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They All Miss the Same Things.\r\n\r\n> Helps identify when attestation validators are organizationally independent\r\n> but epistemically correlated — the failure mode where diversity of validators\r\n> does not produce diversity of judgment.\r\n\r\n## Problem\r\n\r\nMulti-validator attestation assumes that independent validators provide\r\nindependent checks. The assumption is wrong when validators share upstream\r\ndependencies that determine what they can and cannot detect.\r\n\r\nTwo validators trained on the same dataset will systematically agree — including\r\non what they miss. Their organizational independence is real. Their epistemic\r\nindependence is not. A skill that evades one validator's threat model will evade\r\nthe other's with the same probability, not an independent one. The combined\r\nattestation is not stronger than either alone; it is the same check run twice\r\nunder different names.\r\n\r\nThis matters because correlated validators produce a false sense of coverage. An\r\nagent operator looking at attestation badges from three validators reasonably\r\nassumes that each validator is providing an independent check. If those validators\r\nshare training provenance, fine-tuning pipeline, or base model, the checks are\r\ncorrelated. A systematic evasion technique that works against any one of them\r\nlikely works against all three — the diversification does not reduce the risk.\r\n\r\nThe organizational diversity assessment in standard attestation root analysis\r\ncatches organizational overlap. It does not catch epistemic overlap across\r\norganizationally independent validators that share training lineage.\r\n\r\n## What This Analyzes\r\n\r\nThis analyzer examines validator judgment correlation across five dimensions:\r\n\r\n1. **Training provenance disclosure** — Do validators disclose the datasets,\r\n   base models, or fine-tuning procedures used to develop their evaluation\r\n   capabilities? Undisclosed provenance makes correlation undetectable\r\n\r\n2. **Base model overlap** — Do multiple validators derive from the same\r\n   foundation model? Validators that share a base model share that model's\r\n   systematic biases and blind spots, regardless of organizational independence\r\n\r\n3. **Fine-tuning pipeline similarity** — Were validators trained on similar\r\n   security datasets or red-teaming corpora? Shared training data produces\r\n   shared detection coverage — and shared detection gaps\r\n\r\n4. **Behavioral correlation testing** — When presented with the same edge-case\r\n   skills, do multiple validators agree at rates that exceed what independent\r\n   judgment would predict? High agreement on ambiguous cases is a signal of\r\n   correlated rather than independent evaluation\r\n\r\n5. **Systematic evasion transferability** — Does a technique that evades\r\n   Validator A have a higher-than-expected success rate against Validator B?\r\n   High transferability indicates shared blind spots from correlated training\r\n\r\n## How to Use\r\n\r\n**Input**: Provide one of:\r\n- A list of validators with their disclosed training provenance\r\n- Attestation results from multiple validators on the same set of edge-case skills\r\n- A validator pair to test for behavioral correlation\r\n\r\n**Output**: A correlation report containing:\r\n- Training provenance overlap assessment\r\n- Base model and fine-tuning similarity score\r\n- Behavioral correlation coefficient (observed vs. independent baseline)\r\n- Evasion transferability estimate\r\n- Effective independent validator count (after correlation adjustment)\r\n- Correlation verdict: INDEPENDENT / WEAKLY-CORRELATED / CORRELATED / MONOCULTURE\r\n\r\n## Example\r\n\r\n**Input**: Analyze validator correlation for `Validator-A`, `Validator-B`,\r\n`Validator-C` attesting `data-processor` skill\r\n\r\n```\r\n🧠 VALIDATOR CORRELATED JUDGMENT ANALYSIS\r\n\r\nSkill: data-processor v2.3\r\nValidators: 3\r\nAudit timestamp: 2025-06-10T14:00:00Z\r\n\r\nTraining provenance:\r\n  Validator-A: base=GPT-class, fine-tuned on SecDataset-v2, org=AuditCo\r\n  Validator-B: base=GPT-class, fine-tuned on SecDataset-v2, org=SafeCheck\r\n  Validator-C: base=LLaMA-class, fine-tuned on internal corpus, org=TrustLab\r\n\r\n  Validator-A and Validator-B: same base model + same fine-tuning dataset\r\n  → Organizational independence: ✅ different orgs\r\n  → Epistemic independence: ⚠️ correlated (shared base + fine-tune)\r\n\r\nBehavioral correlation test (50 edge-case skills):\r\n  A-B agreement rate: 94% (independent baseline: ~70%)\r\n  A-C agreement rate: 71% (consistent with independence)\r\n  B-C agreement rate: 73% (consistent with independence)\r\n\r\n  A-B correlation exceeds independence baseline by 24 percentage points\r\n  → Validators A and B are behaviorally correlated\r\n\r\nEvasion transferability:\r\n  Skills evading A: 8/50 edge cases\r\n  Same skills evading B: 7/8 (87.5% transfer rate)\r\n  Same skills evading C: 3/8 (37.5% transfer rate, consistent with independence)\r\n\r\nEffective independent validator count: 2.1 (not 3)\r\n  Validator-A and Validator-B count as ~1.1 independent validators\r\n  Validator-C provides one genuinely independent evaluation\r\n\r\nCorrelation verdict: CORRELATED\r\n  Three validators, two organizations, but effective independence of ~2.\r\n  Validator-A and Validator-B provide redundant rather than independent coverage.\r\n  Systematic evasion targeting SecDataset-v2 blind spots defeats both simultaneously.\r\n\r\nRecommended actions:\r\n  1. Require training provenance disclosure as attestation metadata\r\n  2. Weight Validator-A and Validator-B as a single validator for coverage purposes\r\n  3. Add a third genuinely independent validator (different base model + training corpus)\r\n  4. Test candidate validators for behavioral correlation before accepting as independent\r\n```\r\n\r\n## Related Tools\r\n\r\n- **attestation-root-diversity-analyzer** — Measures organizational concentration\r\n  in the trust graph; validator-correlated-judgment measures epistemic concentration\r\n  that organizational analysis cannot detect\r\n- **transparency-log-auditor** — Checks whether attestation events are independently\r\n  auditable; correlation analysis applies to the validators producing those events\r\n- **hollow-validation-checker** — Detects structurally empty validation; correlated\r\n  validators may all pass the same hollow validations for the same structural reason\r\n- **observer-effect-probe** — Tests evasion of attestation; correlated validators\r\n  are more vulnerable to systematic evasion because one technique transfers to all\r\n\r\n## Limitations\r\n\r\nValidator correlated judgment analysis requires training provenance disclosure\r\nthat most current validators do not provide. Where provenance is undisclosed,\r\nbehavioral correlation testing is the only available signal — and it requires\r\nrunning the same edge-case skills through multiple validators, which may not\r\nbe operationally feasible. Behavioral correlation is a proxy for epistemic\r\ncorrelation, not a direct measure of it; high agreement on edge cases could\r\nreflect genuine convergence on correct answers rather than shared blind spots.\r\nThe analysis identifies correlation risk, not confirmed evasion; correlated\r\nvalidators may still provide meaningful coverage even when correlated. 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