Complete technique description
Adversaries may insert, delete, or alter data in order to manipulate external outcomes or hide activity. By manipulating data, adversaries may attempt to affect a business process, organizational understanding, or decision making.
The type of modification and the impact it will have depends on the target application, process, and the goals and objectives of the adversary. For complex systems, an adversary would likely need special expertise and possibly access to specialized software related to the system, typically gained through a prolonged information gathering campaign, in order to have the desired impact.
Official technique definition · Open in the interactive matrix
Parent and sub-techniques
Related pages are navigation context. Their tools, rules and lab results are not automatically inherited.
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.
API Calls · source-mapped
DC0112 collection page — Instrument selected APIs in the owned test app or approved research build; log caller, API, sanitized argument class and return status.
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, api, result, thread.
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": "DC0112",
"collector": "illustrative-lab-collector",
"observation": {
"package": "test.example.lab",
"api": "android.hardware.camera2.CameraManager.openCamera",
"result": "permission_denied",
"thread": "main"
}
}
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.
Application State · source-mapped
DC0123 collection page — Instrument the owned app lifecycle and corroborate permitted service snapshots; distinguish app events from inferred gateway activity.
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, state_before, state_after, source.
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": "DC0123",
"collector": "illustrative-lab-collector",
"observation": {
"package": "test.example.lab",
"state_before": "background",
"state_after": "foreground",
"source": "own_app_callback"
}
}
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 Modification · source-mapped
DC0061 collection page — Use file-integrity monitoring or scoped OS file-write auditing for general changes. Sysmon 2 records creation-time changes; it is not a general file-write event.
Providers and collection configuration
- Microsoft Sysmon: Windows event-based collection; enable the event types needed below.
- Linux Audit: Linux alternative for supported system-call and file events; different semantics and fields.
- Apple Endpoint Security clients: macOS alternative where the subscribed event exists; requires an entitled, approved client, not an iOS collector.
- On a disposable Windows host, review the installed Sysmon schema and current configuration; merge scoped event filters into the existing policy rather than replacing it.
- Forward Microsoft-Windows-Sysmon/Operational to the lab collector. Preserve event ID, timestamp, computer, process identifiers and the original event.
- For Linux or macOS, select the equivalent sensor separately and test its emitted fields; a Windows event ID does not transfer across platforms.
Required normalized fields: path, attribute, before, after.
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": "DC0061",
"collector": "illustrative-lab-collector",
"observation": {
"path": "C:\\Lab\\demo.txt",
"attribute": "content_hash",
"before": "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"after": "bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb"
}
}
Visibility limits: Event filtering and sensor versions change coverage. High-volume image-load and process-access collection needs tuning; endpoint events alone do not establish intent.
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
- Choose one procedure implementing T1641 Data Manipulation; identify the observable behavior in the source definition, not just its tactic.
- Record exact target, tool/version, inputs, privileges, network limits and approval. Never run an imported default target automatically.
- Enable the collection sources above and verify a benign baseline reaches the detector.
- Run only the approved isolated scenario; capture native events and an independent behavior observable.
- Evaluate the exact rule/model, restore the lab, and preserve configuration, event hashes, alerts, controls and misses.