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T1621 Multi-Factor Authentication Request Generation — Attack Simulation

Adversaries may attempt to bypass multi-factor authentication (MFA) mechanisms and gain access to accounts by generating MFA requests sent to users. Adversaries in possession of credentials to Valid Accounts may be unable to complete the login process if they lack access to the 2FA or MFA mechanisms required as an additional credential and security control. To circumvent this, adversaries may abuse the automatic generation of push notifications…

Complete technique description

Adversaries may attempt to bypass multi-factor authentication (MFA) mechanisms and gain access to accounts by generating MFA requests sent to users.

Adversaries in possession of credentials to Valid Accounts may be unable to complete the login process if they lack access to the 2FA or MFA mechanisms required as an additional credential and security control. To circumvent this, adversaries may abuse the automatic generation of push notifications to MFA services such as Duo Push, Microsoft Authenticator, Okta, or similar services to have the user grant access to their account. If adversaries lack credentials to victim accounts, they may also abuse automatic push notification generation when this option is configured for self-service password reset (SSPR).[Obsidian SSPR Abuse 2023]

In some cases, adversaries may continuously repeat login attempts in order to bombard users with MFA push notifications, SMS messages, and phone calls, potentially resulting in the user finally accepting the authentication request in response to “MFA fatigue.”[Russian 2FA Push Annoyance - Cimpanu][MFA Fatigue Attacks - PortSwigger][Suspected Russian Activity Targeting Government and Business Entities Around the Globe]

Official technique definition · Open in the interactive matrix

Detection telemetry and configuration

These contracts are selected through exact technique-to-data-component relationships or explicitly labelled planning overrides. Configure a collector at the relevant observation point; a telemetry name alone is not coverage.

Application Log Content · source-mapped

DC0038 collection page — Select the application event family required by the detection and document its native schema before normalization.

Providers and collection configuration

  • Enable the application audit category or instrument the owned lab application at the authorization/action boundary.
  • Define a schema with timestamp, service, actor, object, action, result and correlation ID. Export structured records through an authenticated collector.
  • Redact secrets and personal content, separate audit from debug logs, set retention and verify delivery during rotation/restart.

Required normalized fields: service, action, actor, record_count, result.

Illustrative collection event

Project-normalized synthetic JSON, not a native provider log, captured event, attack verdict or validated detection. A parser/adapter is required for a real SIEM. Example names, IPs and values are fictional.

{
  "schema": "1200km.telemetry.example.v1",
  "synthetic": true,
  "timestamp": "2026-09-27T12:00:00Z",
  "telemetry_id": "DC0038",
  "collector": "illustrative-lab-collector",
  "observation": {
    "service": "lab-portal",
    "action": "export_requested",
    "actor": "lab-reader",
    "record_count": 3,
    "result": "authorized"
  }
}

Visibility limits: Debug output is not necessarily a durable audit trail. Application developers must emit the relevant event; installing a log pipeline does not make an absent event exist.

Logon Session Metadata · source-mapped

DC0088 collection page — Collect supported session inventory and correlate session IDs with authentication, logon and logoff events.

Providers and collection configuration

  • osquery: Scheduled host-state queries; supported tables and privileges vary by OS. Select a table that actually exposes the required object.
  • Linux Audit plus inventory snapshots: Events explain changes; snapshots describe state at collection time.
  • Define the inventory query, allowed host set, interval and least-privileged reader. Store a stable asset ID, observation time and collector version.
  • Keep successive snapshots and calculate additions, removals and changed attributes. Pair state changes with audit events when available.
  • Export collector health and missed-poll counts so a missing snapshot is not misclassified as a missing asset.

Required normalized fields: session_id, account, host, session_type.

Illustrative collection event

Project-normalized synthetic JSON, not a native provider log, captured event, attack verdict or validated detection. A parser/adapter is required for a real SIEM. Example names, IPs and values are fictional.

{
  "schema": "1200km.telemetry.example.v1",
  "synthetic": true,
  "timestamp": "2026-09-27T12:00:00Z",
  "telemetry_id": "DC0088",
  "collector": "illustrative-lab-collector",
  "observation": {
    "session_id": "lab-session-1",
    "account": "LAB\\analyst",
    "host": "lab-host-1",
    "session_type": "interactive"
  }
}

Visibility limits: Snapshots miss short-lived objects and do not identify who caused a change. A generic inventory agent is not guaranteed to expose firmware or kernel-level details.

User Account Authentication · source-mapped

DC0002 collection page — Collect successful and failed sign-ins, authentication method, application and conditional-access context; distinguish account validation from session creation.

Providers and collection configuration

  • Enable/export the required identity sign-in and audit categories from the lab tenant to a protected destination. Document license, role and retention requirements.
  • Correlate issuance or administrative changes with later sign-ins using non-secret credential IDs, application IDs, user IDs and correlation IDs.
  • Exclude passwords, bearer tokens, cookies and private keys. Record success, failure, authentication context and application result separately.

Required normalized fields: account, application, result, reason, source_ip.

Illustrative collection event

Project-normalized synthetic JSON, not a native provider log, captured event, attack verdict or validated detection. A parser/adapter is required for a real SIEM. Example names, IPs and values are fictional.

{
  "schema": "1200km.telemetry.example.v1",
  "synthetic": true,
  "timestamp": "2026-09-27T12:00:00Z",
  "telemetry_id": "DC0002",
  "collector": "illustrative-lab-collector",
  "observation": {
    "account": "analyst@example.test",
    "application": "lab-portal",
    "result": "failure",
    "reason": "invalid_credential",
    "source_ip": "192.0.2.10"
  }
}

Visibility limits: A token creation record is not evidence of token use; a sign-in is not a complete record of every resource access. Some provider-side data is unavailable to the customer.

Detection rules and analytic guidance

Open the dedicated detection workspace · Existing query engineering

DET0160 Detection Strategy for Multi-Factor Authentication Request Generation (T1621)

MITRE detection strategy

AN0449 Analytic 0449

Monitor for excessive or anomalous MFA push notifications or token requests, especially when login attempts originate from unusual IPs or geolocations and do not correspond to legitimate user-initiated sessions.

Platforms: Identity Provider.

AN0450 Analytic 0450

Detect abnormal MFA activity within cloud service provider logs, such as repeated generation of MFA challenges for the same user session or mismatched MFA device and login origin.

Platforms: IaaS.

  • User Account Authentication — AWS:CloudTrail; channel AssumeRole or ConsoleLogin with repeated MFA failures followed by repeated MFA requests.

AN0451 Analytic 0451

Detect repeated failed login events followed by MFA challenges triggered in rapid succession, especially if originating from service accounts or anomalous IP addresses.

Platforms: Windows.

AN0452 Analytic 0452

Monitor PAM and syslog entries for unusual frequency of login attempts that trigger MFA prompts, particularly when MFA challenges do not match expected user behavior.

Platforms: Linux.

AN0453 Analytic 0453

Detect anomalous OAuth or SSO logins that repeatedly generate MFA challenges, particularly where MFA approvals are denied or timed out by the user.

Platforms: SaaS.

AN0454 Analytic 0454

Detect user account logon attempts that trigger multiple MFA challenges through enterprise identity integrations, especially if MFA push requests are generated without successful interactive login.

Platforms: macOS.

Source rule directory and observation requirements

2 exact-tagged source rules. A source tag is a discovery aid, not a relevance verdict. Check the observation point and actual condition of each rule.

Before treating a rule as coverage

  1. Read the actual condition and logsource; identify attacker-side, victim-side or third-party visibility.
  2. Map native sensor fields, parse times and identities, then compile for the selected backend.
  3. Replay a behavior-positive case, a normal control, an authorized look-alike and a missing-telemetry case.
  4. Record matches and misses with rule version and source events. No match is inconclusive when required telemetry is absent.

Anomaly-based detection

Anomaly engineering worksheet

Start from the exact analytic above. The collection-specific lenses below are analyst recommendations; they do not assert that an arbitrary statistical deviation detects this technique.

  • Application and service audit: Per application, client and route: request rate, rare actions, failure ratios and unusual state transitions. Version changes, retries, health probes and seasonal load need separate baselines.
  • Host state and inventory: Compare authorized state snapshots: first-seen drivers/services/modules, unexpected configuration drift and integrity changes. Record collection cadence; absence in a sample is not evidence of deletion.
  • Identity and SaaS: Per identity and application: first-seen clients/resources, changes in authentication failures and session patterns, and unusual permission changes. Separate automation, travel, shared egress and identity-provider policy changes.
  1. Define an entity/peer group and feature; exclude the evaluation period from baseline training.
  2. Use representative normal and maintenance activity. Do not invent a production threshold from a synthetic example.
  3. Evaluate held-out behavior-positive and benign cases; measure false positives, misses and telemetry loss separately.
  4. Keep abnormality separate from maliciousness. Correlate with the behavior-specific source evidence.

Anomaly Detection Atlas · Baseline, validation and blind spots

Attack tools and documented software

No reviewed lab-tool association is selected for this exact technique. This is a curation gap, not a statement that no tool can implement the behavior.

Simulation design and documented procedures

Atomic evidence: no compatible candidate in this snapshot. Live lab status: not run for these imported procedures.

Use disposable lab hosts and test accounts, explicit target allowlists, a clean snapshot, bounded egress and a restore plan. Review every candidate and dependency before any execution.

No compatible Atomic procedure is present in this pinned evidence set. Technical feasibility has not been ruled out. A custom procedure must define the behavior, target, collection point, success observable and rollback before execution.

Technique-specific experiment contract

  1. Choose one procedure implementing T1621 Multi-Factor Authentication Request Generation; identify the observable behavior in the source definition, not just its tactic.
  2. Record exact target, tool/version, inputs, privileges, network limits and approval. Never run an imported default target automatically.
  3. Enable the collection sources above and verify a benign baseline reaches the detector.
  4. Run only the approved isolated scenario; capture native events and an independent behavior observable.
  5. Evaluate the exact rule/model, restore the lab, and preserve configuration, event hashes, alerts, controls and misses.

Synthetic logs and offline contract check

Download synthetic collection fixture (JSON) · Download the complete engineering workbook

This fixture exercises the listed collection fields. It does not reproduce Multi-Factor Authentication Request Generation, label its example events as malicious, or measure detection effectiveness. Use the exact analytic and selected procedure to build an independently labelled behavior-positive dataset.

Not run in this browser. No network scan or attack command is executed by this control.

Validation status and remaining work

Source identity
Joined by exact technique/object IDs against hash-pinned sources.
Collection fixture
Available for offline schema exercises; not attack emulation.
Native telemetry / backend / real attack
Not validated for these imported procedures. Source fidelity and page tests cannot establish sensor or detector effectiveness.

All-page audit and evidence inventory · This page’s machine-readable workbook

Sources and evidence boundaries

Pinned MITRE STIX source · SHA-256 81113ba98afa2928f3dcff62022539f7ce30c315c32e14422f11ff95ce534352. Definition and procedure context are reproduced from this snapshot, not independently re-investigated incidents.

Source-linked mitigations
  • M1017 User Training

    Train users to only accept 2FA/MFA requests from login attempts they initiated, to review source location of the login attempt prompting the 2FA/MFA requests, and to report suspicious/unsolicited prompts.

  • M1032 Multi-factor Authentication

    Implement more secure 2FA/MFA mechanisms in replacement of simple push or one-click 2FA/MFA options. For example, having users enter a one-time code provided by the login screen into the 2FA/MFA application or utilizing other out-of-band 2FA/MFA mechanisms (such as rotating code-based hardware tokens providing rotating codes that need an accompanying user pin) may be more secure. Furthermore, change default configurations and implement limits upon the maximum number of 2FA/MFA request prompts that can be sent to users in period of time.[MFA Fatigue Attacks - PortSwigger]

  • M1036 Account Use Policies

    Enable account restrictions to prevent login attempts, and the subsequent 2FA/MFA service requests, from being initiated from suspicious locations or when the source of the login attempts do not match the location of the 2FA/MFA smart device. Use conditional access policies to block logins from non-compliant devices or from outside defined organization IP ranges.[Microsoft Common Conditional Access Policies]

Connected ecosystem references

Linked tags

Detection and collection

T1621 detection workspace

No reviewed association in this snapshot.

Threat actor context

These are explicit actor-to-technique associations in the existing Threat Matrix snapshot, not attribution of an event or proof that a detector identifies the actor. No tool-to-actor relationship is inferred.

Existing research

Threat Matrix: knowledge routes, evidence and actor context

Connected anomaly research

Curated research views reached through an exact source technique, a catalog model, or a reviewed collection reference. These are navigation associations, not claims of detector effectiveness or sensor equivalence.

Telemetry contracts · Maintained query examples · Validation and blind spots

Pinned research references. No browser attack runner or production-validated detector is asserted. Imported procedures remain unvalidated; any bounded lab evidence has its own scope. ATT&CK / Atomic provenance · Detection provenance.