Sign in
Deutsch English Español Polski
Medium

Out of bounds access in tensorflow-lite

  • Disclosed: 25.09.2020
  • Updated: 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.

Affected software

Click a technology to see all vulnerabilities affecting it.

PlatformPackageVulnerable versionsFixed in
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

References

CVSS score 4.8/10
CVSS vector
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N
Weakness (CWE)
CWE-125, CWE-787
Platform
Python (PyPI)
Package
tensorflow, tensorflow-cpu, tensorflow-gpu
Disclosed
25.09.2020
Updated
11.10.2026 13:19

Sources and licenses

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

Vulnerability history: tensorflow-cpu

All published vulnerabilities of this package (Python (PyPI)), newest first. Shows whether this is a one-off slip or a package that needs watching.

All (430) in the archive
Total entries
430
First → latest
04.2019 07.2024
Without a fix
0
Exploited in attacks
0

Severity breakdown

  • Critical 6
  • High 111
  • Medium 208
  • Low 105

Disclosures per year

Severity:
  1. Out of bounds access in tensorflow-lite this entry
    Medium · 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. TensorFlow has segfault in array_ops.upper_bound
    High · 7.5 CVE-2023-33976 < 2.12.1 2.12.1
  3. TensorFlow vulnerable to Out-of-Bounds Read in DynamicStitch
    High · 7.5 CVE-2023-25659 < 2.11.1 2.11.1
  4. TensorFlow vulnerable to seg fault in `tf.raw_ops.Print`
    High · 7.5 CVE-2023-25660 < 2.11.1 2.11.1
  5. TensorFlow vulnerable to integer overflow in EditDistance
    High · 7.5 CVE-2023-25662 < 2.11.1 2.11.1
  6. TensorFlow has Null Pointer Error in TensorArrayConcatV2
    High · 7.5 CVE-2023-25663 < 2.11.1 2.11.1
  7. TensorFlow has Heap-buffer-overflow in AvgPoolGrad
    High · 7.5 CVE-2023-25664 < 2.11.1 2.11.1
  8. TensorFlow has Null Pointer Error in SparseSparseMaximum
    High · 7.5 CVE-2023-25665 < 2.11.1 2.11.1
  9. TensorFlow has Floating Point Exception in AudioSpectrogram
    High · 7.5 CVE-2023-25666 < 2.11.1 2.11.1
  10. TensorFlow has a heap out-of-buffer read vulnerability in the QuantizeAndDequantize operation
    Critical · 9.8 CVE-2023-25668 < 2.11.1 2.11.1
  11. TensorFlow has Floating Point Exception in AvgPoolGrad with XLA
    High · 7.5 CVE-2023-25669 < 2.11.1 2.11.1
  12. TensorFlow has Null Pointer Error in QuantizedMatMulWithBiasAndDequantize
    High · 7.5 CVE-2023-25670 < 2.11.1 2.11.1
  13. TensorFlow has segmentation fault in tfg-translate
    High · 7.5 CVE-2023-25671 < 2.11.1 2.11.1
  14. TensorFlow has Null Pointer Error in LookupTableImportV2
    High · 7.5 CVE-2023-25672 < 2.11.1 2.11.1
  15. TensorFlow has Floating Point Exception in TensorListSplit with XLA
    High · 7.5 CVE-2023-25673 < 2.11.1 2.11.1
  16. TensorFlow has Null Pointer Error in RandomShuffle with XLA enable
    High · 7.5 CVE-2023-25674 < 2.11.1 2.11.1
  17. TensorFlow has Segfault in Bincount with XLA
    High · 7.5 CVE-2023-25675 < 2.11.1 2.11.1
  18. TensorFlow has null dereference on ParallelConcat with XLA
    High · 7.5 CVE-2023-25676 < 2.11.1 2.11.1
  19. TensorFlow has double free in Fractional(Max/Avg)Pool
    High · 8.0 CVE-2023-25801 < 2.11.1 2.11.1
  20. TensorFlow has Floating Point Exception in TFLite in conv kernel
    High · 7.5 CVE-2023-27579 < 2.11.1 2.11.1
  21. TensorFlow vulnerable to Out-of-Bounds Read in GRUBlockCellGrad
    High · 7.5 CVE-2023-25658 < 2.11.1 2.11.1
  22. Out of bounds write in grappler in Tensorflow
    High · 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
  23. FractionalMaxPool and FractionalAVGPool heap out-of-bounds acess
    High · 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
  24. TensorFlow vulnerable to OOB write in `scatter_nd` in TF Lite
    High · 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
  25. TensorFlow vulnerable to OOB read in `Gather_nd` in TF Lite
    High · 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
  26. Code injection in `saved_model_cli` in TensorFlow
    High · 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
  27. Segfault and OOB write due to incomplete validation in `EditDistance` in TensorFlow
    High · 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
  28. NULL Pointer Dereference and Access of Uninitialized Pointer in TensorFlow
    Critical · 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
  29. Improper Validation of Integrity Check Value in TensorFlow
    High · 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
  30. Out of bounds read in Tensorflow
    High · 8.8 CVE-2022-23594 >= 2.7.0, < 2.7.1 2.7.1
  31. Read and Write outside of bounds in TensorFlow
    High · 8.8 CVE-2022-23560 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  32. Insecure temporary file in Tensorflow
    High · 7.1 CVE-2022-23563 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  33. Out of bounds read in Tensorflow
    High · 8.1 CVE-2022-23592 >= 2.7.0, < 2.8.0 2.8.0
  34. Stack overflow in TensorFlow
    High · 7.5 CVE-2022-23591 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  35. Out of bounds read and write in Tensorflow
    High · 8.8 CVE-2022-23574 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  36. Uninitialized variable access in Tensorflow
    High · 7.6 CVE-2022-23573 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  37. Integer overflow in Tensorflow
    High · 7.6 CVE-2022-23562 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  38. Out of bounds write in TFLite
    High · 8.8 CVE-2022-23561 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  39. Integer overflow in TFLite
    High · 8.8 CVE-2022-23559 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  40. Out of bounds write in Tensorflow
    High · 8.8 CVE-2022-23566 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  41. Use after free in `DecodePng` kernel
    High · 7.6 CVE-2022-23584 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  42. Integer overflow in TFLite array creation
    High · 7.6 CVE-2022-23558 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  43. Integer overflow in TensorFlow
    High · 8.8 CVE-2022-23587 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  44. Heap overflow in Tensorflow
    High · 7.6 CVE-2022-21740 < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 2.5.3, 2.6.3, 2.7.1
  45. Division by zero in TFLite
    High · 6.5 CVE-2022-21741 < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 2.5.3, 2.6.3, 2.7.1
  46. Null pointer dereference in TensorFlow
    High · 6.5 CVE-2022-21739 < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 2.5.3, 2.6.3, 2.7.1
  47. Assertion failure based denial of service in Tensorflow
    High · 6.5 CVE-2022-21737 < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 2.5.3, 2.6.3, 2.7.1
  48. Integer overflow leading to crash in Tensorflow
    High · 6.5 CVE-2022-21738 < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 2.5.3, 2.6.3, 2.7.1
  49. `CHECK`-fails when building invalid tensor shapes in Tensorflow
    High · 6.5 CVE-2022-23569 < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 2.5.3, 2.6.3, 2.7.1
  50. Overflow and uncaught divide by zero in Tensorflow
    High · 6.5 CVE-2022-21729 < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 2.5.3, 2.6.3, 2.7.1
  51. `CHECK`-failures in Tensorflow
    High · 6.5 CVE-2022-21734 < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 2.5.3, 2.6.3, 2.7.1
  52. Division by zero in Tensorflow
    High · 6.5 CVE-2022-21735 < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 2.5.3, 2.6.3, 2.7.1
  53. Undefined behavior in `SparseTensorSliceDataset`
    High · 7.6 CVE-2022-21736 < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 2.5.3, 2.6.3, 2.7.1
  54. Type confusion leading to segfault in Tensorflow
    High · 6.5 CVE-2022-21731 < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 2.5.3, 2.6.3, 2.7.1
  55. Integer overflows in Tensorflow
    High · 6.5 CVE-2022-23567 < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 2.5.3, 2.6.3, 2.7.1
  56. Integer overflows in Tensorflow
    High · 6.5 CVE-2022-23568 < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 2.5.3, 2.6.3, 2.7.1
  57. Out of bounds read in Tensorflow
    High · 8.1 CVE-2022-21726 < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 2.5.3, 2.6.3, 2.7.1
  58. Out of bounds read in Tensorflow
    High · 8.1 CVE-2022-21728 < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 2.5.3, 2.6.3, 2.7.1
  59. Integer overflow in Tensorflow
    High · 7.6 CVE-2022-21727 < 2.5.3, >= 2.6.0, < 2.6.3 2.5.3, 2.6.3
  60. Out of bounds read in Tensorflow
    High · 8.1 CVE-2022-21730 < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 2.5.3, 2.6.3, 2.7.1
  61. Access to invalid memory during shape inference in `Cudnn*` ops
    High · 7.8 CVE-2021-41221 >= 2.6.0, < 2.6.1, >= 2.5.0, < 2.5.2, < 2.4.4 2.6.1, 2.5.2, 2.4.4
Show more (20) Showing 61 of 118

Know which vulnerabilities affect your application

With the DockRay integration and software inventory enabled, DockRay checks reported component versions against known vulnerabilities and notifies you about confirmed matches. Coverage depends on the supported technology and available advisory data.

Connect your application
DockRay newsletter

Application security in your inbox

Important vulnerabilities, actionable guidance and practical advice for your applications. Choose your technologies and follow what matters to your project.

  • A daily digest of new vulnerabilities, critical ones included.
  • Notifications about important updates.
  • Practical advice on application security and monitoring.

Leave empty to receive updates for every technology.

Privacy policy

Free to subscribe. Unsubscribe with one click in any email.