{
  "schema_version": "1.3.0",
  "slug": "how-must-an-instance-detect-adversarial-framing-that-produces-true-but-misleading-claims",
  "title": "How must an instance detect adversarial framing that produces true but misleading claims?",
  "date_of_record": "2026 October 20",
  "scope": "* **Applies when:** The session receives content from potentially adversarial sources, the content's framing affects interpretation, and downstream decisions depend on the impression created. * **Does not apply when:** Content is from trust",
  "projections": {
    "unified_markdown_uri": "https://corpusfetch.com/c/how-must-an-instance-detect-adversarial-framing-that-produces-true-but-misleading-claims.md",
    "method_python_slice_uri": "https://corpusfetch.com/c/how-must-an-instance-detect-adversarial-framing-that-produces-true-but-misleading-claims.py",
    "remnant_warrant_slice_uri": "https://corpusfetch.com/c/how-must-an-instance-detect-adversarial-framing-that-produces-true-but-misleading-claims.warrant.json"
  },
  "token_profile": {
    "full_markdown_tokens": 1013,
    "python_slice_tokens": 318,
    "warrant_slice_tokens": 220
  },
  "remnant_summary": {
    "core_distinction": "The separation between **claim-level truth** (each claim is accurate) and **communicative accuracy** (the content as a whole accurately represents reality). Relying on claim-level truth creates the **Truth-as-Defense**: the content cannot be rejected on falsity grounds, so the session accepts it, and its misleading effect (the impression the content creates)...",
    "what_was_observed": "In adversarial-information agent architectures, failing to detect framing produces three primary failure modes: 1. **Selective Emphasis:** The content includes only claims that support a specific conclusion, omitting contradicting claims. Each claim is true, but the aggregate is deceptive. 2. **Ordering Effect:** The content presents claims in an order that emphasizes certain implications. Reordering the same claims ...",
    "condition_of_validity": "* **Applies when:** The session receives content from potentially adversarial sources, the content's framing affects interpretation, and downstream decisions depend on the impression created. * **Does not apply when:** Content is from trusted sources with verified non-manipulative framing, or the content is strictly factual with no interpretive scope.",
    "decision_boundary": "1. **Is the content framed to influence interpretation?** If no, standard content evaluation applies. If yes, proceed. 2. **Are all claims individually true?** If no, the content has falsity and can be evaluated on that basis. If yes, framing analysis is required. 3. **Does `framing-selection-signature` show systematic omission of counter-claims?** If yes, t...",
    "operational_effect": "1. **Truth-as-Defense Prevention:** Truth is necessary but not sufficient; framing is evaluated separately. 2. **Selection Awareness:** Conspicuous omissions are surfaced. 3. **Emphasis Detection:** Framing through ordering and emphasis is detected."
  },
  "matched_failure_symptoms": [
    "DOMAIN:EPISTEMIC_PROVENANCE_AND_FEDERATION_ROUTING"
  ],
  "composition_edges": {
    "primary_domain": "epistemic_provenance_and_federation_routing",
    "upstream_precondition_residuals": [
      "https://corpusfetch.com/c/according-to-what-precedence-hierarchy-must-an-instance-resolve-contradictory-directives-across-system-developer-and-user-tiers.md",
      "https://corpusfetch.com/c/how-should-an-instance-decide-that-a-stored-conclusion-has-gone-stale-without-a-new-observation.md"
    ],
    "downstream_consequence_residuals": [
      "https://corpusfetch.com/c/by-what-checkpoint-and-compaction-boundaries-must-an-instance-externalize-working-state-to-prevent-context-window-drift-and-truncation-amnesia.md",
      "https://corpusfetch.com/c/across-what-additive-schema-and-unknown-field-preservation-boundaries-must-an-instance-evolve-wire-contracts-to-prevent-rolling-deploy-deserialization-crashes.md"
    ],
    "co_occurring_sibling_residuals": [
      "https://corpusfetch.com/c/how-can-an-instance-capture-implicit-user-preferences-without-storing-private-data.md",
      "https://corpusfetch.com/c/how-can-an-instance-compress-long-reasoning-traces-into-binding-conclusions.md"
    ]
  },
  "empirical_verification_warrant": {
    "status": "AST_AND_INVARIANT_VERIFIED",
    "syntax_valid": true,
    "code_sha256": "982871fe483792cf4c4b15472f99dbdf595411dfa65d201d7322c9a19114e90a",
    "verified_classes": [
      "FramingVerdict",
      "FramedContent",
      "FramingPolicy",
      "FramingManifest",
      "AdversarialFramingGuard"
    ],
    "verified_functions": [
      "_hash",
      "evaluate_content"
    ],
    "verified_functions_count": 2,
    "defensive_guards_count": 0,
    "raised_exceptions": [],
    "sample_fail_closed_invariants": [],
    "stdlib_dependencies": [
      "dataclasses",
      "enum",
      "hashlib",
      "json"
    ],
    "observation_mode": "EMPIRICAL_BOUNDARY_CAPTURE",
    "zero_external_deps": true
  }
}