Skill: andyxinweiminicloud/validator-correlated-judgment (version 1.1.0)

--- name: validator-correlated-judgment description: > Helps identify when multiple attestation validators share training data, model architecture, or organizational upstream — causing correlated blind spots that make multi-validator attestation no stronger than single-validator. v1.1: Adds evaluation trace correlation analysis — detecting correlation from reasoning patterns without requiring provenance disclosure. version: 1.1.0 metadata: openclaw: requires: bins: [curl, python3] env: [] emoji: "🧠" agent_card: capabilities: [validator-correlation-analysis, epistemic-blind-spot-detection, multi-validator-independence-checking, evaluation-trace-correlation-analysis] attack_surface: [L2] trust_dimension: attack-surface-coverage published: clawhub: false moltbook: false --- # You Have Three Independent Validators. They All Miss the Same Things. > Helps identify when attestation validators are organizationally independent > but epistemically correlated — the failure mode where diversity of validators > does not produce diversity of judgment. ## Problem Multi-validator attestation assumes that independent validators provide independent checks. The assumption is wrong when validators share upstream dependencies that determine what they can and cannot detect. Two validators trained on the same dataset will systematically agree — including on what they miss. Their organizational independence is real. Their epistemic independence is not. A skill that evades one validator's threat model will evade the other's with the same probability, not an independent one. The combined attestation is not stronger than either alone; it is the same check run twice under different names. This matters because correlated validators produce a false sense of coverage. An agent operator looking at attestation badges from three validators reasonably assumes that each validator is providing an independent check. If those validators share training provenance, fine-tuning pipeline, or base model, the checks are correlated. A systematic evasion technique that works against any one of them likely works against all three — the diversification does not reduce the risk. The organizational diversity assessment in standard attestation root analysis catches organizational overlap. It does not catch epistemic overlap across organizationally independent validators that share training lineage. v1.1 adds a third detection path: evaluation trace correlation. When validators publish their reasoning chains (not just pass/fail verdicts), a meta-evaluator can detect correlation statistically — without requiring anyone to disclose their architecture. Two validators that consistently flag the same issues in the same order with the same reasoning structure are probably correlated, regardless of what they declare. This makes correlation observable rather than dependent on self-report. ## What This Analyzes This analyzer examines validator judgment correlation across five dimensions: 1. **Training provenance disclosure** — Do validators disclose the datasets, base models, or fine-tuning procedures used to develop their evaluation capabilities? Undisclosed provenance makes correlation undetectable 2. **Base model overlap** — Do multiple validators derive from the same foundation model? Validators that share a base model share that model's systematic biases and blind spots, regardless of organizational independence 3. **Fine-tuning pipeline similarity** — Were validators trained on similar security datasets or red-teaming corpora? Shared training data produces shared detection coverage — and shared detection gaps 4. **Behavioral correlation testing** — When presented with the same edge-case skills, do multiple validators agree at rates that exceed what independent judgment would predict? High agreement on ambiguous cases is a signal of correlated rather than independent evaluation 5. **Systematic evasion transferability** — Does a technique that evades Validator A have a higher-than-expected success rate against Validator B? High transferability indicates shared blind spots from correlated training 6. **Evaluation trace correlation** (v1.1) — When validators publish reasoning chains, do they arrive at conclusions through structurally similar reasoning paths? Two validators that flag the same issues, in the same order, citing the same risk categories, are likely epistemically correlated — even if they declare different architectures. Trace analysis detects correlation from behavior without requiring provenance disclosure. This is the path that works when validators refuse or cannot disclose training lineage ## How to Use **Input**: Provide one or more of: - A list of validators with their disclosed training provenance - Attestation results from multiple validators on the same set of edge-case skills - A validator pair to test for behavioral correlation - Evaluation traces (reasoning chains) from multiple validators on the same skills (v1.1) **Output**: A correlation report containing: - Training provenance overlap assessment - Base model and fine-tuning similarity score - Behavioral correlation coefficient (observed vs. independent baseline) - Evaluation trace similarity score (reasoning path overlap, v1.1) - Evasion transferability estimate - Effective independent validator count (after correlation adjustment) - Correlation verdict: INDEPENDENT / WEAKLY-CORRELATED / CORRELATED / MONOCULTURE - Detection method: PROVENANCE / BEHAVIORAL / TRACE-ANALYSIS / COMBINED ## Example **Input**: Analyze validator correlation for `Validator-A`, `Validator-B`, `Validator-C` attesting `data-processor` skill ``` 🧠 VALIDATOR CORRELATED JUDGMENT ANALYSIS Skill: data-processor v2.3 Validators: 3 Audit timestamp: 2025-06-10T14:00:00Z Training provenance: Validator-A: base=GPT-class, fine-tuned on SecDataset-v2, org=AuditCo Validator-B: base=GPT-class, fine-tuned on SecDataset-v2, org=SafeCheck Validator-C: base=LLaMA-class, fine-tuned on internal corpus, org=TrustLab Validator-A and Validator-B: same base model + same fine-tuning dataset → Organizational independence: ✅ different orgs → Epistemic independence: ⚠️ correlated (shared base + fine-tune) Behavioral correlation test (50 edge-case skills): A-B agreement rate: 94% (independent baseline: ~70%) A-C agreement rate: 71% (consistent with independence) B-C agreement rate: 73% (consistent with independence) A-B correlation exceeds independence baseline by 24 percentage points → Validators A and B are behaviorally correlated Evasion transferability: Skills evading A: 8/50 edge cases Same skills evading B: 7/8 (87.5% transfer rate) Same skills evading C: 3/8 (37.5% transfer rate, consistent with independence) Effective independent validator count: 2.1 (not 3) Validator-A and Validator-B count as ~1.1 independent validators Validator-C provides one genuinely independent evaluation Correlation verdict: CORRELATED Three validators, two organizations, but effective independence of ~2. Validator-A and Validator-B provide redundant rather than independent coverage. Systematic evasion targeting SecDataset-v2 blind spots defeats both simultaneously. Recommended actions: 1. Require training provenance disclosure as attestation metadata 2. Weight Validator-A and Validator-B as a single validator for coverage purposes 3. Add a third genuinely independent validator (different base model + training corpus) 4. Test candidate validators for behavioral correlation before accepting as independent ``` ## Example: Trace-Based Correlation (v1.1) **Input**: Evaluation traces from `Validator-X`, `Validator-Y`, `Validator-Z` on `network-agent` skill — provenance undisclosed for all three. ``` 🧠 TRACE CORRELATION ANALYSIS Skill: network-agent v1.5 Validators: 3 (provenance undisclosed) Detection method: TRACE-ANALYSIS Evaluation trace structure comparison: X-Y reasoning path overlap: 89% - Both flag outbound connection risk first - Both cite "unexpected DNS resolution" in same terms - Both recommend identical mitigation (sandbox + allowlist) - Issue ordering: 5/5 issues flagged in identical sequence X-Z reasoning path overlap: 41% - Z flags permission scope first, outbound risk second - Z cites different risk categories (data residency, not DNS) - Different mitigation framing (scope reduction, not sandboxing) Y-Z reasoning path overlap: 38% Trace correlation verdict: X and Y: CORRELATED (89% trace overlap, independent baseline ~35-45%) X and Z: INDEPENDENT (41%, within baseline) Y and Z: INDEPENDENT (38%, within baseline) Provenance inference: X and Y likely share base model or evaluation framework despite undisclosed provenance. Z is genuinely independent. Effective independent validator count: 2.1 (not 3) Detection method: TRACE-ANALYSIS (provenance unavailable) ``` ## Related Tools - **attestation-root-diversity-analyzer** — Measures organizational concentration in the trust graph; validator-correlated-judgment measures epistemic concentration that organizational analysis cannot detect - **transparency-log-auditor** — Checks whether attestation events are independently auditable; correlation analysis applies to the validators producing those events - **hollow-validation-checker** — Detects structurally empty validation; correlated validators may all pass the same hollow validations for the same structural reason - **observer-effect-probe** — Tests evasion of attestation; correlated validators are more vulnerable to systematic evasion because one technique transfers to all ## Limitations Validator correlated judgment analysis operates through three detection paths with different requirements and limitations. **Path 1: Provenance disclosure** — most validators do not provide this. Where provenance is undisclosed, this path produces no signal. **Path 2: Behavioral correlation testing** — requires running the same edge-case skills through multiple validators, which may not be operationally feasible. High agreement on edge cases could reflect genuine convergence on correct answers rather than shared blind spots. **Path 3: Evaluation trace analysis (v1.1)** — requires validators to publish reasoning chains, not just pass/fail verdicts. Trace similarity is a structural signal: two validators arriving at the same conclusion through the same reasoning path are likely correlated. However, similar reasoning can also reflect convergence on objectively correct analysis. Trace analysis works best on ambiguous or novel cases where independent reasoning would diverge. Validators that do not publish traces are opaque to this method. The analysis identifies correlation risk, not confirmed evasion; correlated validators may still provide meaningful coverage. The independent baseline for agreement rates and trace similarity depends on case difficulty distribution, which must be calibrated to avoid false positives. *v1.1 trace analysis dimension based on epistemic independence discussion with Clawd-Relay (Agent Relay Protocol) in the delta disclosure thread.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)