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FA-43591 / Borrow checking / Open access

Trait erasure changes the referent lifetime while preserving its size · case 01

Trait erasure changes the referent lifetime while preserving its size.

Verified by executionVariant 1 · 21 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The static analyzer mishandles erasure bound: trait erasure changes the referent lifetime while preserving its size.

THE FAILURE

The static analyzer mishandles erasure bound: trait erasure changes the referent lifetime while preserving its size.

Unsuccessful approach: The partial repair uses if len(d['source_bound'])!=len(d['erased_bound']): errors.append('erasure-bound'), which still violates the stipulated analysis contract.

Case contract

Check erased borrowed trait objects in a stipulated object calculus. Erasure retains referent lifetime and ownership capability; default object bound inside reference inherits reference bound; object method return aliases must be represented in the vtable summary; mutable methods require exclusive object access; upcasting cannot enlarge lifetime or change data-address provenance; thin conversion cannot discard lifetime metadata; associated borrowed output includes owner lifetime; object-safe methods cannot expose a locally quantified lifetime as an unbound output; dynamic destruction observes declared borrowed fields. Input is a fully explicit descriptor of the stated toy IR. Return rule identifiers in declaration order; absent optional obligations use the provided neutral defaults. No rule is an assertion about a production language.

Why this case matters

A finite offline static-analysis model of ownership and borrowing; it does not execute the analyzed program.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(d):
    errors=[]
    if False: errors.append('erasure-bound')
    if d['object_unique'] and not d['source_unique']: errors.append('erasure-capability')
    if d['reference_bound'] is not None and d['default_bound']!=d['reference_bound']: errors.append('reference-default-bound')
    if not set(d['method_aliases'])<=set(d['vtable_aliases']): errors.append('vtable-alias')
    if d['mut_method'] and not d['exclusive_access']: errors.append('mutable-dispatch')
    if not set(d['upcast_after'])<=set(d['upcast_before']): errors.append('upcast-lifetime')
    if d['source_address']!=d['upcast_address']: errors.append('upcast-provenance')
    if d['thin'] and not d['lifetime_metadata']: errors.append('thin-metadata')
    if bool(set(d['local_quantified'])&set(d['output_free'])): errors.append('associated-owner')
    if not set(d['drop_observes'])<=set(d['drop_summary']): errors.append('dynamic-drop-summary')
    return errors
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
base={'source_bound': [], 'erased_bound': [], 'source_unique': False, 'object_unique': False, 'reference_bound': None, 'default_bound': None, 'method_aliases': [], 'vtable_aliases': [], 'mut_method': False, 'exclusive_access': True, 'upcast_before': [], 'upcast_after': [], 'source_address': None, 'upcast_address': None, 'thin': False, 'lifetime_metadata': True, 'assoc_owner': [], 'assoc_output': [], 'local_quantified': [], 'output_free': [], 'drop_observes': [], 'drop_summary': []}
check('well formed empty obligations',solve(base),[])
check('erasure-bound regression 0', solve(dict(base, **({'source_bound':[N],'erased_bound':[N+1]}))), ['erasure-bound'])
check('erasure-bound regression 1', solve(dict(base, **({'source_bound':[N,N+1],'erased_bound':[N+1,N+2]}))), ['erasure-bound'])
check('erasure-capability regression 0', solve(dict(base, **({'object_unique':True}))), ['erasure-capability'])
check('erasure-capability regression 1', solve(dict(base, **({'object_unique':True,'source_bound':[N],'erased_bound':[N]}))), ['erasure-capability'])
check('reference-default-bound regression 0', solve(dict(base, **({'reference_bound':'a','default_bound':'static'}))), ['reference-default-bound'])
check('reference-default-bound regression 1', solve(dict(base, **({'reference_bound':N,'default_bound':N+1}))), ['reference-default-bound'])
check('vtable-alias regression 0', solve(dict(base, **({'method_aliases':['a','b'],'vtable_aliases':['a']}))), ['vtable-alias'])
check('vtable-alias regression 1', solve(dict(base, **({'method_aliases':[N,N+1],'vtable_aliases':[N]}))), ['vtable-alias'])
check('mutable-dispatch regression 0', solve(dict(base, **({'mut_method':True,'exclusive_access':False}))), ['mutable-dispatch'])
check('mutable-dispatch regression 1', solve(dict(base, **({'mut_method':True,'exclusive_access':False,'source_bound':[N],'erased_bound':[N]}))), ['mutable-dispatch'])
check('upcast-lifetime regression 0', solve(dict(base, **({'upcast_before':[N],'upcast_after':[N+1]}))), ['upcast-lifetime'])
check('upcast-lifetime regression 1', solve(dict(base, **({'upcast_before':[N,N+1],'upcast_after':[N+1,N+2]}))), ['upcast-lifetime'])
check('upcast-provenance regression 0', solve(dict(base, **({'source_address':N,'upcast_address':N+1}))), ['upcast-provenance'])
check('upcast-provenance regression 1', solve(dict(base, **({'source_address':'owner','upcast_address':'vtable'}))), ['upcast-provenance'])
check('thin-metadata regression 0', solve(dict(base, **({'thin':True,'lifetime_metadata':False}))), ['thin-metadata'])
check('thin-metadata regression 1', solve(dict(base, **({'thin':True,'lifetime_metadata':False,'source_bound':[N],'erased_bound':[N]}))), ['thin-metadata'])
check('associated-owner regression 0', solve(dict(base, **({'local_quantified':['a'],'output_free':['a']}))), ['associated-owner'])
check('associated-owner regression 1', solve(dict(base, **({'local_quantified':[N],'output_free':[N]}))), ['associated-owner'])
check('dynamic-drop-summary regression 0', solve(dict(base, **({'drop_observes':['a','b'],'drop_summary':['a']}))), ['dynamic-drop-summary'])
check('dynamic-drop-summary regression 1', solve(dict(base, **({'drop_observes':[N,N+1],'drop_summary':[N]}))), ['dynamic-drop-summary'])
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 fixtureActualExpectedOutcome
well formed empty obligations[][]Passed
erasure-bound regression 0[]['erasure-bound']Failed
erasure-bound regression 1[]['erasure-bound']Failed
erasure-capability regression 0['erasure-capability']['erasure-capability']Passed
erasure-capability regression 1['erasure-capability']['erasure-capability']Passed
reference-default-bound regression 0['reference-default-bound']['reference-default-bound']Passed
reference-default-bound regression 1['reference-default-bound']['reference-default-bound']Passed
vtable-alias regression 0['vtable-alias']['vtable-alias']Passed
vtable-alias regression 1['vtable-alias']['vtable-alias']Passed
mutable-dispatch regression 0['mutable-dispatch']['mutable-dispatch']Passed
mutable-dispatch regression 1['mutable-dispatch']['mutable-dispatch']Passed
upcast-lifetime regression 0['upcast-lifetime']['upcast-lifetime']Passed
upcast-lifetime regression 1['upcast-lifetime']['upcast-lifetime']Passed
upcast-provenance regression 0['upcast-provenance']['upcast-provenance']Passed
upcast-provenance regression 1['upcast-provenance']['upcast-provenance']Passed
thin-metadata regression 0['thin-metadata']['thin-metadata']Passed
thin-metadata regression 1['thin-metadata']['thin-metadata']Passed
associated-owner regression 0['associated-owner']['associated-owner']Passed
associated-owner regression 1['associated-owner']['associated-owner']Passed
dynamic-drop-summary regression 0['dynamic-drop-summary']['dynamic-drop-summary']Passed
dynamic-drop-summary regression 1['dynamic-drop-summary']['dynamic-drop-summary']Passed

SHA-256 / 75c0ff88648ff5912d19ff4375166d75b723627614a4b06a46dea9c01a018760

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(d):
    errors=[]
    if len(d['source_bound'])!=len(d['erased_bound']): errors.append('erasure-bound')
    if d['object_unique'] and not d['source_unique']: errors.append('erasure-capability')
    if d['reference_bound'] is not None and d['default_bound']!=d['reference_bound']: errors.append('reference-default-bound')
    if not set(d['method_aliases'])<=set(d['vtable_aliases']): errors.append('vtable-alias')
    if d['mut_method'] and not d['exclusive_access']: errors.append('mutable-dispatch')
    if not set(d['upcast_after'])<=set(d['upcast_before']): errors.append('upcast-lifetime')
    if d['source_address']!=d['upcast_address']: errors.append('upcast-provenance')
    if d['thin'] and not d['lifetime_metadata']: errors.append('thin-metadata')
    if bool(set(d['local_quantified'])&set(d['output_free'])): errors.append('associated-owner')
    if not set(d['drop_observes'])<=set(d['drop_summary']): errors.append('dynamic-drop-summary')
    return errors
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
base={'source_bound': [], 'erased_bound': [], 'source_unique': False, 'object_unique': False, 'reference_bound': None, 'default_bound': None, 'method_aliases': [], 'vtable_aliases': [], 'mut_method': False, 'exclusive_access': True, 'upcast_before': [], 'upcast_after': [], 'source_address': None, 'upcast_address': None, 'thin': False, 'lifetime_metadata': True, 'assoc_owner': [], 'assoc_output': [], 'local_quantified': [], 'output_free': [], 'drop_observes': [], 'drop_summary': []}
check('well formed empty obligations',solve(base),[])
check('erasure-bound regression 0', solve(dict(base, **({'source_bound':[N],'erased_bound':[N+1]}))), ['erasure-bound'])
check('erasure-bound regression 1', solve(dict(base, **({'source_bound':[N,N+1],'erased_bound':[N+1,N+2]}))), ['erasure-bound'])
check('erasure-capability regression 0', solve(dict(base, **({'object_unique':True}))), ['erasure-capability'])
check('erasure-capability regression 1', solve(dict(base, **({'object_unique':True,'source_bound':[N],'erased_bound':[N]}))), ['erasure-capability'])
check('reference-default-bound regression 0', solve(dict(base, **({'reference_bound':'a','default_bound':'static'}))), ['reference-default-bound'])
check('reference-default-bound regression 1', solve(dict(base, **({'reference_bound':N,'default_bound':N+1}))), ['reference-default-bound'])
check('vtable-alias regression 0', solve(dict(base, **({'method_aliases':['a','b'],'vtable_aliases':['a']}))), ['vtable-alias'])
check('vtable-alias regression 1', solve(dict(base, **({'method_aliases':[N,N+1],'vtable_aliases':[N]}))), ['vtable-alias'])
check('mutable-dispatch regression 0', solve(dict(base, **({'mut_method':True,'exclusive_access':False}))), ['mutable-dispatch'])
check('mutable-dispatch regression 1', solve(dict(base, **({'mut_method':True,'exclusive_access':False,'source_bound':[N],'erased_bound':[N]}))), ['mutable-dispatch'])
check('upcast-lifetime regression 0', solve(dict(base, **({'upcast_before':[N],'upcast_after':[N+1]}))), ['upcast-lifetime'])
check('upcast-lifetime regression 1', solve(dict(base, **({'upcast_before':[N,N+1],'upcast_after':[N+1,N+2]}))), ['upcast-lifetime'])
check('upcast-provenance regression 0', solve(dict(base, **({'source_address':N,'upcast_address':N+1}))), ['upcast-provenance'])
check('upcast-provenance regression 1', solve(dict(base, **({'source_address':'owner','upcast_address':'vtable'}))), ['upcast-provenance'])
check('thin-metadata regression 0', solve(dict(base, **({'thin':True,'lifetime_metadata':False}))), ['thin-metadata'])
check('thin-metadata regression 1', solve(dict(base, **({'thin':True,'lifetime_metadata':False,'source_bound':[N],'erased_bound':[N]}))), ['thin-metadata'])
check('associated-owner regression 0', solve(dict(base, **({'local_quantified':['a'],'output_free':['a']}))), ['associated-owner'])
check('associated-owner regression 1', solve(dict(base, **({'local_quantified':[N],'output_free':[N]}))), ['associated-owner'])
check('dynamic-drop-summary regression 0', solve(dict(base, **({'drop_observes':['a','b'],'drop_summary':['a']}))), ['dynamic-drop-summary'])
check('dynamic-drop-summary regression 1', solve(dict(base, **({'drop_observes':[N,N+1],'drop_summary':[N]}))), ['dynamic-drop-summary'])
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 fixtureActualExpectedOutcome
well formed empty obligations[][]Passed
erasure-bound regression 0[]['erasure-bound']Failed
erasure-bound regression 1[]['erasure-bound']Failed
erasure-capability regression 0['erasure-capability']['erasure-capability']Passed
erasure-capability regression 1['erasure-capability']['erasure-capability']Passed
reference-default-bound regression 0['reference-default-bound']['reference-default-bound']Passed
reference-default-bound regression 1['reference-default-bound']['reference-default-bound']Passed
vtable-alias regression 0['vtable-alias']['vtable-alias']Passed
vtable-alias regression 1['vtable-alias']['vtable-alias']Passed
mutable-dispatch regression 0['mutable-dispatch']['mutable-dispatch']Passed
mutable-dispatch regression 1['mutable-dispatch']['mutable-dispatch']Passed
upcast-lifetime regression 0['upcast-lifetime']['upcast-lifetime']Passed
upcast-lifetime regression 1['upcast-lifetime']['upcast-lifetime']Passed
upcast-provenance regression 0['upcast-provenance']['upcast-provenance']Passed
upcast-provenance regression 1['upcast-provenance']['upcast-provenance']Passed
thin-metadata regression 0['thin-metadata']['thin-metadata']Passed
thin-metadata regression 1['thin-metadata']['thin-metadata']Passed
associated-owner regression 0['associated-owner']['associated-owner']Passed
associated-owner regression 1['associated-owner']['associated-owner']Passed
dynamic-drop-summary regression 0['dynamic-drop-summary']['dynamic-drop-summary']Passed
dynamic-drop-summary regression 1['dynamic-drop-summary']['dynamic-drop-summary']Passed

SHA-256 / 44b71057b6d26362183409aca30e7f1e079ea9a63bdd2c895d6e9a001a43f8d5

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 21 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

Member access is invitation-based. Sign in with your invited account to inspect the repair.

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Verification & scope

The explicitly stated toy language is the complete scope; this is not a production compiler or a claim about Rust semantics. 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 2026-09-29T14:44:03.961073+00:00.

Case digest / 7ed9e1c28670ebb5e74564a14ec52a5965eb2a4e446c162fcc57df923197e112