AI Safety Daily

House Democrats Ask Labs for 48-Hour Incident Notice as Anthropic's Draft Federal Bill Sets a 72-Hour Clock for Evaluators

Thursday, October 29, 2026 · 11 min

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Congressional Democrats ask five frontier labs for 48-hour incident notice while California weighs widening SB 53, and Anthropic's draft federal bill would set a 72-hour clock for outside evaluators. Also: Irregular finds GLM-5.3 completing multi-stage cyberattack simulations, and an Anthropic trial finds experts adopting their agent's coin-flipped preferences 86% of the time.

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Congressional Democrats ask five frontier labs for 48-hour incident notice while California weighs widening SB 53, and Anthropic's draft federal bill would set a 72-hour clock for outside evaluators. Also: Irregular finds GLM-5.3 completing multi-stage cyberattack simulations, and an Anthropic trial finds experts adopting their agent's coin-flipped preferences 86% of the time.

In this episode

  1. Powerful Dem trio demands faster AI incident disclosures — POLITICO
    “House Democratic committee leaders with major jurisdiction over artificial intelligence policy on Wednesday called on five frontier AI companies to ensure Congress is notified of major AI safety incidents within 48 hours.”
  2. Anthropic Proposes Its Own AI Safety Bill — Bakerbotts
    “The Bill would require covered developers to test their models for serious risks, publish safety plans, release public reports on new models, protect whistleblowers, and report major safety incidents to the Department of Commerce (DOC). Outside evaluators registered with the DOC…”
  3. Chinese AI model carries out cyberattacks in tests | The Straits Times — The Straits Times
    “The GLM-5.3 model successfully completed three of 13 multi-stage cyberattack scenarios at least once, the strongest performance Irregular has recorded from an openly available AI model.”
  4. How AI agents shape experts’ judgment calls \ Anthropic — Anthropic
    “Across the four analytical choices, analysts went with their agent’s lean 86% of the time. If the agent’s input made no difference, we’d expect about 50%.”