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T0882 Theft of Operational Information — Attack Simulation

Adversaries may steal operational information on a production environment as a direct mission outcome for personal gain or to inform future operations. This information may include design documents, schedules, rotational data, or similar artifacts that provide insight on operations. In the Bowman Dam incident, adversaries probed systems for operational data.

Complete technique description

Adversaries may steal operational information on a production environment as a direct mission outcome for personal gain or to inform future operations. This information may include design documents, schedules, rotational data, or similar artifacts that provide insight on operations. In the Bowman Dam incident, adversaries probed systems for operational data. [Mark Thompson March 2016] [Danny Yadron December 2015]

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.

Analyst-proposed lab instrumentation, not an official ATT&CK component mapping or a validated detection. It may observe effects without establishing malicious intent.

Operational document access audit · planning input

proposed-operational-document-access-audit collection page — Enable document-read/download audit on the lab repository; retain document ID, identity and result without storing document bodies.

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: document_id, actor, action, 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": "proposed-operational-document-access-audit",
  "collector": "illustrative-lab-collector",
  "observation": {
    "document_id": "lab-procedure-1",
    "actor": "lab-reader",
    "action": "download",
    "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.

Egress and transfer records · planning input

proposed-egress-and-transfer-records collection page — Collect outbound flow/proxy/application transfer metadata with direction and bytes; distinguish application exports from network delivery.

Providers and collection configuration

  • Zeek: Connection and protocol metadata from traffic visible to the sensor.
  • Suricata EVE: Configured flow, alert, DNS, HTTP, TLS and protocol records.
  • AWS VPC Flow Logs: IP traffic metadata alternative; no packet payload or DNS answer history, and some traffic is not logged.
  • Use an authorized lab TAP/SPAN, virtual mirror or gateway interface. Define which traffic crosses it; avoid assuming visibility into every segment.
  • Enable the required Zeek analyzers or Suricata EVE event types. Export logs with sensor identity, clock synchronization and flow correlation identifiers.
  • Monitor capture loss, truncation and exporter sampling. Capture payload only with approval and restrictive retention; encryption normally prevents plaintext inspection.

Required normalized fields: source, destination, direction, bytes_sent.

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": "proposed-egress-and-transfer-records",
  "collector": "illustrative-lab-collector",
  "observation": {
    "source": "192.0.2.10",
    "destination": "198.51.100.20",
    "direction": "outbound",
    "bytes_sent": 128
  }
}

Visibility limits: Flows are not packet payloads. NAT, asymmetric routes, encryption, missing mirrors and sampling can hide attribution or content. A destination connection alone does not prove malicious intent.

Detection rules and analytic guidance

Open the dedicated detection workspace · Existing query engineering

DET0732 Detection of Theft of Operational Information

MITRE detection strategy

AN1865 Analytic 1865

No standard detection method currently exists for this technique.

Platforms: None.

No structured log-source mapping supplied by this analytic.

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 T0882 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.

  • 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.
  • Network and protocol telemetry: Per source and observation point: connection rate, destination-host/port fan-out, response/failure ratio and previously unseen destinations. Compare equivalent time windows; separate authorized scanners, NAT and sampling effects.
  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.

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.

4 source-documented software associations
  • S0038 Duqu — malware.

    Duqu's purpose is to gather intelligence data and assets from entities such as industrial infrastructure and system manufacturers, amongst others not in the industrial sector, in order to more easily conduct a future attack against another third party.[Symantec W32.Duqu]

    Exact relationship: relationship--792324b4-064a-430c-8ffc-7f7acd537778
  • S0143 Flame — malware.

    Flame can collect AutoCAD design data and visio diagrams as well as other documents that may contain operational information. [Kevin Savage and Branko Spasojevic]

    Exact relationship: relationship--79d05cb2-ded0-4847-b52e-af7af421f303
  • S0496 REvil — malware.

    REvil sends exfiltrated data from the victims system using HTTPS POST messages sent to the C2 system. [McAfee Labs October 2019] [SecureWorks September 2019]

    Exact relationship: relationship--6ed07095-c23a-4676-807f-a544deaeb274
  • S1000 ACAD/Medre.A — malware.

    ACAD/Medre.A can collect AutoCad files with drawings. These drawings may contain operational information. [ESET]

    Exact relationship: relationship--228b9a13-0545-4ecf-99ff-be02addaf7fe

Simulation design and documented procedures

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

Use a process simulator or isolated test rig with no connection to production controllers or physical actuators. Preserve safety interlocks; destructive effects require synthetic evidence, not real equipment damage.

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 T0882 Theft of Operational Information; 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 Theft of Operational Information, 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 c9ca0ce523a1b5e512060a390415e8aefe8f6d32c5f82d7de81264960c1e1183. Definition and procedure context are reproduced from this snapshot, not independently re-investigated incidents.

Source-linked mitigations

Connected ecosystem references

Linked tags

Detection and collection

T0882 detection workspace

No reviewed association in this snapshot.

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.