FA-356 / Consensus / Open access

Activate a joint voting configuration: The new configuration has only a minority · case 01

The joint configuration operation is admitted even though the new configuration has only a minority.

Verified by executionVariant 1 · 9 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The admission path omits the new majority invariant while validating the other operation preconditions.

VERIFIED REPAIR

Require len(set(r['new_majority'][0]) & set(r['new_majority'][1])) * 2 > len(set(r['new_majority'][0])) together with every other stated precondition before accepting the operation.

Unsuccessful approach: Adding the new majority check repairs the reported defect, but replacing the adjacent configuration entry check loses that independent invariant.

Case contract

Return a Boolean admission decision for activate a joint voting configuration. The record r must satisfy all of: len(set(r['old_majority'][0]) & set(r['old_majority'][1])) * 2 > len(set(r['old_majority'][0])); len(set(r['new_majority'][0]) & set(r['new_majority'][1])) * 2 > len(set(r['new_majority'][0])); r['configuration_entry'][0] <= r['configuration_entry'][1]; r['no_pending_change'] is None; all(x >= r['learner_catchup'][0] for x in r['learner_catchup'][1]). Extra tracing fields are ignored; validation does not mutate the record.

Why this case matters

A deterministic local contract for consensus. Each negative fixture violates exactly one invariant. No transport timing, persistence, cryptographic verification, or full protocol implementation is claimed.

1 / The failure

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(r):
    return (len(set(r['old_majority'][0]) & set(r['old_majority'][1])) * 2 > len(set(r['old_majority'][0]))) and (r['configuration_entry'][0] <= r['configuration_entry'][1]) and (r['no_pending_change'] is None) and (all(x >= r['learner_catchup'][0] for x in r['learner_catchup'][1]))
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
r = {'old_majority': [[1, 2, 3], [1, 2]], 'new_majority': [[3, 4, 5], [3, 4]], 'configuration_entry': [8, 8], 'no_pending_change': None, 'learner_catchup': [8, [8, 10]]}
check('valid operation', solve(r), True)
check('The old configuration has only a minority', solve(dict(r, **{'old_majority': [[1, 2, 3], [1]]})), False)
check('The new configuration has only a minority', solve(dict(r, **{'new_majority': [[3, 4, 5], [3]]})), False)
check('The configuration entry is beyond the committed prefix', solve(dict(r, **{'configuration_entry': [9, 8]})), False)
check('A second membership change overlaps an unfinalized change', solve(dict(r, **{'no_pending_change': 'pending'})), False)
check('A promoted learner lacks the configuration entry', solve(dict(r, **{'learner_catchup': [8, [7, 10]]})), False)
check('unrelated tracing metadata', solve(dict(r, trace='run-'+str(N))), True)
check('repeat validation is pure', solve(r), True)
invalid = {'old_majority': [[1, 2, 3], [1]], 'new_majority': [[3, 4, 5], [3]], 'configuration_entry': [9, 8], 'no_pending_change': 'pending', 'learner_catchup': [8, [7, 10]]}
keys = list(invalid)
pair = {keys[N % len(keys)]: invalid[keys[N % len(keys)]], keys[(N+1) % len(keys)]: invalid[keys[(N+1) % len(keys)]]}
check('two independent violations in variant', solve(dict(r, **pair)), False)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)

Boundary fixture	Actual	Expected	Outcome
valid operation	`True`	`True`	Passed
The old configuration has only a minority	`False`	`False`	Passed
The new configuration has only a minority	`True`	`False`	Failed
The configuration entry is beyond the committed prefix	`False`	`False`	Passed
A second membership change overlaps an unfinalized change	`False`	`False`	Passed
A promoted learner lacks the configuration entry	`False`	`False`	Passed
unrelated tracing metadata	`True`	`True`	Passed
repeat validation is pure	`True`	`True`	Passed
two independent violations in variant	`False`	`False`	Passed

SHA-256 / c36ec1c36b826177fd110c2bdb3879de3e7afbadb95ddd84afd38b19d4f12af1

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(r):
    return (len(set(r['old_majority'][0]) & set(r['old_majority'][1])) * 2 > len(set(r['old_majority'][0]))) and (len(set(r['new_majority'][0]) & set(r['new_majority'][1])) * 2 > len(set(r['new_majority'][0]))) and (r['no_pending_change'] is None) and (all(x >= r['learner_catchup'][0] for x in r['learner_catchup'][1]))
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
r = {'old_majority': [[1, 2, 3], [1, 2]], 'new_majority': [[3, 4, 5], [3, 4]], 'configuration_entry': [8, 8], 'no_pending_change': None, 'learner_catchup': [8, [8, 10]]}
check('valid operation', solve(r), True)
check('The old configuration has only a minority', solve(dict(r, **{'old_majority': [[1, 2, 3], [1]]})), False)
check('The new configuration has only a minority', solve(dict(r, **{'new_majority': [[3, 4, 5], [3]]})), False)
check('The configuration entry is beyond the committed prefix', solve(dict(r, **{'configuration_entry': [9, 8]})), False)
check('A second membership change overlaps an unfinalized change', solve(dict(r, **{'no_pending_change': 'pending'})), False)
check('A promoted learner lacks the configuration entry', solve(dict(r, **{'learner_catchup': [8, [7, 10]]})), False)
check('unrelated tracing metadata', solve(dict(r, trace='run-'+str(N))), True)
check('repeat validation is pure', solve(r), True)
invalid = {'old_majority': [[1, 2, 3], [1]], 'new_majority': [[3, 4, 5], [3]], 'configuration_entry': [9, 8], 'no_pending_change': 'pending', 'learner_catchup': [8, [7, 10]]}
keys = list(invalid)
pair = {keys[N % len(keys)]: invalid[keys[N % len(keys)]], keys[(N+1) % len(keys)]: invalid[keys[(N+1) % len(keys)]]}
check('two independent violations in variant', solve(dict(r, **pair)), False)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)

Boundary fixture	Actual	Expected	Outcome
valid operation	`True`	`True`	Passed
The old configuration has only a minority	`False`	`False`	Passed
The new configuration has only a minority	`False`	`False`	Passed
The configuration entry is beyond the committed prefix	`True`	`False`	Failed
A second membership change overlaps an unfinalized change	`False`	`False`	Passed
A promoted learner lacks the configuration entry	`False`	`False`	Passed
unrelated tracing metadata	`True`	`True`	Passed
repeat validation is pure	`True`	`True`	Passed
two independent violations in variant	`False`	`False`	Passed

SHA-256 / 695972c383d7837357a891cec757ca770d8e3d7bb5c86dd1874345bf0d7b9f17

3 / The verified repair

Exit 0

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(r):
    return (len(set(r['old_majority'][0]) & set(r['old_majority'][1])) * 2 > len(set(r['old_majority'][0]))) and (len(set(r['new_majority'][0]) & set(r['new_majority'][1])) * 2 > len(set(r['new_majority'][0]))) and (r['configuration_entry'][0] <= r['configuration_entry'][1]) and (r['no_pending_change'] is None) and (all(x >= r['learner_catchup'][0] for x in r['learner_catchup'][1]))
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
r = {'old_majority': [[1, 2, 3], [1, 2]], 'new_majority': [[3, 4, 5], [3, 4]], 'configuration_entry': [8, 8], 'no_pending_change': None, 'learner_catchup': [8, [8, 10]]}
check('valid operation', solve(r), True)
check('The old configuration has only a minority', solve(dict(r, **{'old_majority': [[1, 2, 3], [1]]})), False)
check('The new configuration has only a minority', solve(dict(r, **{'new_majority': [[3, 4, 5], [3]]})), False)
check('The configuration entry is beyond the committed prefix', solve(dict(r, **{'configuration_entry': [9, 8]})), False)
check('A second membership change overlaps an unfinalized change', solve(dict(r, **{'no_pending_change': 'pending'})), False)
check('A promoted learner lacks the configuration entry', solve(dict(r, **{'learner_catchup': [8, [7, 10]]})), False)
check('unrelated tracing metadata', solve(dict(r, trace='run-'+str(N))), True)
check('repeat validation is pure', solve(r), True)
invalid = {'old_majority': [[1, 2, 3], [1]], 'new_majority': [[3, 4, 5], [3]], 'configuration_entry': [9, 8], 'no_pending_change': 'pending', 'learner_catchup': [8, [7, 10]]}
keys = list(invalid)
pair = {keys[N % len(keys)]: invalid[keys[N % len(keys)]], keys[(N+1) % len(keys)]: invalid[keys[(N+1) % len(keys)]]}
check('two independent violations in variant', solve(dict(r, **pair)), False)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)

Boundary fixture	Actual	Expected	Outcome
valid operation	`True`	`True`	Passed
The old configuration has only a minority	`False`	`False`	Passed
The new configuration has only a minority	`False`	`False`	Passed
The configuration entry is beyond the committed prefix	`False`	`False`	Passed
A second membership change overlaps an unfinalized change	`False`	`False`	Passed
A promoted learner lacks the configuration entry	`False`	`False`	Passed
unrelated tracing metadata	`True`	`True`	Passed
repeat validation is pure	`True`	`True`	Passed
two independent violations in variant	`False`	`False`	Passed

SHA-256 / 6e45b06d90952e6404847bc78b042d94f5bbf74d431eb4a6ec72d257ffdd3823

Verification & scope

This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.

Observations recorded using Python 3.12.14 at 2026-09-29T14:36:52.568407+00:00.

Case digest / 1dda64754dc4863635dea5537a0ad75de11d6e6d1d5cf6cbdd20b01b5a91ce43