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T1687 Exploitation for Defense Impairment — Attack Simulation

Adversaries may exploit vulnerabilities in security software, infrastructure, or defensive components to degrade, disable, or otherwise continue to impair their ability to prevent, detect, or respond to malicious activity. Adversaries may exploit a system or application vulnerability to directly interfere with defensive mechanisms. Exploitation occurs when an adversary takes advantage of a programming error in software, services, or the…

Complete technique description

Adversaries may exploit vulnerabilities in security software, infrastructure, or defensive components to degrade, disable, or otherwise continue to impair their ability to prevent, detect, or respond to malicious activity.

Adversaries may exploit a system or application vulnerability to directly interfere with defensive mechanisms. Exploitation occurs when an adversary takes advantage of a programming error in software, services, or the operating system to execute adversary-controlled code, often with the goal of weakening or disabling protections.

Vulnerabilities may exist in security tools such as antivirus, endpoint detection and response (EDR), firewalls, or other monitoring solutions. Adversaries may use prior reconnaissance or perform discovery activities (e.g., Software Discovery) to identify defensive tools present in an environment and target them for exploitation.

Successful exploitation may allow adversaries to terminate security processes, disable protections, bypass enforcement mechanisms, or reduce the effectiveness of defensive controls. In some cases, vulnerabilities in cloud-based or SaaS infrastructure may also be leveraged to bypass built-in security boundaries or disrupt visibility and enforcement across environments.[Salesforce zero-day in facebook phishing attack]

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.

Cloud Service Disable · source-mapped

DC0090 collection page — Collect service disable/stop-logging administrative actions and independent health/configuration checks; monitor the audit destination itself.

Providers and collection configuration

  • In the isolated account/project/subscription, route administrative audit logs to a protected destination with retention and access controls.
  • Select the relevant service, regions and read/write operations. Confirm whether the API below is a management or data event in the provider documentation.
  • Preserve actor/session, resource, request ID, service, operation, region and error details. Check delivery latency and avoid logging credentials or sensitive request bodies.

Required normalized fields: resource_id, action, enabled_before, enabled_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": "DC0090",
  "collector": "illustrative-lab-collector",
  "observation": {
    "resource_id": "lab-service-1",
    "action": "disable",
    "enabled_before": true,
    "enabled_after": false
  }
}

Visibility limits: AWS, Azure and Google expose different APIs and event schemas. An accepted API request does not prove the final resource state; join asynchronous outcomes and inventory.

Command Execution · source-mapped

DC0064 collection page — Collect process command lines and relevant shell/interpreter audit; shell history alone is neither complete nor tamper-resistant.

Providers and collection configuration

  • Enable Script Block Logging through the policy/configuration for the installed edition. Forward Microsoft-Windows-PowerShell/Operational for Windows PowerShell, or PowerShellCore/Operational for PowerShell 7.
  • Retain script-block ID and fragment ordering alongside process creation. Protect access to collected script text; secrets may be included.
  • On Linux, use narrowly scoped Audit execution rules for the lab account and supported architectures, and retain joined SYSCALL/EXECVE/PATH records.

Required normalized fields: interpreter, command_line, actor_uid, session_id.

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": "DC0064",
  "collector": "illustrative-lab-collector",
  "observation": {
    "interpreter": "/bin/sh",
    "command_line": "printf LAB_TELEMETRY_CHECK",
    "actor_uid": 1000,
    "session_id": "lab-session-1"
  }
}

Visibility limits: Script content and process command lines are different signals. Logging may be partial, fragmented, filtered or disabled; never assume a command line reveals everything executed.

Driver Load · source-mapped

DC0079 collection page — Collect Sysmon DriverLoad (6) and retain image path, hash and signature result; distinguish load from file installation.

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: driver_path, action, signature_status.

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": "DC0079",
  "collector": "illustrative-lab-collector",
  "observation": {
    "driver_path": "C:\\Lab\\demo.sys",
    "action": "load",
    "signature_status": "lab_fixture_only"
  }
}

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.

Driver Metadata · source-mapped

DC0074 collection page — Collect signed-driver inventory with hash algorithm and signer verification result; differentiate installed files from loaded drivers.

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: driver_path, version, signed, loaded.

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": "DC0074",
  "collector": "illustrative-lab-collector",
  "observation": {
    "driver_path": "C:\\Lab\\demo.sys",
    "version": "1.0-lab",
    "signed": false,
    "loaded": false
  }
}

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.

Firewall Rule Modification · source-mapped

DC0051 collection page — Collect firewall policy audit plus before/after rule snapshots; identify local versus centrally managed policy and the responsible administrator.

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: rule_id, 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": "DC0051",
  "collector": "illustrative-lab-collector",
  "observation": {
    "rule_id": "lab-rule-1",
    "attribute": "remote_address",
    "before": "192.0.2.10",
    "after": "192.0.2.11"
  }
}

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.

Instance Modification · source-mapped

DC0073 collection page — Collect compute update/modify APIs and before/after state, including attached role, network interfaces and launch parameters where exposed.

Providers and collection configuration

  • In the isolated account/project/subscription, route administrative audit logs to a protected destination with retention and access controls.
  • Select the relevant service, regions and read/write operations. Confirm whether the API below is a management or data event in the provider documentation.
  • Preserve actor/session, resource, request ID, service, operation, region and error details. Check delivery latency and avoid logging credentials or sensitive request bodies.

Required normalized fields: instance_id, 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": "DC0073",
  "collector": "illustrative-lab-collector",
  "observation": {
    "instance_id": "vm-lab-1",
    "attribute": "instance_type",
    "before": "small-lab",
    "after": "medium-lab"
  }
}

Visibility limits: AWS, Azure and Google expose different APIs and event schemas. An accepted API request does not prove the final resource state; join asynchronous outcomes and inventory.

Network Traffic Content · source-mapped

DC0085 collection page — Scope approved packet capture/protocol content collection to the lab flow; retain capture provenance and truncation indicators.

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.
  • 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: flow_id, protocol, method, path, payload_excerpt.

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": "DC0085",
  "collector": "illustrative-lab-collector",
  "observation": {
    "flow_id": "lab-flow-1",
    "protocol": "http",
    "method": "GET",
    "path": "/lab-check",
    "payload_excerpt": "LAB_TELEMETRY_CHECK"
  }
}

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.

Process Creation · source-mapped

DC0032 collection page — Collect Sysmon ProcessCreate (1); preserve ProcessGuid and parent identifiers. Security 4688 is an alternative with separate audit/command-line settings.

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: process_guid, image, parent_image, command_line, user.

<EventFiltering>
  <ProcessCreate onmatch="include">
    <Image condition="is">C:\Windows\System32\whoami.exe</Image>
  </ProcessCreate>
</EventFiltering>

This fragment only selects the named process. It is not a complete production policy. Inspect the installed schema with sysmon64 -s; preserve existing rules, then apply the reviewed complete configuration with sysmon64 -c <configuration-file>.

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": "DC0032",
  "collector": "illustrative-lab-collector",
  "observation": {
    "process_guid": "lab-process-001",
    "image": "C:\\Windows\\System32\\whoami.exe",
    "parent_image": "C:\\Windows\\System32\\cmd.exe",
    "command_line": "whoami",
    "user": "LAB\\analyst"
  }
}

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.

Process Metadata · source-mapped

DC0034 collection page — Collect read-only process inventory with PID plus start time/stable identity, command line and collection time; restrict access to potentially sensitive arguments.

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: process_id, image, owner_uid, started_at.

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": "DC0034",
  "collector": "illustrative-lab-collector",
  "observation": {
    "process_id": 4200,
    "image": "/usr/bin/sleep",
    "owner_uid": 1000,
    "started_at": "2026-09-27T11:59:00Z"
  }
}

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.

Service Metadata · source-mapped

DC0041 collection page — Inventory service name, executable, startup mode, owner and status using supported OS APIs; compare successive snapshots.

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: service, executable, start_mode, state.

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": "DC0041",
  "collector": "illustrative-lab-collector",
  "observation": {
    "service": "LabService",
    "executable": "C:\\Lab\\service.exe",
    "start_mode": "manual",
    "state": "stopped"
  }
}

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.

Detection rules and analytic guidance

Open the dedicated detection workspace · Existing query engineering

DET0900 Detection of Defense Impairment

MITRE detection strategy

AN2038 Analytic 2038

Detects suspicious interactions with security products followed by service crashes, unexpected restarts, driver unloads, telemetry gaps, or tamper-state changes. Correlates exploit precursor behavior with immediate degradation of defensive services and follow-on process execution.

Platforms: Windows.

AN2039 Analytic 2039

Detects exploitation attempts against security daemons or kernel security modules followed by daemon termination, disabled logging, module unload, audit stoppage, or reduced endpoint telemetry. Correlates local execution or network input with control degradation.

Platforms: Linux.

  • Command Execution — auditd:EXECVE; channel execve, kill, ptrace, insmod, rmmod targeting security processes.
  • Service Metadata — auditd:DAEMON; channel auditd stopped, config changed, logging suspended.

AN2042 Analytic 2042

Detects exploitation or abuse of SaaS security workflows resulting in disabled alerts, reduced retention, bypassed enforcement, role escalation, or tokenized persistence that weakens monitoring. Correlates unusual admin/API activity with visibility reduction.

Platforms: SaaS.

AN2040 Analytic 2040

Detects crafted activity resulting in crashes or impairment of endpoint security extensions, network filters, launch daemons, or telemetry agents. Correlates process activity, system extension state changes, and telemetry interruption.

Platforms: macOS.

  • Process Metadata — macos:unifiedlog; channel Crash or abnormal termination of security agent or system extension host.
  • Driver Metadata — macos:unifiedlog; channel Extension disabled, unloaded, failed to start.
  • Network Traffic Content — NSM:Flow; channel Traffic spike preceding control crash.

AN2041 Analytic 2041

Detects exploitation of cloud-native security boundaries or management components followed by disabled logging, detached agents, changed security groups, policy bypass, or telemetry suppression. Correlates suspicious API activity with reduced control coverage.

Platforms: IaaS.

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 T1687 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.
  • Cloud control plane: Per account, role, region and API: rare actions, new role-resource relationships, enumeration bursts and configuration changes. Separate deployment automation and organization-level administrative activity.
  • Script and command execution: Per host role and principal: rare interpreters, command argument patterns and parent-child sequences. Normalize scripts carefully; obfuscation, administrative automation and missing command lines change visibility.
  • Endpoint activity: Per host role and user: rare process/object combinations, bursts of object access or modification, and ordered parent/action sequences. Compare maintenance windows and signed software rollout activity.
  • 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.
  • 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.
  • 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.

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 T1687 Exploitation for Defense Impairment; 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 Exploitation for Defense Impairment, 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.

Connected ecosystem references

Linked tags

Detection and collection

T1687 detection workspace

No reviewed association in this snapshot.

Existing research

Threat Matrix: knowledge routes, evidence and actor context

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.