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Elastic Docs ›Elastic Security [8.17] ›Downloadable rule update v8.17.14

Microsoft Entra ID Exccessive Account Lockouts Detected

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IMPORTANT: This documentation is no longer updated. Refer to Elastic's version policy and the latest documentation.

Microsoft Entra ID Exccessive Account Lockouts Detected

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Identifies a high count of failed Microsoft Entra ID sign-in attempts as the result of the target user account being locked out. Adversaries may attempt to brute-force user accounts by repeatedly trying to authenticate with incorrect credentials, leading to account lockouts by Entra ID Smart Lockout policies.

Rule type: esql

Rule indices: None

Severity: high

Risk score: 73

Runs every: 15m

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

Maximum alerts per execution: 100

References:

Tags:

  • Domain: Cloud
  • Domain: Identity
  • Data Source: Azure
  • Data Source: Entra ID
  • Data Source: Entra ID Sign-in Logs
  • Use Case: Identity and Access Audit
  • Use Case: Threat Detection
  • Tactic: Credential Access
  • Resources: Investigation Guide

Version: 1

Rule authors:

  • Elastic

Rule license: Elastic License v2

Investigation guide

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Triage and analysis

Investigating Microsoft Entra ID Exccessive Account Lockouts Detected

This rule detects a high number of sign-in failures due to account lockouts (error code 50053) in Microsoft Entra ID sign-in logs. These lockouts are typically caused by repeated authentication failures, often as a result of brute-force tactics such as password spraying, credential stuffing, or automated guessing. This detection is time-bucketed and aggregates attempts to identify bursts or coordinated campaigns targeting multiple users.

Possible investigation steps

  • Review user_id_list and user_principal_name: Check if targeted users include high-value accounts such as administrators, service principals, or shared inboxes.
  • Check error_codes and result_description: Validate that 50053 (account locked) is the consistent failure type. Messages indicating "malicious IP" activity suggest Microsoft’s backend flagged the source.
  • Analyze ip_list and source_orgs: Identify whether the activity originated from known malicious infrastructure (e.g., VPNs, botnets, or public cloud providers). In the example, traffic originates from MASSCOM, which should be validated.
  • Inspect device_detail_browser and user_agent: Clients like "Python Requests" indicate scripted automation rather than legitimate login attempts.
  • Evaluate unique_users vs. total_attempts: A high ratio suggests distributed attacks across multiple accounts, characteristic of password spraying.
  • Correlate client_app_display_name and incoming_token_type: PowerShell or unattended sign-in clients may be targeted for automation or legacy auth bypass.
  • Review conditional_access_status and risk_state: If Conditional Access was not applied and risk was not flagged, policy scope or coverage should be reviewed.
  • Validate time range (first_seen, last_seen): Determine whether the attack is a short burst or part of a longer campaign.

False positive analysis

  • Misconfigured clients, scripts, or services with outdated credentials may inadvertently cause lockouts.
  • Repeated lockouts from known internal IPs or during credential rotation windows could be benign.
  • Legacy applications without modern auth support may repeatedly fail and trigger Smart Lockout.
  • Specific known user agents (e.g., corporate service accounts).
  • Internal IPs or cloud-hosted automation with expected failure behavior.

Response and remediation

  • Investigate locked accounts immediately. Confirm if the account was successfully accessed prior to lockout.
  • Reset credentials for impacted users and enforce MFA before re-enabling accounts.
  • Block malicious IPs or ASN at the firewall, identity provider, or Conditional Access level.
  • Audit authentication methods in use, and enforce modern auth (OAuth, SAML) over legacy protocols.
  • Strengthen Conditional Access policies to reduce exposure from weak locations, apps, or clients.
  • Conduct credential hygiene audits to assess reuse and rotation for targeted accounts.

Rule query

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FROM logs-azure.signinlogs*

| EVAL
    time_window = DATE_TRUNC(30 minutes, @timestamp),
    user_id = TO_LOWER(azure.signinlogs.properties.user_principal_name),
    ip = source.ip,
    login_error = azure.signinlogs.result_description,
    error_code = azure.signinlogs.properties.status.error_code,
    request_type = TO_LOWER(azure.signinlogs.properties.incoming_token_type),
    app_name = TO_LOWER(azure.signinlogs.properties.app_display_name),
    asn_org = source.`as`.organization.name,
    country = source.geo.country_name,
    user_agent = user_agent.original,
    event_time = @timestamp

| WHERE event.dataset == "azure.signinlogs"
    AND event.category == "authentication"
    AND azure.signinlogs.category IN ("NonInteractiveUserSignInLogs", "SignInLogs")
    AND event.outcome == "failure"
    AND azure.signinlogs.properties.authentication_requirement == "singleFactorAuthentication"
    AND error_code == 50053
    AND user_id IS NOT NULL AND user_id != ""
    AND asn_org != "MICROSOFT-CORP-MSN-AS-BLOCK"

| STATS
    authentication_requirement = VALUES(azure.signinlogs.properties.authentication_requirement),
    client_app_id = VALUES(azure.signinlogs.properties.app_id),
    client_app_display_name = VALUES(azure.signinlogs.properties.app_display_name),
    target_resource_id = VALUES(azure.signinlogs.properties.resource_id),
    target_resource_display_name = VALUES(azure.signinlogs.properties.resource_display_name),
    conditional_access_status = VALUES(azure.signinlogs.properties.conditional_access_status),
    device_detail_browser = VALUES(azure.signinlogs.properties.device_detail.browser),
    device_detail_device_id = VALUES(azure.signinlogs.properties.device_detail.device_id),
    device_detail_operating_system = VALUES(azure.signinlogs.properties.device_detail.operating_system),
    incoming_token_type = VALUES(azure.signinlogs.properties.incoming_token_type),
    risk_state = VALUES(azure.signinlogs.properties.risk_state),
    session_id = VALUES(azure.signinlogs.properties.session_id),
    user_id = VALUES(azure.signinlogs.properties.user_id),
    user_principal_name = VALUES(azure.signinlogs.properties.user_principal_name),
    result_description = VALUES(azure.signinlogs.result_description),
    result_signature = VALUES(azure.signinlogs.result_signature),
    result_type = VALUES(azure.signinlogs.result_type),

    unique_users = COUNT_DISTINCT(user_id),
    user_id_list = VALUES(user_id),
    login_errors = VALUES(login_error),
    unique_login_errors = COUNT_DISTINCT(login_error),
    error_codes = VALUES(error_code),
    unique_error_codes = COUNT_DISTINCT(error_code),
    request_types = VALUES(request_type),
    app_names = VALUES(app_name),
    ip_list = VALUES(ip),
    unique_ips = COUNT_DISTINCT(ip),
    source_orgs = VALUES(asn_org),
    countries = VALUES(country),
    unique_country_count = COUNT_DISTINCT(country),
    unique_asn_orgs = COUNT_DISTINCT(asn_org),
    first_seen = MIN(event_time),
    last_seen = MAX(event_time),
    total_attempts = COUNT()
BY time_window
| WHERE unique_users >= 15 AND total_attempts >= 20
| KEEP
    time_window, total_attempts, first_seen, last_seen,
    unique_users, user_id_list, login_errors, unique_login_errors,
    unique_error_codes, error_codes, request_types, app_names,
    ip_list, unique_ips, source_orgs, countries,
    unique_country_count, unique_asn_orgs,
    authentication_requirement, client_app_id, client_app_display_name,
    target_resource_id, target_resource_display_name, conditional_access_status,
    device_detail_browser, device_detail_device_id, device_detail_operating_system,
    incoming_token_type, risk_state, session_id, user_id,
    user_principal_name, result_description, result_signature, result_type

Framework: MITRE ATT&CKTM

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