1200kmSECURITY RESEARCH
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T1533 Data from Local System — Attack Simulation

Adversaries may search local system sources, such as file systems or local databases, to find files of interest and sensitive data prior to exfiltration. Access to local system data, which includes information stored by the operating system, often requires escalated privileges. Examples of local system data include authentication tokens, the device keyboard cache, Wi-Fi passwords, and photos. On Android, adversaries may also attempt to access…

Complete technique description

Adversaries may search local system sources, such as file systems or local databases, to find files of interest and sensitive data prior to exfiltration.

Access to local system data, which includes information stored by the operating system, often requires escalated privileges. Examples of local system data include authentication tokens, the device keyboard cache, Wi-Fi passwords, and photos. On Android, adversaries may also attempt to access files from external storage which may require additional storage-related permissions.

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 Permission · source-mapped

DC0114 collection page — Compare manifest declarations with runtime granted state using authorized package inspection and own-app permission checks.

Providers and collection configuration

  • Android logcat: Permitted logs from a consented test device/emulator; output depends on build, permissions and instrumentation.
  • Android dumpsys: Point-in-time service/package state on an authorized development device.
  • Instrumented test application: Own-app permission, API and state callbacks; not universal visibility into other apps.
  • Use a disposable emulator or owned development device with approved debugging. Record OS/API level, app version and instrumentation privileges.
  • Collect only relevant logcat buffers/tags and service snapshots; add explicit own-app instrumentation for events absent from OS logs.
  • Forward sanitized logs and compare before/after state. Remove test data and revoke debugging access after the lab session.

Required normalized fields: package, permission, declared, granted.

adb shell dumpsys package test.example.lab

Read-only snapshot on a consented debug device. Replace the example package with your owned test app. Output and access vary by Android build; this is not a permission-request event stream.

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": "DC0114",
  "collector": "illustrative-lab-collector",
  "observation": {
    "package": "test.example.lab",
    "permission": "android.permission.CAMERA",
    "declared": true,
    "granted": false
  }
}

Visibility limits: Android tooling does not imply iOS coverage. Standard mobile apps cannot observe all other apps or protected OS internals; MDM status is not full API, filesystem or content telemetry.

File Access · source-mapped

DC0055 collection page — Enable Audit File System plus a narrow SACL on a lab directory; on Linux select path/syscall rules and join associated Audit records.

Providers and collection configuration

  • Use a lab-only GPO under Computer Configuration → Windows Settings → Security Settings → Advanced Audit Policy Configuration. Enable the specific subcategory named below.
  • For object-level auditing, configure a narrow system access control list (SACL) on the test object in addition to policy. Do not audit an entire production directory by default.
  • Forward Security events from the responsible host or controller; retain subject and target identities, object identifiers, result and original XML. Confirm effective policy with auditpol /get /category:*.

Required normalized fields: path, access, actor, 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": "DC0055",
  "collector": "illustrative-lab-collector",
  "observation": {
    "path": "C:\\Lab\\demo.txt",
    "access": "read",
    "actor": "LAB\\analyst",
    "result": "success"
  }
}

Visibility limits: A policy checkbox alone does not guarantee an event. Host role, SACL, success/failure selection and audit policy precedence matter. Directory changes may arrive as multiple records.

OS API Execution · source-mapped

DC0021 collection page — Use an approved EDR trace or instrument the owned lab process at selected APIs. Define API name, caller, sanitized arguments and return status.

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: process_id, api, path, 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": "DC0021",
  "collector": "illustrative-lab-collector",
  "observation": {
    "process_id": 4200,
    "api": "CreateFileW",
    "path": "C:\\Lab\\demo.txt",
    "result": "success"
  }
}

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.

Detection rules and analytic guidance

Open the dedicated detection workspace · Existing query engineering

DET0713 Detection of Data from Local System

MITRE detection strategy

AN1840 Analytic 1840

For Android, detect Data from Local System from documented management, network, or vetting evidence; treat state queries as periodic observations rather than real-time events. Correlate Application Permission via Android Enterprise DPC / Application Vetting [DevicePolicyManager.getPermissionGrantState(); PackageManager manifest/requested-permission review]; File Access via Application Vetting / Forensic Acquisition [Dynamic filesystem trace or acquired app-container/filesystem artifacts: App reads files across $MinDirectoryCount$+ distinct parent directories with file_read_count > $FileReadRateThreshold$ within $DataCollectionWindow$ in background or low-interaction state — bulk cross-directory read is the collection signal]; OS API Execution via Application Vetting / Instrumented Dynamic Analysis [Dynamic API/runtime trace: App invokes ContentResolver or SAF (Storage Access Framework) queries across multiple user-data paths outside its own sandbox in background state]. Alert on a sequence or state change inconsistent with the device/app baseline, approved management policy, or expected user workflow; do not infer unobserved API calls, file/process activity, or payload content from management state alone.

Platforms: Android.

  • Application Permission — android:DevicePolicyManager; channel DevicePolicyManager.getPermissionGrantState(); PackageManager manifest/requested-permission review.
  • File Access — mobile:ForensicArtifacts; channel Dynamic filesystem trace or acquired app-container/filesystem artifacts: App reads files across $MinDirectoryCount$+ distinct parent directories with file_read_count > $FileReadRateThreshold$ within $DataCollectionWindow$ in background or low-interaction state — bulk cross-directory read is the collection signal.
  • OS API Execution — mobile:DynamicAnalysis; channel Dynamic API/runtime trace: App invokes ContentResolver or SAF (Storage Access Framework) queries across multiple user-data paths outside its own sandbox in background state.

AN1841 Analytic 1841

For iOS, detect Data from Local System primarily during application vetting or controlled analysis rather than assuming a live device SOC feed. Correlate Application Permission via Application Vetting [Code-signing entitlements and Info.plist privacy usage-description keys]; File Access via Application Vetting / Forensic Acquisition [Dynamic filesystem trace or acquired app-container/filesystem artifacts: App reads multiple files from Photos library, Files provider, or iCloud Drive in background or lock-adjacent state exceeding $FileReadRateThreshold$ within $DataCollectionWindow$]; OS API Execution via Application Vetting / Instrumented Dynamic Analysis [Dynamic API/runtime trace: Managed app accesses broad local data scope without user-initiated document picker interaction on supervised device]. Alert on a sequence or state change inconsistent with the device/app baseline, approved management policy, or expected user workflow; do not infer unobserved API calls, file/process activity, or payload content from management state alone.

Platforms: iOS.

  • Application Permission — mobile:ApplicationVetting; channel Code-signing entitlements and Info.plist privacy usage-description keys.
  • File Access — mobile:ForensicArtifacts; channel Dynamic filesystem trace or acquired app-container/filesystem artifacts: App reads multiple files from Photos library, Files provider, or iCloud Drive in background or lock-adjacent state exceeding $FileReadRateThreshold$ within $DataCollectionWindow$.
  • OS API Execution — mobile:DynamicAnalysis; channel Dynamic API/runtime trace: Managed app accesses broad local data scope without user-initiated document picker interaction on supervised device.
Source rule directory and observation requirements

No exact-tagged Sigma source rule was imported. This does not establish that the technique is undetectable; the MITRE guidance and collection requirements remain relevant.

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 T1533 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.

  • 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.
  • Windows and directory audit: Per principal, host and object: unusual access/request rates, new principal-object relationships, and changes outside approved administration windows. Control for service accounts and scheduled directory maintenance.
  • 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.
  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

Software observed in source procedures

These are exact ATT&CK software-use relationships, including malware. They provide behavior context, not recommendations to download or execute malware. No safety or detector claim is inherited.

52 source-documented software associations

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 T1533 Data from Local System; 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 Data from Local System, 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.

Connected ecosystem references

Linked tags

mobilemobileAndroidiOScollectionisolated_authorized_labprocedure_review_requiredtelemetry_configuration_requireddevice_or_emulator_and_sensor_access_required
No Atomic candidate in snapshottechniquesource_mapped

Detection and collection

T1533 detection workspace

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.