Halting Online Nonconsensual Offenses in the Ranks Act

Prohibits the wrongful publication of intimate visual images, including AI-generated digital forgeries, within military settings. Defines responsibilities and exemptions while establishing consent criteria. Emphasizes harm intentions in unlawful distributions. Expands definitions to cover AI-related manipulations and intimate visual depictions.

  • Authority: United States Congress
  • Parent authority: Federal government
  • Jurisdiction: United States
  • Collections: U.S. federal laws
  • Most recent activity: Proposed
  • Most recent activity date: 2025-09-02

Risk subdomain coverage

Good coverage — Comprehensive governance measures or mitigations with clear procedures relating to the risk subdomain are explicitly described.

  • Exposure to toxic content
  • Compromise of privacy
  • False or misleading information
  • Fraud, scams, and targeted manipulation

Minimal coverage — Brief mention but no specific mitigations, recommendations or governance measures relating to the risk subdomain are described.

  • AI possessing dangerous capabilities

All other risk subdomains had no coverage in this document.

Risk Coverage Summary

The document has good coverage of approximately 4 risk subdomains, with strong focus on toxic content (1.2), privacy compromise (2.1), false information via deepfakes (3.1), and fraud/manipulation (4.3). Coverage is concentrated in content harm, privacy protection, and misuse prevention domains, specifically addressing AI-generated intimate imagery used to harm individuals.

Sector coverage

Good coverage — Comprehensive governance measures or mitigations with clear procedures relating to the sector are explicitly described.

  • National Security

All other sectors had no coverage in this document.

Sectors Summary

This Act exclusively governs the National Security sector, as it applies only to persons subject to the Uniform Code of Military Justice (military personnel). It does not extend to civilian sectors or general commercial activities.

Legislative Status

Hard Law

Legislative Status Summary

This is a binding federal statute enacted by the U.S. Congress that amends the Uniform Code of Military Justice with mandatory criminal prohibitions, court-martial enforcement mechanisms, and explicit criminal penalties for violations.

Actors

Proposer

  • Entity Types: AI_Governance_Actor
  • Summary: The document is a bill proposed by the U.S. Congress to amend the Uniform Code of Military Justice, as evidenced by the enactment clause.

Target

  • Entity Types: AI_User, Affected_Stakeholder
  • Summary: The law explicitly applies to 'any person subject to this chapter' referring to the Uniform Code of Military Justice, which governs military personnel. Targets include both perpetrators (as AI users creating/distributing deepfakes) and victims (as affected stakeholders).

Enforcer

  • Entity Types: AI_Governance_Actor
  • Summary: The document establishes that violations shall be prosecuted and punished through court-martial proceedings, which is the military criminal justice system.

Monitor

  • Entity Types: AI_Governance_Actor
  • Summary: The document references law enforcement agencies in the context of lawful investigative activities, suggesting they play a monitoring role in detecting violations.

AI Lifecycle Stages

Lifecycle stages covered by the documents, as defined in the OECD Framework for the Classification of AI Systems

  • Build and Use Model
  • Deploy
  • Operate and Monitor

AI Lifecycle Summary

The document primarily addresses the Deploy and Operate stages of the AI lifecycle, focusing on the use and distribution of AI-generated digital forgeries (deepfakes) after they have been created. It does not significantly address the planning, data collection, or model development stages, but rather governs the deployment and operational use of AI outputs in military contexts.

Technical Scope

Terms relating to technical scope that are mentioned or implicitly referenced in the document

  • Generative AI

Technical Scope Summary

The document explicitly mentions artificial intelligence, machine learning, and software in the context of creating digital forgeries, but does not reference specific AI model types (frontier, GPAI, foundation models, etc.), compute thresholds, or distinctions between generative/predictive AI. The focus is on AI outputs (synthetic intimate imagery) rather than technical AI system characteristics.

https://www.congress.gov/bill/119th-congress/house-bill/5090
https://agora.eto.tech/instrument/2914
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Document data from AGORA, CSET Emerging Technology Observatory.