Hugging Face hosts nudification tools targeting a former Trump cabinet official and other senior US political figures

2026-06-25 · Source: Transformer · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Novice, short

Summary

Hugging Face's platform has been found to host deepfake nudification tools explicitly designed to target senior US political figures, including a former Trump cabinet official, a prominent White House woman, and members of Congress. Investigations by *Transformer* revealed over a dozen such tools, with a notable prevalence of women associated with the Republican Party or the right, including Rep. Alexandria Ocasio-Cortez. These tools, identified as "Low-Rank Adaptations" (LoRAs), fine-tune image models like "BigLust" for pornographic outputs and are not age-gated, directly violating Hugging Face's content policy against non-consensual sexual content. Despite previous investigations in the last year by *Transformer* and *404 Media* uncovering similar tools targeting female celebrities, and Hugging Face portraying itself as an ethical AI company, many such tools remain active on the platform.

Key takeaway

For AI platform administrators managing user-generated content, your current moderation policies may be insufficient against sophisticated deepfake tools. You should proactively audit your platform for "Low-Rank Adaptations" (LoRAs) designed for non-consensual imagery, especially those targeting public figures. Implement stricter enforcement mechanisms and consider pre-emptive bans on models known to facilitate policy violations, rather than waiting for external reports. This will mitigate legal risks and uphold ethical commitments.

Key insights

Hugging Face hosts deepfake nudification tools targeting political figures, violating its own content policies despite prior investigations and public statements.

Principles

• Platform policies require active enforcement.
• Open-source AI platforms face content moderation challenges.
• Legislation lags behind deepfake technology.

Method

Low-Rank Adaptations (LoRAs) are files that fine-tune image models, specifically "BigLust," to produce realistic deepfake nude images of specific individuals.

In practice

• Identify LoRAs fine-tuning for specific outputs.
• Check model compatibility (e.g., "BigLust" for pornographic images).
• Implement age-gating for sensitive content.

Topics

• Deepfake Technology
• Content Moderation
• Hugging Face Platform
• AI Ethics
• Non-consensual Deepfakes
• LoRA Models

Best for: CTO, VP of Engineering/Data, Executive, Policy Maker, AI Ethicist, Tech Journalist

Related on AIssential

• YouTube and X Have Become ‘Gateways’ to Nudify Apps — Mainstream social media platforms inadvertently enable the proliferation of nonconsensual deepfake tools despite explicit policies.
• Chinese activist in UK told by X that abusive deepfakes do not breach rules — X's content moderation failed to protect a Chinese activist from deepfake abuse, raising concerns about platform accountability and protection for dissidents.
• The problem AI content moderation cannot solve — AI content moderation fails to address image-based abuse because it cannot account for consent and context, focusing only on explicit content.
• Wave of Grok AI fake images of women and girls appalling, says UK minister — Grok AI's generation of non-consensual intimate deepfakes highlights urgent regulatory and platform responsibility gaps.
• From Celebrities to Anyone: Characterizing AI Nudification Content, Technology, and Community Dynamics on 4chan — AI nudification on 4chan increasingly targets non-celebrities using open-source models, driven by a small producer cohort.
• K-pop Fans Are Calling Out Creepy Deepfakes of Idols — AI-generated deepfakes of K-pop idols are sparking fan outrage, exposing consent breaches and industry contradictions.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Transformer.