T1648 Serverless Execution — Attack Simulation

Complete technique description

Adversaries may abuse serverless computing, integration, and automation services to execute arbitrary code in cloud environments. Many cloud providers offer a variety of serverless resources, including compute engines, application integration services, and web servers.

Adversaries may abuse these resources in various ways as a means of executing arbitrary commands. For example, adversaries may use serverless functions to execute malicious code, such as crypto-mining malware (i.e. Resource Hijacking).[Cado Security Denonia] Adversaries may also create functions that enable further compromise of the cloud environment. For example, an adversary may use the `IAM:PassRole` permission in AWS or the `iam.serviceAccounts.actAs` permission in Google Cloud to add Additional Cloud Roles to a serverless cloud function, which may then be able to perform actions the original user cannot.[Rhino Security Labs AWS Privilege Escalation][Rhingo Security Labs GCP Privilege Escalation]

Serverless functions can also be invoked in response to cloud events (i.e. Event Triggered Execution), potentially enabling persistent execution over time. For example, in AWS environments, an adversary may create a Lambda function that automatically adds Additional Cloud Credentials to a user and a corresponding CloudWatch events rule that invokes that function whenever a new user is created.[Backdooring an AWS account] This is also possible in many cloud-based office application suites. For example, in Microsoft 365 environments, an adversary may create a Power Automate workflow that forwards all emails a user receives or creates anonymous sharing links whenever a user is granted access to a document in SharePoint.[Varonis Power Automate Data Exfiltration][Microsoft DART Case Report 001] In Google Workspace environments, they may instead create an Apps Script that exfiltrates a user's data when they open a file.[Cloud Hack Tricks GWS Apps Script][OWN-CERT Google App Script 2024]

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.

{
  "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"
  }
}

{
  "schema": "1200km.telemetry.example.v1",
  "synthetic": true,
  "timestamp": "2026-09-27T12:00:00Z",
  "telemetry_id": "DC0069",
  "collector": "illustrative-lab-collector",
  "observation": {
    "service": "lab-audit-service",
    "resource_id": "lab-service-1",
    "attribute": "retention_days",
    "before": 7,
    "after": 30
  }
}

Detection rules and analytic guidance

Open the dedicated detection workspace · Existing query engineering

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.

• 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.

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.

Simulation design and documented procedures

Atomic evidence: 1 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.

Technique-specific experiment contract

1. Choose one procedure implementing T1648 Serverless Execution; 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 Serverless Execution, 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.

• mitre-attack
• Microsoft DART Case Report 001
• Backdooring an AWS account
• Varonis Power Automate Data Exfiltration
• Cloud Hack Tricks GWS Apps Script
• OWN-CERT Google App Script 2024
• Cado Security Denonia
• Rhino Security Labs AWS Privilege Escalation
• Rhingo Security Labs GCP Privilege Escalation