T1608.007 AI Result Poisoning — Attack Simulation

Complete technique description

Adversaries may manipulate publicly accessible web content to influence generative AI systems or AI-powered search engines into retrieving, citing, or recommending attacker-controlled information.[Digiday AI Result Poisoning][Netcraft AI Summaries]

Unlike traditional SEO Poisoning, which primarily attempts to influence the ordering of conventional search results, AI Result Poisoning targets systems that synthesize information from multiple sources and provide a direct answer to the user. Adversaries may create malicious or misleading websites, publish fabricated claims, generate supporting articles, or establish multiple apparently independent sources that reinforce the same information. These sources may then be retrieved by AI systems using web search or retrieval-augmented generation and incorporated into generated responses.

Adversaries may use this technique to recommend or link to phishing sites, fraudulent online stores, malicious downloads, impersonation pages, or other attacker-controlled infrastructure.[dev.to Abusing AI Chatbot Recommendations][darkreading geo poisoning][darkreading ai result poisoning] It can also be used to promote misinformation or fabricated claims without requiring access to the underlying AI model, system prompt, or application infrastructure.[Lasso Security AI Engine Poisoning] Because the malicious content can resemble ordinary marketing, editorial, or informational material, traditional prompt injection and malware-focused detections may not identify the activity.

Official technique definition · Open in the interactive matrix

Parent and sub-techniques

• T1608 Stage Capabilities — parent

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.

The upstream guidance describes limited target visibility. These are conditional investigation inputs, not direct victim-side detection of preparation. Simulator-side logs do not prove defender visibility.

{
  "schema": "1200km.telemetry.example.v1",
  "synthetic": true,
  "timestamp": "2026-09-27T12:00:00Z",
  "telemetry_id": "proposed-external-intelligence-context-only",
  "collector": "illustrative-lab-collector",
  "observation": {
    "indicator": "lab.example.test",
    "source_record": "lab-feed-1",
    "confidence": "context_only",
    "victim_access_observed": false
  }
}

{
  "schema": "1200km.telemetry.example.v1",
  "synthetic": true,
  "timestamp": "2026-09-27T12:00:00Z",
  "telemetry_id": "proposed-provider-side-audit-if-lawfully-available",
  "collector": "illustrative-lab-collector",
  "observation": {
    "provider_record_id": "lab-disclosure-1",
    "access_basis": "authorized_test_account_export",
    "action": "account_created",
    "victim_network_observation": false
  }
}

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

No exact technique-specific Atlas model is present for T1608.007 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.

• External context and intelligence: Changes in external infrastructure, registration or published intelligence can supply context. They do not expose private attacker actions or establish a victim-side detector; preserve timestamps and attribution uncertainty.

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 T1608.007 AI Result Poisoning; 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 AI Result Poisoning, 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
• darkreading geo poisoning
• darkreading ai result poisoning
• dev.to Abusing AI Chatbot Recommendations
• Lasso Security AI Engine Poisoning
• Netcraft AI Summaries
• Digiday AI Result Poisoning