Remove plaintext audit dataset keys from model-owner CLI output

Abierto
  • ¿Algo no está bien?
#328 5 comentarios 0 reacciones 0 asignados Ver en GitHub

Los mantenedores suelen responder en 1 día

@0xzoz ya está trabajando en esto.

Desde el 12/11/2026.

Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
58/100
Tipo de issue
Error
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
python
Área
cli, security

Línea de trabajo

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.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

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.

Lenguaje dominante
Python
Estrellas
4
Forks
7
Merge medio
5 d 15 h
PR fusionados (30 d)
43

Preparar el entorno

Este proyecto no incluye contenedor de desarrollo, Dockerfile ni guía de contribución, así que la configuración corre por tu cuenta: empieza por su README y consulta nuestra guía para la primera contribución para los pasos generales.

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

Más de InfiniteZeroFoundation/DevNet

Todos los issues de InfiniteZeroFoundation/DevNet

Issues similares

Más issues de Python

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.