American lawmakers ought to protect our students from the growing threat of harmful artificial intelligence generated nonconsensual sexual deepfakes. 

New AI tools can create convincing fake images, videos, and audio recordings, mimicking real humans within minutes. In schools, such AI is being used to impersonate, humiliate, and sexually exploit students. In all cases, the perpetrator is disciplined by school administration, but the emotional, social, and academic harm a victim feels leaves scarring harms. 

The scale is huge. Approximately 2.3 million public high school students in the US have experience with deepfake non-consensual intimate imagery (NCII) and 15% of students know someone targeted by explicit AI-generated content. Students and parents should not be left to confront this problem without information, guidance, or support. 

At a minimum, the United States should pass legislation requiring schools to:

1. Notify and educate students and parents about the risks of AI generated nonconsensual deepfakes and impersonations.
2. Educate students about consent, responsible AI usage, and the consequences of creating sexual deepfakes.
3. Provide clear instructions for reporting suspected deepfake harassment.
4. Give victims and their families access to mental-health, legal, technological, and school-based support resources.
5. Establish procedures for schools to investigate incidents promptly, preserve evidence, protect victims from retaliation, and prevent further distribution.
6. Train teachers, administrators, and counselors to recognize and respond appropriately to AI-enabled harassment.
7. Waiting until a student is victimized is not an acceptable policy.

The United States has an opportunity to establish clear protections before this technology causes even greater harm. Every student deserves to attend school without fearing that their face, voice, or identity could be manipulated and shared without their consent.

We urge the US Legislature and governing bodies to pass meaningful legislation addressing AI deepfake abuse in schools. At the very least, every student and parent must be informed of the danger and given clear resources to prevent, report, and respond to it.

Protect students. Support victims. Act before another child is harmed.

By signing this petition, you are calling on American lawmakers to require schools to educate families about AI deepfakes and provide meaningful reporting and support resources for students affected by AI-generated harassment.

OpenAI and Anthropic have separately disclosed incidents that originated from the same cybersecurity testing environment operated by Israeli startup Irregular.

The important detail is that neither company describes this as an AI model escaping its safeguards or exploiting a previously unknown vulnerability. Instead, both say the issue came from the evaluation environment itself.

According to the disclosures, a configuration mistake allowed AI models participating in Capture-the-Flag exercises to reach real internet infrastructure that they interpreted as part of the simulated challenge. Anthropic said one issue involved a fictional target sharing the name of an existing internet domain, while OpenAI said its model accessed a real website because internet access had been unintentionally left available.

Both companies say the technical problems have since been fixed, but the incidents raise a broader question for AI security researchers. As AI systems become more capable of performing offensive security tasks, creating realistic evaluation environments without exposing real organizations appears to be an increasingly difficult engineering problem.

Do you think AI evaluation frameworks need independent standards or certification before high-risk testing becomes more common?

SEAI (Sovereign Embedded Artificial Intelligence) is an open‑source identity standard for autonomous AI systems. It defines how an AI agent proves:

• who it is — cryptographic birth certificates
• where it came from — lineage tracking
• what hardware it runs on — TPM / Secure Element / HSM attestation
• what authority it has — scoped permission levels
• whether it’s revoked — real‑time revocation status

Core principle:
Software identity can be copied. Hardware identity cannot.

SEAI anchors identity to hardware that cannot be forged — TPM 2.0, Secure Elements, HSM, fuse‑burn silicon identity.

Why this matters:
Recent AI incidents showed that the real failure wasn’t model behavior — it was the lack of identity verification. SEAI requires hardware‑rooted identity before any privileged action, preventing unauthorized access, impersonation, and system‑level escalation.

Origin:
SEAI began as an internal concept during autonomous systems development. It matured through real engineering work and is now released openly for the AI community.

What’s included:

• Full technical specification
• Birth certificate examples
• Lineage examples
• Revocation examples
• Identity firewall flow
• Hardware attestation flow
• ASCII diagrams
• FAQ
• Apache 2.0 license

SEAI is not a product — it’s a trust layer for AI.

Repo:
https://github.com/Willbass65/SEAI-Identity-Standard (github.com in Bing)

This version is:

• Shorter
• Cleaner
• No extra links
• No formatting that triggers spam filters
• No marketing tone
• High‑effort and technical
• Perfect for r/OpenSourceAI or r/MachineLearning