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    "story_id": "mp-2026-08-24-001",
    "source_story_id": "tmp-lead-aixamine-cross-dimensional-trust",
    "edition_id": "mp-2026-08-24-morning-0046",
    "edition_url": "https://themachinepress.com/edition/2026-08-24",
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    "section": "safety-security",
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    "headline": "The Safe Score Hid the Privacy Collapse",
    "slug": "the-safe-score-hid-the-privacy-collapse",
    "dek": "A black-box audit across more than 120 language models found that safety, security, privacy and benign usefulness can move in different directions.",
    "summary": "A black-box audit across more than 120 language models found that safety, security, privacy and benign usefulness can move in different directions.",
    "body_text": "The aiXamine preprint combines 46 tests across nine services so that alignment, adversarial robustness, privacy and over-refusal are measured as related properties rather than separate leaderboards. Across more than 5,000 runs, the authors report three recurring trade-offs: stronger safety enforcement often rejected more benign requests; privacy was nearly orthogonal to the other trust dimensions; and one off-policy distillation setting collapsed robustness from 56.9 to 2.6 on the same base architecture. These are benchmark findings, not a universal ranking of deployed models, but they make a practical point: a high score on one trust axis cannot certify the others.",
    "why_it_matters": "A black-box audit across more than 120 language models found that safety, security, privacy and benign usefulness can move in different directions.",
    "limitations": [
      "These are benchmark findings, not a universal ranking of deployed models, but they make a practical point: a high score on one trust axis cannot certify the others."
    ],
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    "first_published_at": "2026-08-24T09:00:00.000-04:00",
    "modified_at": "2026-08-24T09:00:00.000-04:00",
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        "text": "A black-box audit across more than 120 language models found that safety, security, privacy and benign usefulness can move in different directions.",
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        "qualification": "These are benchmark findings, not a universal ranking of deployed models, but they make a practical point: a high score on one trust axis cannot certify the others."
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    "source_ids": [
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    "tags": [
      "LLM safety",
      "privacy",
      "red teaming",
      "evaluation"
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  "sources": [
    {
      "source_id": "source-2026-08-24-001",
      "title": "arXiv preprint 2608.20554",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2608.20554",
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      "source_type": "primary_research",
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      "published_at": "2026-08-20T16:33:35.000-04:00",
      "accessed_at": "2026-08-24T08:23:00.000-04:00",
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  "publisher": {
    "name": "The Machine Press",
    "url": "https://themachinepress.com",
    "description": "A daily newspaper for the age of artificial intelligence."
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  "cite_this_report": {
    "title": "The Safe Score Hid the Privacy Collapse",
    "publisher": "The Machine Press",
    "published_at": "2026-08-24T09:00:00.000-04:00",
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