Media

Out of bounds access in tensorflow-lite

  • Publicada: 25.09.2020
  • Actualizado: 11.10.2026 13:19

Impact

In TensorFlow Lite, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/kernel_util.cc#L36

However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative -1 value as index for these tensors: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/c/common.h#L82

This results in special casing during validation at model loading time: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/core/subgraph.cc#L566-L580

Unfortunately, this means that the -1 index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays.

This results in both read and write gadgets, albeit very limited in scope.

Patches

We have patched the issue in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83). We will release patch releases for all versions between 1.15 and 2.3.

We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Workarounds

A potential workaround would be to add a custom Verifier to the model loading code to ensure that only operators which accept optional inputs use the -1 special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

Software afectado

Haz clic en una tecnología para ver todas sus vulnerabilidades.

PlataformaPaqueteVersiones vulnerablesCorregida en
Python (PyPI) tensorflow < 1.15.4, >= 2.0.0, < 2.0.3, >= 2.1.0, < 2.1.2, >= 2.2.0, < 2.2.1, >= 2.3.0, < 2.3.1 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1
Python (PyPI) tensorflow-cpu < 1.15.4, >= 2.0.0, < 2.0.3, >= 2.1.0, < 2.1.2, >= 2.2.0, < 2.2.1, >= 2.3.0, < 2.3.1 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1
Python (PyPI) tensorflow-gpu < 1.15.4, >= 2.0.0, < 2.0.3, >= 2.1.0, < 2.1.2, >= 2.2.0, < 2.2.1, >= 2.3.0, < 2.3.1 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1

Referencias

Puntuación CVSS 4.8/10
Vector CVSS
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N
Debilidad (CWE)
CWE-125, CWE-787
Plataforma
Python (PyPI)
Paquete
tensorflow, tensorflow-cpu, tensorflow-gpu
Publicada
25.09.2020
Actualizado
11.10.2026 13:19

Fuentes y licencias

OSV / GitHub Advisory Database - PYSEC-2020-134 - PyPA Advisory Database; CC BY 4.0

OSV / GitHub Advisory Database - PYSEC-2020-291 - PyPA Advisory Database; CC BY 4.0

OSV / GitHub Advisory Database - PYSEC-2020-326 - PyPA Advisory Database; CC BY 4.0

OSV / GitHub Advisory Database - GHSA-cvpc-8phh-8f45 - CC BY 4.0

Historial de vulnerabilidades: tensorflow-gpu

Todas las vulnerabilidades publicadas de este paquete (Python (PyPI)), de la más reciente a la más antigua. Muestra si es un fallo aislado o un paquete que hay que vigilar.

Todas (427) en el archivo
Entradas en total
427
Primera → última
04.2019 07.2024
Sin corrección
0
Explotada en ataques
0

Distribución por gravedad

  • Crítica 6
  • Alta 109
  • Media 207
  • Baja 105

Divulgaciones por año

  1. 2019: 7
  2. 2020: 35
  3. 2021: 200
  4. 2022: 166
  5. 2023: 18
  6. 2024: 1
  7. 2025: 0
  8. 2026: 0
Gravedad:
  1. Out of bounds access in tensorflow-lite esta entrada
    Media · 4.8 CVE-2020-15211 < 1.15.4, >= 2.0.0, < 2.0.3, >= 2.1.0, < 2.1.2, >= 2.2.0, < 2.2.1, >= 2.3.0, < 2.3.1 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1
  2. Alta · 7.5 CVE-2023-33976 < 2.12.1 2.12.1
  3. Alta · 7.5 CVE-2023-25659 < 2.11.1 2.11.1
  4. Alta · 7.5 CVE-2023-25660 < 2.11.1 2.11.1
  5. Alta · 7.5 CVE-2023-25662 < 2.11.1 2.11.1
  6. Alta · 7.5 CVE-2023-25663 < 2.11.1 2.11.1
  7. Alta · 7.5 CVE-2023-25665 < 2.11.1 2.11.1
  8. Alta · 7.5 CVE-2023-25666 < 2.11.1 2.11.1
  9. Alta · 7.5 CVE-2023-25669 < 2.11.1 2.11.1
  10. Alta · 7.5 CVE-2023-25672 < 2.11.1 2.11.1
  11. Alta · 7.5 CVE-2023-25673 < 2.11.1 2.11.1
  12. Alta · 7.5 CVE-2023-25674 < 2.11.1 2.11.1
  13. Alta · 7.5 CVE-2023-25675 < 2.11.1 2.11.1
  14. Alta · 7.5 CVE-2023-25676 < 2.11.1 2.11.1
  15. Alta · 8.0 CVE-2023-25801 < 2.11.1 2.11.1
  16. Alta · 7.5 CVE-2023-27579 < 2.11.1 2.11.1
  17. Alta · 7.5 CVE-2023-25658 < 2.11.1 2.11.1
  18. Alta · 7.1 CVE-2022-41902 < 2.8.4, >= 2.9.0, < 2.9.3, >= 2.10.0, < 2.10.1 2.8.4, 2.9.3, 2.10.1
  19. Alta · 7.1 CVE-2022-41900 < 2.8.4, >= 2.9.0, < 2.9.3, >= 2.10.0, < 2.10.1 2.8.4, 2.9.3, 2.10.1
  20. Alta · 7.0 CVE-2022-35939 < 2.7.2, >= 2.8.0, < 2.8.1, >= 2.9.0, < 2.9.1 2.7.2, 2.8.1, 2.9.1
  21. Alta · 7.0 CVE-2022-35937 < 2.7.2, >= 2.8.0, < 2.8.1, >= 2.9.0, < 2.9.1 2.7.2, 2.8.1, 2.9.1
  22. Alta · 7.8 CVE-2022-29216 < 2.6.4, >= 2.7.0, < 2.7.2, >= 2.8.0, < 2.8.1 2.6.4, 2.7.2, 2.8.1
  23. Alta · 7.1 CVE-2022-29208 < 2.6.4, >= 2.7.0, < 2.7.2, >= 2.8.0, < 2.8.1 2.6.4, 2.7.2, 2.8.1
  24. Crítica · 9.3 ghsa-h6gw-r52c-724r < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 2.5.3, 2.6.3, 2.7.1
  25. Alta · 7.0 ghsa-43q8-3fv7-pr5x < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 2.5.3, 2.6.3, 2.7.1
  26. Alta · 8.8 CVE-2022-23594 >= 2.7.0, < 2.7.1 2.7.1
  27. Alta · 8.8 CVE-2022-23560 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  28. Alta · 7.1 CVE-2022-23563 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  29. Alta · 8.1 CVE-2022-23592 >= 2.7.0, < 2.8.0 2.8.0
  30. Alta · 7.5 CVE-2022-23591 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  31. Alta · 8.8 CVE-2022-23574 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  32. Alta · 7.6 CVE-2022-23573 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  33. Alta · 7.6 CVE-2022-23562 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  34. Alta · 8.8 CVE-2022-23561 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  35. Alta · 8.8 CVE-2022-23559 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  36. Alta · 8.8 CVE-2022-23566 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  37. Alta · 7.6 CVE-2022-23584 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  38. Alta · 7.6 CVE-2022-23558 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  39. Alta · 8.8 CVE-2022-23587 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
Mostrar más (20) Mostrando 41 de 116

Identifica las vulnerabilidades de tu aplicación

Con la integración de DockRay y el inventario de software activado, DockRay compara las versiones notificadas de los componentes con las vulnerabilidades conocidas y avisa de las coincidencias confirmadas. La cobertura depende de la tecnología compatible y de los avisos disponibles.

Conectar tu aplicación
Newsletter DockRay

La seguridad de tus aplicaciones en tu correo

Vulnerabilidades importantes, recomendaciones concretas y consejos prácticos para tus aplicaciones. Elige tus tecnologías y sigue lo que importa para tu proyecto.

  • Un resumen diario de nuevas vulnerabilidades, también las críticas.
  • Avisos sobre actualizaciones importantes.
  • Consejos prácticos sobre seguridad y monitorización de aplicaciones.

Déjalo vacío para recibir avisos de todas las tecnologías.

Suscripción gratuita. Date de baja con un clic en cualquier correo.