Reading Authority Backwards
Quantifying the failure modes of automated control-plane measurement in EVM DeFi
In briefThe deployments a surface-oriented probe fails to resolve are not a random sample: they are enriched for timelocked and governance-gated authority, so reading an unresolved answer as unconstrained control overstates immediately exercisable risk in a measurable, mechanism-predictable direction.
Contents
Control-plane measurement, the automated determination of who can change a deployed contract and how fast, increasingly feeds risk scores, insurance underwriting, and governance dashboards. The accuracy of end-to-end authority resolution has not itself been measured. This work quantifies where automated resolution fails over the top-100 EVM DeFi protocols, and then tests a backward resolution method on a frozen held-out frame it was never developed against.
#Three failure stages
Automated resolution fails at three points in the pipeline.
The detection stage classifies contracts as upgradeable or fixed. On the measured frame it labels 73 deployments fixed-code, of which 12 are in fact upgradeable proxies at storage slots it does not read.
The authorization stage reads an ownership surface. It misses whole mechanism classes, Maker-style wards, OpenZeppelin AccessManaged, and protocol-specific registries among them, and the deployments it misses do not overlap the detection misses, so the two errors compound.
The resolution stage assigns a control tier. Here the central result is about what the resolver leaves silent: the deployments it fails to resolve are substantially more constrained than the ones it tiers, 31 of 47 versus 10 of 42 timelocked or governance-gated (Fisher exact p = 1.0e-4, odds ratio 6.2). A consumer that treats an unresolved answer as immediate or unconstrained therefore overstates immediately exercisable control in a systematic, mechanism-predictable direction. We call this silence bias.
#Backward resolution and the held-out test
The method resolves upgrade authority by reading the privileged function backwards to its terminal actor rather than enumerating a fixed set of ownership surfaces. To test whether it generalizes, we froze the analysis and evaluated it on 121 unseen deployments from a disjoint rank band of the same July 2026 snapshot. The frame, the two resolvers' predictions, and the scoring protocol were fixed and hashed before any ground truth existed. Backward resolution improves authorization-dependency and terminal-authority recovery when it resolves a path, while incomplete structural support limits coverage and holds aggregate tier accuracy below the fixed-surface baseline.
#Artifact bundle
The versioned bundle below reproduces every number in the paper and contains the full held-out evaluation. It includes the resolved per-deployment dataset, the frozen held-out frame, the sealed baseline and guard-directed predictions, the final held-out ground-truth labels, the scoring protocol, and the reconciliation script. It does not include the resolver implementation; the frozen predictions are the resolvers' outputs, not their source.
Main frame (top-100 census):
- RESOLVED.json, the per-deployment dataset
- reconcile.py, recomputes every headline number and checks it against the dataset
- SILENCE_BIAS.json, the silence-bias table and test statistics
- PROXY_AUDIT_FINAL.json, the independent proxy-detection audit
Held-out frame (ranks 101 to 200):
- HOLDOUT_DEPLOYMENTS_CONFIRMED.json, the frozen frame (identity and scope only)
- HOLDOUT_GROUND_TRUTH.final.json, the final ground truth
- sealed/guard_directed_raw.json and sealed/baseline_raw.json, the sealed predictions
- HOLDOUT_SCORING_PROTOCOL.md and R_B_adapter.py, the frozen scoring protocol and baseline tier adapter
- verify_holdout.py, recomputes the headline held-out numbers and checks them against HOLDOUT_COMPARISON_RESULTS.json (per-deployment decisions in HOLDOUT_COMPARISON_PER_DEPLOYMENT.csv)
To reproduce the numbers, run python3 reconcile.py from main-frame (expected 74/74 figures reconcile) and python3 verify_holdout.py from held-out (expected 26/26 held-out figures reconcile). No network access is required.
#Scope and provenance
The held-out ground truth is AI primary adjudication with prediction-blinded human review of a predeclared 37-case subset, not an independent human relabeling of all 121 deployments. The scoring covers upgrade-path authority only. Each released file is versioned, and the frozen artifacts (the held-out frame, the sealed predictions, the ground truth, the scoring protocol, and the R_B adapter) carry the SHA-256 hashes fixed in SHA256SUMS and _seal_meta.json. These artifacts support reproduction of the measurements and the held-out evaluation; they do not change any experiment or reported number.