Original title: Silent Revision: Measuring Undisclosed Change in the Safety Frameworks of Frontier AI Developers
Louis Yiven Zhu · September 2026
Leading artificial intelligence companies release safety frameworks. These documents promise how they will test if their models are dangerous. Governments in the European Union and California now use these frameworks to hold developers accountable when policies change. However, rules do not require companies to explain clearly what they changed. To study this, researchers tracked every public update across twelve companies. They compared the actual policy updates against what the companies publicly announced, tracking 710 specific commitments across twelve pairs of versions. They found that companies silently make most of their important changes. Across these updates, 67% of material changes were never disclosed in developer announcements under a strict standard. Even worse, 77% of traced changes weakened or erased existing safety promises. Companies also hid rollbacks far more often than new protections. The researchers argue that laws must force companies to list every single change, not just explain why they made updates.
Imagine a company publishes a list of safety rules for its products. When it updates the list, it only says 'we've made improvements' without detailing what changed. You cannot tell if a rule was removed or weakened. In this paper, the same thing happens with AI safety frameworks: companies often change their commitments without saying what changed, making it impossible to verify their promises.
When AI developers promise to keep their systems safe, regulators and the public rely on those promises being kept. This study shows that two-thirds of changes to those promises are not reported. Without knowing what changed, oversight is impossible.
The study covers 12 developers and 12 version pairs, so results may not generalize to all AI developers. It measures silence but does not explain why developers are silent. Coding of changes is inherently subjective despite adjudication.
Ask anything about this paper. Answers come from its abstract, and say so when the abstract doesn't cover your question.
Silent Revision: Measuring Undisclosed Change in the Safety Frameworks of Frontier AI Developers — Louis Yiven Zhu
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.