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T1036 Masquerading — Attack Simulation

Adversaries may attempt to manipulate features of their artifacts to make them appear legitimate or benign to users and/or security tools. Masquerading occurs when the name or location of an object, legitimate or malicious, is manipulated or abused for the sake of evading defenses and observation. This may include manipulating file metadata, tricking users into misidentifying the file type, and giving legitimate task or service names. Renaming…

Complete technique description

Adversaries may attempt to manipulate features of their artifacts to make them appear legitimate or benign to users and/or security tools. Masquerading occurs when the name or location of an object, legitimate or malicious, is manipulated or abused for the sake of evading defenses and observation. This may include manipulating file metadata, tricking users into misidentifying the file type, and giving legitimate task or service names.

Renaming abusable system utilities to evade security monitoring is also a form of Masquerading.[LOLBAS Main Site]

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.

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.

File Metadata · source-mapped

DC0059 collection page — Collect scoped file inventory/FIM metadata with hash algorithm and observation time; preserve original timestamps separately from collector time.

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: path, size_bytes, sha256, owner_uid.

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": "DC0059",
  "collector": "illustrative-lab-collector",
  "observation": {
    "path": "/opt/lab/demo.txt",
    "size_bytes": 64,
    "sha256": "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
    "owner_uid": 1000
  }
}

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.

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.

Image Metadata · source-mapped

DC0028 collection page — Collect authorized cloud VM image inventory with stable image IDs and observation time; this component is not container registry metadata.

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: image_id, owner, state, platform, public.

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": "DC0028",
  "collector": "illustrative-lab-collector",
  "observation": {
    "image_id": "image-lab-1",
    "owner": "lab-account",
    "state": "available",
    "platform": "Linux",
    "public": 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.

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 Creation · source-mapped

DC0060 collection page — Collect service-install auditing through Audit Security System Extension where supported, plus Service Control Manager/System logs and the new service configuration.

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: service, action, executable, actor.

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": "DC0060",
  "collector": "illustrative-lab-collector",
  "observation": {
    "service": "LabService",
    "action": "install",
    "executable": "C:\\Lab\\service.exe",
    "actor": "lab-admin"
  }
}

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.

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

DET0127 Behavioral Detection of Masquerading Across Platforms via Metadata and Execution Discrepancy

MITRE detection strategy

AN0355 Analytic 0355

Adversary renames LOLBINs or deploys binaries with spoofed file names, internal PE metadata, or misleading icons to appear legitimate. File creation is followed by execution or service registration inconsistent with known usage.

Platforms: Windows.

AN0356 Analytic 0356

Adversary drops renamed binaries in uncommon directories (e.g., /tmp, /dev/shm) or uses special characters in names (e.g., trailing space, Unicode RLO). Execution or cronjob registration follows shortly after file drop.

Platforms: Linux.

AN0357 Analytic 0357

Adversary creates disguised launch daemons or apps with misleading names and bundle metadata (e.g., Info.plist values inconsistent with binary path or icon). Launch is correlated with user logon or persistence setup.

Platforms: macOS.

AN0358 Analytic 0358

Adversary uses renamed container images, injects files into containers with misleading names or metadata (e.g., renamed system binaries), and executes them during startup or scheduled jobs.

Platforms: Containers.

AN0359 Analytic 0359

Adversary places scripts or binaries with misleading names in /etc/rc.local.d or /var/spool/cron, or registers services with legitimate-sounding names not present in default ESXi builds.

Platforms: ESXi.

  • Service Metadata — esxi:hostd; channel registers services with legitimate-sounding names.
  • Command Execution — esxi:shell; channel scripts or binaries with misleading names.
Source rule directory and observation requirements

38 exact-tagged source rules. A source tag is a discovery aid, not a relevance verdict. Check the observation point and actual condition of each rule.

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

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.

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  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.

33 source-documented software associations

Simulation design and documented procedures

Atomic evidence: 2 compatible documented candidates. 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.

Malware Masquerading and Execution from Zip File

When the file is unzipped and the README.cmd file opened, it executes and changes the .pdf to .dll and executes the dll. This is a BazaLoader technique as reported here

Pinned Atomic procedure · 4449c89b-ec82-43a4-89c1-91e2f1abeecc · windows · powershell.

Elevation: not declared required. Cleanup: defined upstream; review required. This is source documentation, not an execution approval.

Source-defined inputs

  • url: Location of zip file (type url; source default https://github.com/redcanaryco/atomic-red-team/raw/master/atomics/T1036/bin/T1036.zip)

Review the upstream command, dependencies and cleanup at the source link. Source defaults may refer to real infrastructure or destructive actions; they are not authorized targets. No automatic install, prerequisite download or command execution is provided.

1 prerequisite definitions:

  • Zip file must be present.
System File Copied to Unusual Location

It may be suspicious seeing a file copy of an EXE in System32 or SysWOW64 to a non-system directory or executing from a non-system directory.

Pinned Atomic procedure · 51005ac7-52e2-45e0-bdab-d17c6d4916cd · windows · powershell.

Elevation: not declared required. Cleanup: defined upstream; review required. This is source documentation, not an execution approval.

No input arguments declared.

Review the upstream command, dependencies and cleanup at the source link. Source defaults may refer to real infrastructure or destructive actions; they are not authorized targets. No automatic install, prerequisite download or command execution is provided.

Technique-specific experiment contract

  1. Choose one procedure implementing T1036 Masquerading; 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 Masquerading, 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.

Source-linked mitigations
  • M1017 User Training

    Train users not to open email attachments or click unknown links (URLs). Such training fosters more secure habits within your organization and will limit many of the risks.

  • M1018 User Account Management

    Consider defining and enforcing a naming convention for user accounts to more easily spot generic account names that do not fit the typical schema.

  • M1022 Restrict File and Directory Permissions

    Use file system access controls to protect folders such as C:\\Windows\\System32.

  • M1038 Execution Prevention

    Use tools that restrict program execution via application control by attributes other than file name for common operating system utilities that are needed.

  • M1040 Behavior Prevention on Endpoint

    Implement security controls on the endpoint, such as a Host Intrusion Prevention System (HIPS), to identify and prevent execution of potentially malicious files (such as those with mismatching file signatures).

  • M1045 Code Signing

    Require signed binaries.

  • M1047 Audit

    Audit user accounts to ensure that each one has a defined purpose.

  • M1049 Antivirus/Antimalware

    Anti-virus can be used to automatically quarantine suspicious files.

Connected ecosystem references

Linked tags

Source procedure platform / method / executor tags:

Detection and collection

T1036 detection workspace

No reviewed association in this snapshot.

Threat actor context

These are explicit actor-to-technique associations in the existing Threat Matrix snapshot, not attribution of an event or proof that a detector identifies the actor. No tool-to-actor relationship is inferred.

Existing research

Threat Matrix: knowledge routes, evidence and actor context

Connected anomaly research

Curated research views reached through an exact source technique, a catalog model, or a reviewed collection reference. These are navigation associations, not claims of detector effectiveness or sensor equivalence.

Telemetry contracts · Maintained query examples · Validation and blind spots

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.