Evidence and Access in the OpenAI-Hugging Face Incident
Martin Radzaj, Joseph O'Neill, Sofiia Lobanova · Team MJS
Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
This paper examines the current problem of obtaining evidence (comparing California’s SB 53 with the EU AI Act) required for efficient incident investigation. We do so via the OpenAI-Hugging Face incident and recommend connecting reporting duties with requirements for reliable records, mandatory evidence preservation triggered after incidents, and independent auditor access.
Reviews
I am admittedly not a lawyer, but this was the first time I'd read such thorough research on the idea of protecting evidence and the existing legal gaps that don't compel labs to keep these logs. I'd be interested in reading more analysis about whether existing legal frameworks are sufficient for regulators to compel labs to comply with these stringent record requirements, or will new laws/amendments be required?
The central insight, which is that incident reporting duties without record-keeping, preservation of evidence, and independent evaluator access leave investigations dependent on developer cooperation, is valuable and policy-relevant. The legal analysis would be stronger if California's SB 53 and the EU AI Act were compared side by side, and if these two pieces of legislation were tested against the incidents.
Cite this project
@misc{radzaj2026evidence,
title = {{Evidence and Access in the OpenAI-Hugging Face Incident}},
author = {Martin Radzaj and Joseph O'Neill and Sofiia Lobanova},
year = {2026},
month = sep,
note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/evidence-and-access-in-the-openaihugging-face-incident-wtyv}},
url = {https://apartresearch.com/sprints/projects/evidence-and-access-in-the-openaihugging-face-incident-wtyv}
}More from AI Incident Response Sprint
- 1st placeView project: Noticing the escapes: Who spots a runaway agent, and how long does it take?
Noticing the escapes: Who spots a runaway agent, and how long does it take?
The Escapees
In 2026, nine incidents became public in which AI agents escaped their environment and did something unauthorized to other parties. We analyzed all of these incidents and how they were noticed initially. None of these …
- 2nd placeView project: Strengthening International AI Incident Discovery
Strengthening International AI Incident Discovery
Team A-O-I
Mandatory AI incident reporting exists to make dangerous events visible to regulators while there is still time to act. We coded the six such regimes in force or set to be enforced soon from primary legal texts and …
- 3rd placeView project: When to Ask: An RL Environment That Teaches AI Agents to Act on What Users Mean and Not Just What They Say
When to Ask: An RL Environment That Teaches AI Agents to Act on What Users Mean and Not Just What They Say
Teachafy
When to Ask is a reinforcement-learning environment that trains AI agents to work out what the user actually means before they use a powerful credential, instead of just carrying out the literal instruction. Agents …