MITRE ATLAS 2026.09 / technique reference

AML.T0102
Generate Malicious Commands

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EnterpriseAI Attack AdaptationSource maturity: Realized

MITRE source definition

Adversaries may use large language models (LLMs) to dynamically generate malicious commands from natural language. Dynamically generated commands may be harder to detect as the attack signature is constantly changing. AI-generated commands may also allow adversaries to more rapidly adapt to different environments and adjust their tactics.

Adversaries may utilize LLMs present in the victim's environment or call out to externally hosted services. APT28 utilized a model hosted on HuggingFace in a campaign with their LAMEHUG malware [[logpoint]]. In either case prompts to generate malicious code can blend in with normal traffic.

Source modified 2026-05-27. 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.

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

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