T1451 SIM Card Swap — Attack Simulation

Complete technique description

Adversaries may gain access to mobile devices through transfers or swaps from victims’ phone numbers to adversary-controlled SIM cards and mobile devices.[ATT SIM Swap Scams][Verizon SIM Swapping]

The typical process is as follows:

1. Adversaries will first gather information about victims through Phishing, social engineering, data breaches, or other avenues.
2. Adversaries will then impersonate victims as they contact mobile carriers to request for the SIM swaps. For example, adversaries would provide victims’ name and address to mobile carriers; once authenticated, adversaries would request for victims’ phone numbers to be transferred to adversary-controlled SIM cards.
3. Once completed, victims will lose mobile data, such as text messages and phone calls, on their mobile devices. In turn, adversaries will receive mobile data that was intended for the victims.

Adversaries may use the intercepted SMS messages to log into online accounts that use SMS-based authentication. Specifically, adversaries may use SMS-based authentication to log into banking and/or cryptocurrency accounts, then transfer funds to adversary-controlled wallets.

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.

{
  "schema": "1200km.telemetry.example.v1",
  "synthetic": true,
  "timestamp": "2026-09-27T12:00:00Z",
  "telemetry_id": "DC0018",
  "collector": "illustrative-lab-collector",
  "observation": {
    "asset_id": "lab-host-1",
    "status": "degraded",
    "heartbeat_age_seconds": 90,
    "collector_healthy": true
  }
}

{
  "schema": "1200km.telemetry.example.v1",
  "synthetic": true,
  "timestamp": "2026-09-27T12:00:00Z",
  "telemetry_id": "DC0118",
  "collector": "illustrative-lab-collector",
  "observation": {
    "device_id": "android-lab-1",
    "setting": "airplane_mode",
    "scope": "device",
    "before": false,
    "after": true
  }
}

{
  "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"
  }
}

Detection rules and analytic guidance

Open the dedicated detection workspace · Existing query engineering

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

No exact technique-specific Atlas model is present for T1451 in the selected source snapshot. The following is a design worksheet, not a newly validated model.

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.

• 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.
• Mobile lab instrumentation: Per owned device, app version and permission state: unexpected API/permission changes, background activity and network destinations. OS privacy restrictions and emulator differences limit observability.
• 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 owned test devices or emulators, disposable app accounts and test data. Verify platform/version instrumentation; desktop test procedures do not establish mobile coverage.

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 T1451 SIM Card Swap; 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 SIM Card Swap, 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 824f7d8a37e7893b29e4c6ca6d87b5fbbf3b228ec6384f1a69ee20d523ff59e5. Definition and procedure context are reproduced from this snapshot, not independently re-investigated incidents.

• mitre-attack
• ATT SIM Swap Scams
• Verizon SIM Swapping
• NIST Mobile Threat Catalogue