Paris Pledge Commits Labs to Kill Switches, as New York Opens Its Frontier AI Registry
Wednesday, November 11, 2026 · 8 min
Sixteen governments and ten AI companies signed the Paris Call, promising fail-safe shutdowns and shared evaluation results without deadlines or penalties, while New York began registering frontier labs under the RAISE Act. Plus, Microsoft's ten-neuron agent backdoor and an Anthropic Fellows paper showing clean data can make poisoned training data stronger.
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Show notes
Sixteen governments and ten AI companies signed the Paris Call, promising fail-safe shutdowns and shared evaluation results without deadlines or penalties, while New York began registering frontier labs under the RAISE Act. Plus, Microsoft's ten-neuron agent backdoor and an Anthropic Fellows paper showing clean data can make poisoned training data stronger.
In this episode
- Paris Call for AI safety and security - Presidentti — Presidentti
“Developers also commit to implement fail-safe mechanisms to shut down model operations within their control, including during training and internal use (e.g. restrictions, pauses or shutdowns)”
- OpenAI, Anthropic face new oversight as New York’s AI safety law takes effect | TechCrunch — Rebecca Bellan
“The RAISE Act, New York’s safety and transparency bill, kicks in at the start of the year. It mandates that large frontier developers — companies with more than $500 million in annual revenue — have to publish safety frameworks on their websites, report critical safety incidents…”
- Detecting sleeper agents hiding in AI model neurons | Microsoft Security Blog — Microsoft Security Blog
“Microsoft Research has released new threat research detailing how a capable agent model could be turned into a sleeper agent by editing fewer than 10 neurons to inject a short payload. The sleeper agent passes all evaluations, but when a trigger string appears in its context,…”
- Diluting Poisoned Fine-Tuning Data Can Strengthen Its Effect — Anthropic
“Consequently, the same training examples can have little effect as part of one training set but create a misalignment backdoor in another. This finding exposes a potential limitation of safety methods that audit or filter training data in isolation.”