MITRE ATLAS 2026.09 / technique reference
AML.T0051
LLM Prompt Injection
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MITRE source definition
An adversary may craft malicious prompts as inputs to an LLM that cause the LLM to act in unintended ways. These "prompt injections" are often designed to cause the model to ignore aspects of its original instructions and follow the adversary's instructions instead.
Prompt Injections can be an initial access vector to the LLM that provides the adversary with a foothold to carry out other steps in their operation. They may be designed to bypass defenses in the LLM, or allow the adversary to issue privileged commands. The effects of a prompt injection can persist throughout an interactive session with an LLM.
Malicious prompts may be injected directly by the adversary (Direct) either to leverage the LLM to generate harmful content or to gain a foothold on the system and lead to further effects. Prompts may also be injected indirectly when the LLM, as part of its normal operation, ingests the malicious prompt from another data source (Indirect). This type of injection can be used by the adversary to gain a foothold on the system or to target the user of the LLM. Malicious prompts may also be Triggered by user actions or system events.
Source modified 2026-05-27. Reproduced from the pinned ATLAS release; inline technique links resolve to local reference pages.
Parent, sub-techniques and ATT&CK references
Source-backed defensive context
MITRE mitigations
- AML.M0019 Control Access to AI Models and Data in Production
- AML.M0020 Generative AI Guardrails
- AML.M0021 Generative AI Guidelines
- AML.M0022 Generative AI Model Alignment
- AML.M0024 AI Telemetry Logging
- AML.M0033 Input and Output Validation for AI Agent Components
- AML.M0035 AI Red Team
MITRE case studies
These are explicit source relationships, not independently reproduced incidents or validated detection coverage. The local relationship indexes link back to the pinned MITRE source.
Detection and collection design
No executable detector is supplied by this ATLAS source import. Define detection against the specific behavior in the full definition above. MITRE case-study and mitigation relationships supply context, not tested rule coverage.
MITRE explicitly links AI Telemetry Logging to this technique. Use that guidance to identify system-appropriate records.
Depending on the selected scenario, the experiment may need dataset/model versions, data-access and build audit records, inference or application traces, or agent tool authorization records. These are planning options, not asserted mappings. Minimize sensitive prompt/data capture and retain provenance.
Anomaly detection design
No exact ATLAS anomaly detector is validated here. Select a measurable feature from the behavior definition; compare like-for-like model versions, workloads and identities. Changes caused by model upgrades, retrieval corpora or legitimate workload shifts must be controls, not automatically labelled attacks.
Tools and reproducible source scenarios
No lab-tool association is invented from the technique name. Inspect the exact source scenarios below for the tools and environment actually used; a case study is not itself an executable or safe lab.
Simulation experiment plan
Select an owned, disposable system implementing AML.T0051 LLM Prompt Injection. Use synthetic data and sandboxed tools; disable production credentials, external side effects and data exfiltration. For poisoning or supply-chain tests, use throwaway artifacts rather than real training or deployment pipelines.
- State the exact trust boundary and expected observable behavior.
- Pin model, application, dataset, tool permissions and evaluation inputs.
- Capture baseline and scenario traces, an authorized look-alike, and a missing-telemetry control.
- Evaluate the detector against independently labelled results; do not substitute model output claims for observed tool actions.
- Restore state and preserve redacted evidence.
Experiment worksheet — not fabricated telemetry
The export below is a planning record, not a native event, positive attack fixture or execution result. Unknown fields deliberately remain null.
{
"schema": "1200km.atlas.experiment-plan.v1",
"technique_id": "AML.T0051",
"simulation": false,
"target": "REQUIRES_OWNED_LAB",
"source_scenario": [
"AML.CS0037"
],
"input_artifact_hash": null,
"system_version": null,
"expected_observable": null,
"native_telemetry_location": null,
"baseline_reference": null,
"result": "not_run"
}Simulation and telemetry boundary
This is a technique reference page, not a runnable simulation. No ATLAS-specific telemetry mapping, local attack execution or detector validation is asserted. MITRE maturity describes its source evidence, not a 1200km lab result.
For broader context—not technique-specific control mappings—see AI Security, AI Security Course, and detection-validation methodology.
Provenance and attribution
Immutable MITRE ATLAS source · Import provenance · Attribution and transformation notice · Apache License 2.0
Copyright 2021-2026 MITRE. Source text and explicit relationships are retained; navigation, formatting and local links are provided by 1200km.
No explicit relationship in this pinned source.