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T1530 Data from Cloud Storage — Attack Simulation

Adversaries may access data from cloud storage. Many IaaS providers offer solutions for online data object storage such as Amazon S3, Azure Storage, and Google Cloud Storage. Similarly, SaaS enterprise platforms such as Office 365 and Google Workspace provide cloud-based document storage to users through services such as OneDrive and Google Drive, while SaaS application providers such as Slack, Confluence, Salesforce, and Dropbox may provide…

Complete technique description

Adversaries may access data from cloud storage.

Many IaaS providers offer solutions for online data object storage such as Amazon S3, Azure Storage, and Google Cloud Storage. Similarly, SaaS enterprise platforms such as Office 365 and Google Workspace provide cloud-based document storage to users through services such as OneDrive and Google Drive, while SaaS application providers such as Slack, Confluence, Salesforce, and Dropbox may provide cloud storage solutions as a peripheral or primary use case of their platform.

In some cases, as with IaaS-based cloud storage, there exists no overarching application (such as SQL or Elasticsearch) with which to interact with the stored objects: instead, data from these solutions is retrieved directly though the Cloud API. In SaaS applications, adversaries may be able to collect this data directly from APIs or backend cloud storage objects, rather than through their front-end application or interface (i.e., Data from Information Repositories).

Adversaries may collect sensitive data from these cloud storage solutions. Providers typically offer security guides to help end users configure systems, though misconfigurations are a common problem.[Amazon S3 Security, 2019][Microsoft Azure Storage Security, 2019][Google Cloud Storage Best Practices, 2019] There have been numerous incidents where cloud storage has been improperly secured, typically by unintentionally allowing public access to unauthenticated users, overly-broad access by all users, or even access for any anonymous person outside the control of the Identity Access Management system without even needing basic user permissions.

This open access may expose various types of sensitive data, such as credit cards, personally identifiable information, or medical records.[Trend Micro S3 Exposed PII, 2017][Wired Magecart S3 Buckets, 2019][HIPAA Journal S3 Breach, 2017][Rclone-mega-extortion_05_2021]

Adversaries may also obtain then abuse leaked credentials from source repositories, logs, or other means as a way to gain access to cloud storage objects.

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.

Cloud Storage Access · source-mapped

DC0025 collection page — Explicitly select relevant object-read data events; keep principal, key, request ID and result without object contents.

Providers and collection configuration

  • Choose only the lab buckets/containers and prefixes. Enable read/write data-event collection when object access is needed; management events alone are insufficient.
  • Keep administrative changes separately from object reads/writes. Set a cost budget and retention limit before enabling high-volume data logging.
  • Preserve object version/key, requesting identity, action, source address, result and request ID. Verify the collector actually receives one benign read and one benign write.

Required normalized fields: action, bucket, key, actor, result.

[
  {
    "Name": "LabObjectReads",
    "FieldSelectors": [
      {
        "Field": "eventCategory",
        "Equals": [
          "Data"
        ]
      },
      {
        "Field": "resources.type",
        "Equals": [
          "AWS::S3::Object"
        ]
      },
      {
        "Field": "resources.ARN",
        "StartsWith": [
          "arn:aws:s3:::lab-bucket/demo/"
        ]
      },
      {
        "Field": "readOnly",
        "Equals": [
          "true"
        ]
      }
    ]
  }
]

Replace the placeholder with an owned lab bucket/prefix. Review costs and merge with required selectors; applying selectors can replace an existing selection. This does not enable management-event retention or object-write coverage.

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": "DC0025",
  "collector": "illustrative-lab-collector",
  "observation": {
    "action": "get_object",
    "bucket": "lab-bucket",
    "key": "demo.txt",
    "actor": "lab-reader",
    "result": "success"
  }
}

Visibility limits: Storage metadata, listing, object access and bucket administration are different operations. Do not equate a successful request with proven exfiltration or complete content visibility.

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.

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.

Detection rules and analytic guidance

Open the dedicated detection workspace · Existing query engineering

DET0484 Multi-Platform Cloud Storage Exfiltration Behavior Chain

MITRE detection strategy

AN1329 Analytic 1329

OAuth token granted to external app followed by download of high-volume files in OneDrive/Google Drive

Platforms: SaaS.

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 T1530 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.

  • Cloud storage: Per principal and bucket/container: unusual object-read counts, transferred volume, new access pairs and bulk permission/deletion activity. Model backups, migrations and lifecycle policies separately.
  • 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.
  • 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

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

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.

Azure - Dump Azure Storage Account Objects via Azure CLI

This test dumps the content of the storage account objects present in the file defined in file_shares_csv_file_path. Note that this file is created in the atomic test T1619 "Azure - Enumerate Storage Account Objects via Key-based authentication using Azure CLI". When created manually, it must contain the columns "ResourceGroup","StorageAccountName", "FileShareName", "ContainerName", "BlobName".

Requirements:
- The test is intended to be executed in interactive mode (with -Interactive parameter) in order to complete the az login command when MFA is required.
- The EntraID user must have the role "Storage Account Contributor", or a role with similar permissions.

Pinned Atomic procedure · 67374845-b4c8-4204-adcc-9b217b65d4f1 · iaas:azure · powershell.

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

Source-defined inputs

  • output_folder: Folder path to output file share content to (type path; source default $env:temp\T1530_storage_account_objects)
  • storage_account_objects_csv_file_path: Path to file that contains all storage account objects in form of a csv file. This may be the result from Test T1619 "Azure - Enumerate Storage Account Objects via Key-based authentication using Azure CLI". (type path; source default $env:temp\T1619_storage_account_objects.csv)

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:

  • Azure CLI must be installed
AWS - Scan for Anonymous Access to S3

Upon successful execution, this test will test for anonymous access to AWS S3 buckets and dumps all the files to a local folder.

Pinned Atomic procedure · 979356b9-b588-4e49-bba4-c35517c484f5 · iaas:aws · sh.

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

Source-defined inputs

  • s3_bucket_name: Name of the bucket (type string; source default redatomic-test2)

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:

  • Check if ~/.aws/credentials file has a default stanza is configured

Technique-specific experiment contract

  1. Choose one procedure implementing T1530 Data from Cloud Storage; 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 Data from Cloud Storage, 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

Source procedure platform / method / executor tags:

Detection and collection

T1530 detection workspace

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

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.