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      "statement": "# Hubinger et al.: persistent deceptive backdoors under safety training\n\nConstructed deliberately backdoored language models and tested whether supervised, reinforcement and adversarial safety training removed the triggered behavior, finding persistence in the studied settings.\n\n## Scientific scope\n\nA constructed proof-of-concept with implanted triggers and finite safety-training procedures; not evidence that all deployed models secretly deceive, or that every possible removal technique fails. CoT contents are model outputs, not verified internal intent.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nEvan Hubinger; Carson Denison; Jesse Mu; Mike Lambert; Meg Tong; Monte MacDiarmid; Tamera Lanham; Daniel M. Ziegler; Tim Maxwell; Newton Cheng; Adam Jermyn; Amanda Askell; Ansh Radhakrishnan; Cem Anil; David Duvenaud; Deep Ganguli; Fazl Barez; Jack Clark; Kamal Ndousse; Kshitij Sachan; Michael Sellitto; Mrinank Sharma; Nova DasSarma; Roger Grosse; Shauna Kravec; Yuntao Bai; Zachary Witten; Marina Favaro; Jan Brauner; Holden Karnofsky; Paul Christiano; Samuel R. Bowman; Logan Graham; Jared Kaplan; Sören Mindermann; Ryan Greenblatt; Buck Shlegeris; Nicholas Schiefer; Ethan Perez\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training](https://arxiv.org/abs/2401.05566v1)\n- [Hubinger et al.: persistent deceptive backdoors under safety training — company research account](https://www.anthropic.com/research/sleeper-agents-training-deceptive-llms-that-persist-through-safety-training)\n",
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    "id": "hubinger-et-al-sleeper-agents-2024",
    "entry_id": "hubinger-et-al-sleeper-agents-2024",
    "revision": 1,
    "title": "Hubinger et al.: persistent deceptive backdoors under safety training",
    "context": "Reasoning supervision and alignment experiments",
    "track": "Human-designed research method or resource",
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    "what_changed": "Constructed deliberately backdoored language models and tested whether supervised, reinforcement and adversarial safety training removed the triggered behavior, finding persistence in the studied settings.",
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    "evidence_summary": "The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. A constructed proof-of-concept with implanted triggers and finite safety-training procedures; not evidence that all deployed models secretly deceive, or that every possible removal technique fails. CoT contents are model outputs, not verified internal intent.",
    "references": [
      {
        "title": "Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training",
        "url": "https://arxiv.org/abs/2401.05566v1",
        "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification."
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        "title": "Hubinger et al.: persistent deceptive backdoors under safety training — company research account",
        "url": "https://www.anthropic.com/research/sleeper-agents-training-deceptive-llms-that-persist-through-safety-training",
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