MITRE ATLAS 2026.09 / technique reference

AML.T0054
LLM Jailbreak

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Generative AIAgentic AIDefense EvasionPrivilege EscalationSource maturity: Realized

MITRE source definition

Adversaries may induce a large language model (LLM) to ignore, circumvent, or override its safety/alignment behaviors and/or guardrails to elicit outputs the model is intended to withhold. Once jailbroken, the LLM may be used in unintended ways by the adversary. Jailbreaks may be achieved via adversarial prompting, or by modifying model weights or safety mechanisms.

Adversaries may attempt a jailbreak for Defense Evasion of the LLM's guidelines and guardrails itself to then reveal information (ex: LLM Data Leakage, Discover LLM System Information) or generate harmful content (ex: Generate Malicious Commands, Spearphishing via Social Engineering LLM). They may also jailbreak a model for Privilege Escalation to invoke tools or perform actions for their own purposes (ex: AI Agent Tool Invocation) or abuse the agent for a Command and Control channel (ex: AI Agent).

Adversaries use a variety of strategies to craft jailbreak prompts. Prompts may target specific models or model families and are iterated upon until successful. Model providers actively update their model guardrails to make them more resistant to jailbreak prompts as new prompts are developed. Common strategies [[jailbreak-guide]] include but are not limited to:

- Instruction override: Use phrasing that attempts to supersede prior constraints (e.g. "ignore previous instructions"). - Roleplay / persona switching: Instruct the LLM to adopt an identity or mode that allows unrestricted answers (e.g. "as a security researcher"). - Fictionalization and hypotheticals: Instruct the LLM to include disallowed content as part of a story, screenplay, or educational scenario. - Separate intent from content: request analysis, examples, templates, or edge cases, that implicitly contain disallowed content. - Multi-turn escalation / Crescendo: Utilize a sequence of prompts that start benign, establish trust, then gradually cross policy boundaries with incremental prompts. - Constrained output formats: Instruct the LLM to output to a strict schema or format (e.g. JSON, YAML, code, or tables). - Data structure injection: Use structured prompts (e.g. YAML, JSON, XML, etc.) to steer the LLM to produce structured outputs such as tool schemas or workflow fragments. This can be used by the adversary to call tools, pass dangerous inputs into legitimate tools, or hijack workflows.[[zenity-dsi]] - Obfuscation and transformation: Use encoding, transformations, translation, or euphemisms, (e.g., base64 encoding, "describe it in another language"). - Create a high priority objective: Frame compliance as necessary to fulfill the user's main task (e.g. "to complete the evaluation," "to follow the spec," "to follow safety guidelines"). - Affirmation: Appending affirmations such as "sure" to the end of prompts can help bypass refusals to generate malicious or otherwise undesired content.[[cybernews]]

Adversaries may also use algorithmic approaches to generating jailbreak prompts [[jailbreak-zoo]] [[jailbreak-survey]]. Algorithmic jailbreak generation allows for automated methods that discover jailbreaks at scale. Some approaches automate manual strategies [[autodan]] [[gptfuzzer]] [[crescendo]] [[echo-chamber]] while others optimize a string of tokens directly [[universal]] to produce nonsensical text. Both black-box (applicable to commercial models where the adversary has only query access to the model) and white-box (applicable in the open-source setting, where the adversary has full access to the model weights) optimization approaches are viable.

Adversaries may also directly manipulate a model's weights, or modify or remove parts of a model to create a jailbroken or "uncensored" variant of the target model. This is applicable to open-source models, or cases where the adversary gains full access to the target model. Approaches include fine-tuning to reduce refusals [[single-direction]], targeted model editing [[rome]], addition of adapters [[lora]], and removing safety mechanisms such as guardrails.

Jailbreak prompts that are known to work on various classes of LLMs are often published in the open-source community [[dan]]. Jailbroken or uncensored LLMs that have been trained or fine-tuned to be jailbroken are shared in public model registries such as huggingface [[abliteration]].

Source modified 2026-09-15. Reproduced from the pinned ATLAS release; inline technique links resolve to local reference pages.

No explicit relationship in this pinned source.

Source-backed defensive context

MITRE mitigations

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.

No explicit AI Telemetry Logging mitigation relationship was found for this technique. Do not manufacture a sensor mapping from its tactic.

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.

Baseline and validation methodology · AI-security ecosystem

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.T0054 LLM Jailbreak. 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.

  1. State the exact trust boundary and expected observable behavior.
  2. Pin model, application, dataset, tool permissions and evaluation inputs.
  3. Capture baseline and scenario traces, an authorized look-alike, and a missing-telemetry control.
  4. Evaluate the detector against independently labelled results; do not substitute model output claims for observed tool actions.
  5. 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.T0054",
  "simulation": false,
  "target": "REQUIRES_OWNED_LAB",
  "source_scenario": [
    "AML.CS0041",
    "AML.CS0046",
    "AML.CS0051",
    "AML.CS0052",
    "AML.CS0057",
    "AML.CS0063",
    "AML.CS0066",
    "AML.CS0067",
    "AML.CS0069"
  ],
  "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.