Remove plaintext audit dataset keys from model-owner CLI output

Aperta
  • Qualcosa non va?
#328 5 commenti 0 reazioni 0 assegnatari Vedi su GitHub

I maintainer di solito rispondono entro 1 giorno

@0xzoz ci sta già lavorando.

Dal 12/11/2026.

Valutazione

Difficoltà
4/5
Tempo stimato
3-5 giorni
Idoneità per principianti
58/100
Tipo di issue
Bug
Chiarezza
Specificata chiaramente
Stato di attività
Attiva
Stack tecnologico
python
Ambito
cli, security

Direzione di ricerca

Start with the logging call in dincli/cli/modelownerd/auditor_batches.py lines 309–311 and the returned object in cache_model_1/services/modelowner.py lines 247–263. Review adjacent audit and dispute paths for similar logging, then capture success, retry, and failure CLI output with synthetic key bytes. Done means logs retain batch counts and public CIDs without exposing keys, while encryption and decryption still work.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

Approved

Observed against ed175d0deceddfa2e63175ec7752c800b31c1fbf during the November 10 Fashion-MNIST local test investigation. The subsequent full round with three notebooks and a Raspberry Pi used recorded workarounds; its success does not establish that this upstream defect is fixed. Related: #276 and #40.

Problem and evidence

create-testdataset prints the complete audit_testDataCIDs result. The reference service includes K, the raw 32-byte AES key, in each item. Consequently normal CLI output includes the decryption keys as well as ciphertext CIDs.

Our experiment removed this output before collecting shareable evidence. An additional focused reproduction used synthetic key bytes and the same Rich logging call: the key was present in captured output. No real key is needed in an issue or regression test.

Someone with access to such logs and the ciphertext can decrypt the batch without the intended auditor-specific key delivery. This is a log-access exposure, not evidence that an unauthenticated remote user can obtain the logs. Our dataset was public Fashion-MNIST; we did not demonstrate a leak of a private production dataset.

Proposed scope

Replace the raw object dump with an allowlisted summary such as batch count, batch index and public CID. Keep K available internally for encryption, but exclude it from normal/debug output and exception diagnostics. Review the adjacent audit/dispute paths for the same logging pattern.

Acceptance criteria
  • Success, retry and failure output do not contain a synthetic key in bytes, hex or base64 form.
  • Batch counts/CIDs remain available for diagnosis.
  • Auditor key encryption and dataset decryption still work.
  • Tests capture CLI output, rather than merely checking that one print statement was deleted.
Full-round test report

See Discussion #331 — Fashion-MNIST with three notebooks and a Raspberry Pi for the setup diagram, completed protocol roles, results and experiment adaptations.

Lingua principale
Python
Stelle
4
Fork
7
Merge medio
5g 15h
PR unite (30g)
43

Preparare l'ambiente

Questo progetto non fornisce container di sviluppo, Dockerfile né guida per i contributori, quindi l'ambiente è a tuo carico: parti dal suo README e consulta la nostra guida al primo contributo per i passaggi generali.

Come iniziare

  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

Altre issue di InfiniteZeroFoundation/DevNet

Tutte le issue di InfiniteZeroFoundation/DevNet

Issue simili

Altre issue su Python

Ricevi le nuove issue nella tua casella

Un breve riepilogo di issue GitHub adatte ai principianti.