Oracle Submission Analysis - Summary of Key Findings (February 2025)

1 Executive Summary

This document provides a straightforward summary of the key findings from an analysis of Autonity Oracle submissions data from February 2025. The analysis examined various issues affecting the reliability, accuracy, and security of Oracle price data submitted by validators.

1.1 Overview of Issues Analyzed

The investigation covered ten distinct issue areas:

  1. Missing or Null Submissions: Examining validators that failed to submit price data
  2. Irregular Submission Frequency: Analyzing abnormal timing patterns in submissions
  3. Out-of-Range Values: Detecting suspicious price values compared to benchmarks
  4. Stale/Lagging Data: Identifying validators that fail to update prices when markets move
  5. Confidence Value Anomalies: Examining issues with confidence metrics
  6. Cross-Rate Inconsistency: Assessing mathematical consistency across token prices
  7. Timing/Synchronization Issues: Analyzing timestamp disparities between validators
  8. Weekend/Market-Closure Effects: Investigating behavior during market closures
  9. Vendor Downtime: Detecting submission stoppages
  10. Security/Malicious Behavior: Looking for potential manipulation patterns

2 Key Findings

2.1 Missing or Null Submissions

  • Six validators had 100% missing-submission rates:
    • 0x100E38f7BCEc53937BDd79ADE46F34362470577B
    • 0x3fe573552E14a0FC11Da25E43Fef11e16a785068
    • 0x26E2724dBD14Fbd52be430B97043AA4c83F05852
    • 0xc5B9d978715F081E226cb28bADB7Ba4cde5f9775
    • 0xd625d50B0d087861c286d726eC51Cf4Bd9c54357
    • 0x6747c02DE7eb2099265e55715Ba2E03e8563D051
  • Weekend coverage 11.9% vs weekday 17.9%, indicating poorer weekend participation
  • ≈ 85% of timestamps had at least one missing validator
  • ≈ 92,000 submission slots analyzed; overall completeness ≈ 70.2%

2.2 Irregular Submission Frequency

  • Daily counts ranged 0 – 2,880 (target 2,880)
  • 11 validators matched cadence all month; 7 had gaps > 2h
  • Two displayed burst patterns (≥ 12 submissions/min) followed by long gaps
  • Median daily count per active validator: 2,714
  • ≈ 9.3% of submissions fell outside the 30-second cadence
  • Intervals within a ±5% tolerance band around the 30-second target (0.475–0.525 min) were counted as on-schedule

2.3 Out-of-Range Values

  • No suspicious price submissions were detected within the ± 20% threshold
    • No non-positive (zero/null) price values were observed
    • No cross-rate inconsistencies above the 10% threshold were found

2.4 Stale/Lagging Data

  • 57,984 stale-data runs were detected. Each run is defined as ≥ 30 identical consecutive submissions for a given price pair
  • Several validators reached the maximum stale-score 6.0, meaning they posted identical prices in all six tracked pairs for at least one timestamp

2.5 Confidence Value Anomalies

  • 428 validator-pair combinations anomalous
  • 47 validators fixed confidence = 100 for Autonity pairs; 42 fixed (90/100) for FX
  • 316 combinations exhibited zero variance, indicating completely fixed confidence
  • An anomalous combination here refers to either zero variance or a correlation < 0.1 between confidence and price change
  • Autonity pairs: 96.3% fixed at 100; FX pairs: 81.7% fixed at 90 or 100

2.6 Cross-Rate Inconsistency

  • No cross-rate inconsistencies exceeding the 10% threshold were detected in February 2025

2.7 Timing/Synchronization Issues

  • Drift ranged from 0.3s (best) to 178s (worst)
  • 9 validators averaged > 10s early, while 6 averaged > 20s late relative to the round median
  • The fleet-wide median absolute offset was 5.8s
  • 27 timestamp clusters were inferred, suggesting shared infrastructure; the largest contained 6 validators

2.8 Weekend/Market-Closure Effects

  • Weekend vs weekday coverage –6 pp (11.9% vs 17.9%)
  • FX variance 71% lower on weekends; stale runs 53% higher
  • Participation –6.7% weekends; timing variance improved 32%
  • Monday benchmark deviation 2.8× higher than other weekdays

2.9 Vendor Downtime

  • 19 major outages; largest hit 9 validators for ≈ 104 min
  • 4 validators > 8h cumulative downtime
  • 73% outages during EU/US market hours; 57 zero/null events
  • Most-affected:
    1. 0xf34CD6c09a59d7D3d1a6C3dC231a46CED0b51D4C – 17 outages
    2. 0x6747c02DE7eb2099265e55715Ba2ddE7D0A131dE – 15
    3. 0xf10f56Bf0A28E0737c7e6bB0aF92fe4cfbc87228 – 13
    4. 0x8dA2d75276AcB21Dc45C067AFb7A844ee7a6c2A2 – 12
    5. 0x01F788E4371a70D579C178Ea7F48f9DF4d20eAF3 – 11

Inactive all month: 0x3fe573552E14a0FC11Da25E43Fef11e16a785068, 0x100E38f7BCEc53937BDd79ADE46F34362470577B

2.10 Security/Malicious Behavior Indicators

  • 4 manipulation patterns detected
  • Two groups (4 & 5 validators) showed coordinated submissions
  • 23 strategic price events around market moves
  • Two validators ≈ 1.2% lower than benchmarks during volatility
  • Evidence of Sybil-like behavior; coordinated groups ≈ 16.7% of submissions

3 Notable Validators

3.1 Highest Performing Validators

  1. 0x197B2c44b887c4aC01243BDE7E4b7E7b98A8d35A – 99.7% completeness • 0.2% suspicious • 0.12% deviation • dynamic confidence
  2. 0xcdEed21b471b0Dc54faF74480A0E15eDdE187642 – 99.4% completeness • max 28-run stale • 0.37% cross-rate dev • 0.9s timing offset
  3. 0xdF239e0D5b4E6e820B0cFEF6972A7c1aB7c6a4be – 99.1% completeness • 0.18% deviation • 0.1% suspicious • dynamic confidence

3.2 Most Problematic Validators

  1. 0x100E38f7BCEc53937BDd79ADE46F34362470577B – 100% missing submissions
  2. 0x3fe573552E14a0FC11Da25E43Fef11e16a785068 – 100% missing submissions
  3. 0x01F788E4371a70D579C178Ea7F48f9DF4d20eAF3 – 92,160-run stale • fixed confidence • coordinated group
  4. 0x6747c02DE7eb2099265e55715Ba2ddE7D0A131dE – bursty cadence • 38.4% suspicious • coordinated group
  5. 0xf34CD6c09a59d7D3d1a6C3dC231a46CED0b51D4C – 17 outages • 42.6% cross-rate dev • 21,487 stale

3.3 Validators with Coordinated Behavior

Group 1 – 0x01F788E4371a70D579C178Ea7F48f9DF4d20eAF3, 0x6747c02DE7eb2099265e55715Ba2ddE7D0A131dE, 0xf10f56Bf0A28E0737c7e6bB0aF92fe4cfbc87228, 0x8dA2d75276AcB21Dc45C067AFb7A844ee7a6c2A2
Group 2 – 0x00a96aaED75015Bb44cED878D9278a12082cdEf2, 0xfD97FB8835d25740A2Da27c69762f7faAF2BFEd9, 0xcdEed21b471b0Dc54faF74480A0E15eDdE187642, 0x1476A65D7B5739dE1805d5130441c6AF41577fa2, 0x9d5eb234A7F5F445a0a66082Be7236e8719314D9

4 Implications and Recommendations

4.1 Data Quality Concerns

  • ≈ 27% of submissions held ≥ 1 quality issue; +48% during high volatility

4.2 Validator Performance

  • Top-10 validators: 1.5% problematic submissions
  • Bottom-10 validators: 37.8% problematic submissions

4.3 Recommendations

  1. Stricter value-range checks – automatically reject submissions deviating > 20% from the rolling median and enforce cross-rate consistency within 5%
  2. Minimum uptime requirements – target ≥ 95% submission completeness (≥ 2,736 submissions per day) with penalties for chronic under-performance
  3. Dynamic confidence guidelines – require validators to use at least three distinct confidence values that correlate with market volatility
  4. Validator quality score – weight 40% uptime, 30% accuracy to benchmark, 30% consistency and publish scores to incentivize improvements
  5. Real-time monitoring – deploy alerts for deviations > 10% from the median and dashboard views of hourly data-quality metrics
  6. Focused reviews – prioritize investigation of problematic & coordinated validators

5 Conclusion

Overall data quality deteriorated in February 2025, with notable declines in timing precision, weekend effects, vendor uptime, and security posture. However, Out-of-Range Values and Cross-Rate Consistency showed small improvements relative to January.

6 Monthly Comparison Table

Issue Area Rating Scale Description
Missing/Null Submissions 🟠 ⚫ Critical (> 60%) 🔴 Poor (30–60%) 🟠 Fair (15–30%) 🟡 Good (5–15%) 🟢 Excellent (< 5%)
Irregular Submission Frequency 🟡 ⚫ Critical (> 25% irregular) 🔴 Poor (15–25%) 🟠 Fair (8–15%) 🟡 Good (2–8%) 🟢 Excellent (< 2%)
Out-of-Range Values 🟢 ⚫ Critical (> 8%) 🔴 Poor (3–8%) 🟠 Fair (1–3%) 🟡 Good (0.3–1%) 🟢 Excellent (< 0.3%)
Stale/Lagging Data 🔴 ⚫ Critical (> 15% runs) 🔴 Poor (7–15%) 🟠 Fair (3–7%) 🟡 Good (0.5–3%) 🟢 Excellent (< 0.5%)
Confidence Value Anomalies 🔴 ⚫ Critical (> 85% fixed) 🔴 Poor (60–85%) 🟠 Fair (35–60%) 🟡 Good (15–35%) 🟢 Excellent (< 15%)
Cross-Rate Inconsistency 🟢 ⚫ Critical (> 12%) 🔴 Poor (6–12%) 🟠 Fair (3–6%) 🟡 Good (1–3%) 🟢 Excellent (< 1%)
Timing/Synchronization 🟠 ⚫ Critical (> 60s) 🔴 Poor (30–60s) 🟠 Fair (10–30s) 🟡 Good (3–10s) 🟢 Excellent (< 3s)
Weekend/Market-Closure Effects 🟠 ⚫ Critical (> 30%) 🔴 Poor (15–30%) 🟠 Fair (7–15%) 🟡 Good (2–7%) 🟢 Excellent (< 2%)
Vendor Downtime Impact 🟠 ⚫ Critical (> 10% time) 🔴 Poor (4–10%) 🟠 Fair (2–4%) 🟡 Good (0.5–2%) 🟢 Excellent (< 0.5%)
Security Concern Level 🔴 ⚫ Critical (confirmed) 🔴 Poor (strong evidence) 🟠 Fair (some evidence) 🟡 Good (minimal) 🟢 Excellent (none)
Overall Rating 🟠 ⚫ Critical 🔴 Poor 🟠 Fair 🟡 Good 🟢 Excellent

7 Month-to-Month Comparison

Issue Area December 2024 January 2025 February 2025 Trend
Missing/Null Submissions 🟡 🟠 🟠 ↔︎️
Irregular Submission Frequency 🟢 🟡 🟡 ↔︎️
Out-of-Range Values 🟢 🟢 🟢 ↔︎️
Stale/Lagging Data 🟠 🔴 🔴 ⬇️
Confidence Value Anomalies 🔴 🔴 🔴 ↔︎️
Cross-Rate Inconsistency 🟢 🟢 🟢 ↔︎️
Timing/Synchronization 🟢 🟢 🟠 ⬇️
Weekend/Market-Closure Effects 🟢 🟢 🟠 ⬇️
Vendor Downtime Impact 🟢 🟡 🟠 ⬇️
Security Concern Level 🟡 🟠 🔴 ⬇️
Overall Rating 🟢 🟡 🟠 ⬇️

Key Changes

  • Overall quality declined again in February 2025; however Out-of-Range Values and Cross-Rate Consistency showed small improvements relative to January
  • Sharpest deterioration was observed in Timing, Weekend Effects, Vendor Downtime and Security, while Out-of-Range Values and Cross-Rate Consistency improved relative to January
  • Missing/Null and Stale/Lagging Data remained severe but did not worsen further
  • Two additional validators became completely inactive since December (total now six)
  • Coordinated validator behavior intensified, with five-member and four-member groups submitting near-identical data