0/token/introspect -d "token={}" -d "client_id=<client-id>"
Windows / PowerShell – monitor sign‑in logs for token audience mismatches:
Fetch sign-in logs from Entra for the last 24 hours, filter by token audience errors
$logs = Get-MgAuditLogSignIn -Filter "createdDateTime ge 2026-05-11" -Top 100
$audienceErrors = $logs | Where-Object {$<em>.Status.ErrorCode -eq 50055 -or $</em>.Status.ErrorCode -eq 50058}
$audienceErrors | Select-Object UserPrincipalName, AppDisplayName, ClientAppUsed, Status
Set up a scheduled task to alert on any token reuse across different resources
$replayDetector = @{
ResourceAccess = @{}
}
foreach ($log in $logs) {
$key = "$($log.UserPrincipalName)_$($log.CorrelationId)"
if ($replayDetector.ResourceAccess.ContainsKey($key)) {
Write-Warning "Potential token replay/audience confusion from $($log.UserPrincipalName)"
} else {
$replayDetector.ResourceAccess[$key] = $log
}
}
How to use it: Run token audience validation on any OAuth token received by your APIs. Use the sign‑in log script to alert when the same correlation ID appears across multiple resources – a sign of token reuse.
5. Implementing Conditional Access for Non‑Human Identities
Extended explanation:
Conditional Access (CA) policies traditionally target users. For agents, you must create policies that evaluate service principal risk, location, and token binding. The post states that organizations are not ready to monitor agents – CA policies are the first line of defense.
Step‑by‑step guide – create CA policies for autonomous agents:
Azure CLI / PowerShell – use Graph API to configure CA for agents:
List existing CA policies for service principals (non‑human) az rest --method GET --uri "https://graph.microsoft.com/beta/identity/conditionalAccess/policies" --query "value[?contains(conditions.applications.includeApplications, 'All')]"
PowerShell – create a policy requiring token binding for all agents:
$policyBody = @"
{
"displayName": "CA for AI Agents – Require Token Binding",
"state": "enabledForReportingButNotEnforced",
"conditions": {
"clientAppTypes": ["other"],
"applications": {
"includeApplications": ["None"],
"excludeApplications": [],
"includeUserActions": []
},
"users": {
"excludeUsers": [],
"includeUsers": [],
"includeGroups": [],
"excludeGroups": [],
"includeRoles": [],
"excludeRoles": []
},
"servicePrincipalRiskLevels": ["high", "medium"],
"signInRiskLevels": ["high", "medium"],
"clientApplications": {
"includeServicePrincipals": ["all"],
"excludeServicePrincipals": []
}
},
"grantControls": {
"operator": "OR",
"builtInControls": ["mfa"]
}
}
"@
Invoke-MgGraphRequest -Method POST -Uri "https://graph.microsoft.com/beta/identity/conditionalAccess/policies" -Body $policyBody -OutputType PSObject
Linux / verify enforcement:
Test a token request from an agent – should be blocked if policy enforced az account get-access-token --resource https://graph.microsoft.com --client-id <agent-client-id> --client-secret "<secret>" If blocked, you'll see a Conditional Access failure error
What this does: The policy blocks any agent token request when service principal risk is high or medium, requiring re‑authentication or MFA (even for non‑human identities if configured). The test command verifies enforcement.
6. Red Teaming: Abusing Delegated Access for Pivoting
Extended explanation:
Attackers compromise an agent with delegated permissions (acting on behalf of a user) then pivot to higher‑value resources. The post explicitly calls out “abusing delegated access to pivot.” Below is a controlled red‑team simulation to understand the attack path.
Step‑by‑step guide – simulate delegated access abuse (authorized lab only):
Linux / use `az` and `curl` to pivot:
Assume you have compromised an agent's refresh token (from a phishing or leaked OAuth app) refresh_token="<compromised_refresh_token>" Exchange for an access token for Microsoft Graph (delegated) curl -X POST https://login.microsoftonline.com/common/oauth2/v2.0/token \ -d "client_id=<compromised-agent-id>" \ -d "refresh_token=$refresh_token" \ -d "grant_type=refresh_token" \ -d "scope=https://graph.microsoft.com/User.Read https://graph.microsoft.com/Calendars.ReadWrite" Use the token to list high‑value users (pivot) token="<new_access_token>" curl -X GET "https://graph.microsoft.com/v1.0/users?`$filter=userType eq 'Member'&`$top=10" \ -H "Authorization: Bearer $token" | jq '.value[].userPrincipalName' If the agent also has application permissions, try accessing SharePoint or OneDrive curl -X GET "https://graph.microsoft.com/v1.0/sites/root/drive/root/children" \ -H "Authorization: Bearer $token" | jq '.value[].name'
Windows / PowerShell – detection script for abnormal delegated token usage:
Query Entra sign‑in logs for delegated token issuance from non‑human identities
$delegatedLogs = Get-MgAuditLogSignIn -Filter "authenticationRequirement eq 'singleFactorAuthentication' and tokenIssuerType eq 'AzureAD' and isInteractive eq false"
$suspicious = $delegatedLogs | Where-Object {$<em>.ResourceDisplayName -eq "Microsoft Graph" -and $</em>.Status.ErrorCode -eq 0}
$suspicious | Group-Object ServicePrincipalId | Select Count, Name | Sort-Object Count -Descending
How to use it: The red‑team script shows how an attacker leverages delegated access to read user calendars and files. The detection script identifies any non‑human identity that silently obtains delegated tokens for Microsoft Graph – a strong indicator of compromise.
7. Incident Response for Compromised Agent Tokens
Extended explanation:
Once an agent token is compromised, immediate actions include revoking tokens, disabling the agent, and rotating secrets. Because agents often have long‑lived trust, response must be aggressive.
Step‑by‑step guide – revoke and mitigate compromised tokens:
Linux / Azure CLI – revoke all tokens for a service principal:
Revoke all existing tokens for a compromised agent
az ad sp credential reset --id <compromised-object-id> --append
Disable the service principal entirely
az ad sp update --id <compromised-object-id> --set accountEnabled=false
Remove all app role assignments (strip permissions)
az ad sp delete --id <compromised-object-id>
Or use: az role assignment list --assignee <object-id> | jq '.[].id' | xargs -I {} az role assignment delete --ids {}
Windows / PowerShell – use Graph API to invalidate refresh tokens:
Revoke all refresh tokens for a user/agent (if the agent acts on behalf of a user)
Revoke-MgUserSignInSession -UserId <user-id>
For service principal, create a new client secret and invalidate old ones
$newSecret = Add-MgServicePrincipalPassword -ServicePrincipalId <object-id> -DisplayName "EmergencyRotation"
Write-Host "New secret (store securely): $($newSecret.SecretText)"
Remove all existing passwords
Get-MgServicePrincipalPassword -ServicePrincipalId <object-id> | ForEach-Object {
Remove-MgServicePrincipalPassword -ServicePrincipalId <object-id> -KeyId $_.KeyId
}
How to use it: Run the revoke commands immediately upon confirmed compromise. Follow up by reviewing audit logs for any actions taken by the agent in the last 24 hours. Rotate any keys the agent had access to.
What Undercode Say:
- Key Takeaway 1: AI agents as first‑class IAM identities create a massive, unmonitored attack surface – token exchange paths and OBO trust chains are the new perimeter.
- Key Takeaway 2: Organizations must shift from user‑centric conditional access to identity‑agnostic policies that treat every token request, human or machine, with equal scrutiny.
Analysis: The post by Elli Shlomo (Microsoft MVP) underscores a paradigm shift. Traditional security assumes humans with MFA; autonomous agents break that model. The commands and guides above provide actionable detection and hardening – from auditing service principal sprawl to simulating OBO abuse. The most dangerous aspect is speed: agents are deployed in days, but security controls take months. We predict a surge in breaches where attackers pivot from a compromised helpdesk chatbot to entire cloud tenants. The only mitigation is real‑time token validation, service principal risk scoring (like Azure AD’s Identity Protection for workloads), and mandatory token binding. Undercode recommends weekly IAM audits focusing on delegated vs application permissions and immediate revocation playbooks for agent compromise.
Prediction:
Within 12 months, we will see the first high‑profile breach where an attacker exfiltrates terabytes of data solely through a compromised AI agent’s token. Regulatory bodies (GDPR, CCPA) will begin mandating non‑human identity governance, leading to a new class of “Agent Access Brokers” (AAB) for token exchange monitoring. Microsoft Entra will likely introduce agent‑specific Conditional Access controls and anomaly detection for chained OBO flows. Organizations that fail to inventory their agent identities today will face ransomware incidents where the initial foothold is a forgotten bot with an active refresh token. The future of IAM is not human – it’s an army of bots, and every bot needs a security guard.
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IT/Security Reporter URL:
Reported By: Elishlomo Cloudsecurity – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
