1200kmSECURITY RESEARCH
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T1550 Use Alternate Authentication Material — Attack Simulation

Adversaries may use alternate authentication material, such as password hashes, Kerberos tickets, and application access tokens, in order to move laterally within an environment and bypass normal system access controls. Authentication processes generally require a valid identity (e.g., username) along with one or more authentication factors (e.g., password, pin, physical smart card, token generator, etc.). Alternate authentication material is…

Complete technique description

Adversaries may use alternate authentication material, such as password hashes, Kerberos tickets, and application access tokens, in order to move laterally within an environment and bypass normal system access controls.

Authentication processes generally require a valid identity (e.g., username) along with one or more authentication factors (e.g., password, pin, physical smart card, token generator, etc.). Alternate authentication material is legitimately generated by systems after a user or application successfully authenticates by providing a valid identity and the required authentication factor(s). Alternate authentication material may also be generated during the identity creation process.[NIST Authentication][NIST MFA]

Caching alternate authentication material allows the system to verify an identity has successfully authenticated without asking the user to reenter authentication factor(s). Because the alternate authentication must be maintained by the system—either in memory or on disk—it may be at risk of being stolen through Credential Access techniques. By stealing alternate authentication material, adversaries are able to bypass system access controls and authenticate to systems without knowing the plaintext password or any additional authentication factors.

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.

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.

Logon Session Creation · source-mapped

DC0067 collection page — Enable Audit Logon and collect successful session creation on the receiving host; join session/logon IDs rather than only account names.

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: account, logon_id, logon_type, source_ip.

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": "DC0067",
  "collector": "illustrative-lab-collector",
  "observation": {
    "account": "LAB\\analyst",
    "logon_id": "0x12345",
    "logon_type": 3,
    "source_ip": "192.0.2.10"
  }
}

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.

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.

User Account Authentication · source-mapped

DC0002 collection page — Collect successful and failed sign-ins, authentication method, application and conditional-access context; distinguish account validation from session creation.

Providers and collection configuration

  • Enable/export the required identity sign-in and audit categories from the lab tenant to a protected destination. Document license, role and retention requirements.
  • Correlate issuance or administrative changes with later sign-ins using non-secret credential IDs, application IDs, user IDs and correlation IDs.
  • Exclude passwords, bearer tokens, cookies and private keys. Record success, failure, authentication context and application result separately.

Required normalized fields: account, application, result, reason, source_ip.

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": "DC0002",
  "collector": "illustrative-lab-collector",
  "observation": {
    "account": "analyst@example.test",
    "application": "lab-portal",
    "result": "failure",
    "reason": "invalid_credential",
    "source_ip": "192.0.2.10"
  }
}

Visibility limits: A token creation record is not evidence of token use; a sign-in is not a complete record of every resource access. Some provider-side data is unavailable to the customer.

User Account Metadata · source-mapped

DC0013 collection page — Export an authorized directory/account inventory with stable IDs, roles, enabled state and collection time; compare snapshots without exposing credentials.

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: account_id, enabled, roles, inventory_time.

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": "DC0013",
  "collector": "illustrative-lab-collector",
  "observation": {
    "account_id": "user-lab-1",
    "enabled": true,
    "roles": [
      "lab-reader"
    ],
    "inventory_time": "2026-09-27T12:00:00Z"
  }
}

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.

Web Credential Usage · source-mapped

DC0007 collection page — Join workload/non-interactive sign-in or application access records to non-secret credential identifiers and resource results.

Providers and collection configuration

  • Enable/export the required identity sign-in and audit categories from the lab tenant to a protected destination. Document license, role and retention requirements.
  • Correlate issuance or administrative changes with later sign-ins using non-secret credential IDs, application IDs, user IDs and correlation IDs.
  • Exclude passwords, bearer tokens, cookies and private keys. Record success, failure, authentication context and application result separately.

Required normalized fields: credential_id, resource, action, result, source_ip.

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": "DC0007",
  "collector": "illustrative-lab-collector",
  "observation": {
    "credential_id": "key-lab-1",
    "resource": "lab-api",
    "action": "authenticate",
    "result": "success",
    "source_ip": "192.0.2.10"
  }
}

Visibility limits: A token creation record is not evidence of token use; a sign-in is not a complete record of every resource access. Some provider-side data is unavailable to the customer.

Detection rules and analytic guidance

Open the dedicated detection workspace · Existing query engineering

DET0338 Behavioral Detection Strategy for Use Alternate Authentication Material (T1550)

MITRE detection strategy

AN0954 Analytic 0954

Use of stolen Kerberos tickets or token impersonation resulting in logon sessions from accounts without expected interactive logon events.

Platforms: Windows.

AN0955 Analytic 0955

Access tokens or SSH keys used without corresponding login shell or PAM module activity, particularly for remote execution.

Platforms: Linux.

AN0956 Analytic 0956

Token replay or impersonation in federated logins without interactive browser session or MFA prompts.

Platforms: Identity Provider.

AN0958 Analytic 0958

Container process uses mounted cloud credentials or token cache to authenticate without known orchestration.

Platforms: Containers.

AN0959 Analytic 0959

Access token reuse to connect to SharePoint or Outlook APIs without interactive user context.

Platforms: Office Suite.

Source rule directory and observation requirements

4 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

No exact technique-specific Atlas model is present for T1550 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.
  • 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.
  • 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.
  • Identity and SaaS: Per identity and application: first-seen clients/resources, changes in authentication failures and session patterns, and unusual permission changes. Separate automation, travel, shared egress and identity-provider policy changes.
  • 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.
  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.

1 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 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 T1550 Use Alternate Authentication Material; 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 Use Alternate Authentication Material, 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

Connected ecosystem references

Linked tags

Detection and collection

T1550 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.