CVE-2022-29210
Heap buffer overflow due to incorrect hash function in TensorFlow
TensorFlow is an open source platform for machine learning. In version 2.8.0, the TensorKey hash function used total estimated AllocatedBytes(), which (a) is an estimate per tensor, and (b) is a very poor hash function for constants (e.g. int32_t). It also tried to access individual tensor bytes through tensor.data() of size AllocatedBytes(). This led to ASAN failures because the AllocatedBytes() is an estimate of total bytes allocated by a tensor, including any pointed-to constructs (e.g. strings), and does not refer to contiguous bytes in the .data() buffer. The discoverers could not use this byte vector anyway because types such as tstring include pointers, whereas they needed to hash the string values themselves. This issue is patched in Tensorflow versions 2.9.0 and 2.8.1.
Attack profile
How easy it is to exploit
- Attack vector
- Local
- Complexity
- Low
- Privileges needed
- Low
- User interaction
- None
What an attacker can affect
- Confidentiality
- None
- Integrity
- None
- Availability
- High
Exploit likelihood
What each source reports
CVE List Score used
Heap buffer overflow due to incorrect hash function in TensorFlow
TensorFlow is an open source platform for machine learning. In version 2.8.0, the TensorKey hash function used total estimated AllocatedBytes(), which (a) is an estimate per tensor, and (b) is a very poor hash function for constants (e.g. int32_t). It also tried to access individual tensor bytes through tensor.data() of size AllocatedBytes(). This led to ASAN failures because the AllocatedBytes() is an estimate of total bytes allocated by a tensor, including any pointed-to constructs (e.g. strings), and does not refer to contiguous bytes in the .data() buffer. The discoverers could not use this byte vector anyway because types such as tstring include pointers, whereas they needed to hash the string values themselves. This issue is patched in Tensorflow versions 2.9.0 and 2.8.1.
- Record
CVE-2022-29210- CVSS 3.1
- 5.5 Medium
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H - Weakness
- CWE-120, CWE-122
- Updated
- 2026-11-02
EUVDEUVD-2022-4120
TensorFlow is an open source platform for machine learning. In version 2.8.0, the TensorKey hash function used total estimated AllocatedBytes(), which (a) is an estimate per tensor, and (b) is a very poor hash function for constants (e.g. int32_t). It also tried to access individual tensor bytes through tensor.data() of size AllocatedBytes(). This led to ASAN failures because the AllocatedBytes() is an estimate of total bytes allocated by a tensor, including any pointed-to constructs (e.g. strings), and does not refer to contiguous bytes in the .data() buffer. The discoverers could not use this byte vector anyway because types such as tstring include pointers, whereas they needed to hash the string values themselves. This issue is patched in Tensorflow versions 2.9.0 and 2.8.1.
- Record
EUVD-2022-4120- CVSS 3.1
- 5.5 Medium
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H - Updated
- 2026-11-02
GitHub AdvisoriesGHSA-hc2f-7r5r-r2hg
Heap buffer overflow due to incorrect hash function in TensorFlow
Impact
The TensorKey hash function used total estimated AllocatedBytes(), which (a) is an estimate per tensor, and (b) is a very poor hash function for constants (e.g. int32_t). It also tried to access individual tensor bytes through tensor.data() of size AllocatedBytes(). This led to ASAN failures because the AllocatedBytes() is an estimate of total bytes allocated by a tensor, including any pointed-to constructs (e.g. strings), and does not refer to contiguous bytes in the .data() buffer. We couldn't use this byte vector anyways, since types like tstring include pointers, whereas we need to hash the string values themselves.
Patches
We have patched the issue in GitHub commit 1b85a28d395dc91f4d22b5f9e1e9a22e92ccecd6.
The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, which is the only other affected version.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
- Record
GHSA-hc2f-7r5r-r2hg- CVSS 3.1
- 5.5 Medium
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H - Weakness
- CWE-120, CWE-787
- Updated
- 2026-11-02
NVD
TensorFlow is an open source platform for machine learning. In version 2.8.0, the TensorKey hash function used total estimated AllocatedBytes(), which (a) is an estimate per tensor, and (b) is a very poor hash function for constants (e.g. int32_t). It also tried to access individual tensor bytes through tensor.data() of size AllocatedBytes(). This led to ASAN failures because the AllocatedBytes() is an estimate of total bytes allocated by a tensor, including any pointed-to constructs (e.g. strings), and does not refer to contiguous bytes in the .data() buffer. The discoverers could not use this byte vector anyway because types such as tstring include pointers, whereas they needed to hash the string values themselves. This issue is patched in Tensorflow versions 2.9.0 and 2.8.1.
- Record
CVE-2022-29210- CVSS 3.1
- 5.5 Medium
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H - CVSS 2.0
- 2.1 Low
AV:L/AC:L/Au:N/C:N/I:N/A:P - Weakness
- CWE-120, CWE-122, CWE-787
- Updated
- 2026-11-02
OSV
Heap buffer overflow due to incorrect hash function in TensorFlow
TensorFlow is an open source platform for machine learning. In version 2.8.0, the TensorKey hash function used total estimated AllocatedBytes(), which (a) is an estimate per tensor, and (b) is a very poor hash function for constants (e.g. int32_t). It also tried to access individual tensor bytes through tensor.data() of size AllocatedBytes(). This led to ASAN failures because the AllocatedBytes() is an estimate of total bytes allocated by a tensor, including any pointed-to constructs (e.g. strings), and does not refer to contiguous bytes in the .data() buffer. The discoverers could not use this byte vector anyway because types such as tstring include pointers, whereas they needed to hash the string values themselves. This issue is patched in Tensorflow versions 2.9.0 and 2.8.1.
- Record
CVE-2022-29210- CVSS 3.1
- 5.5 Medium
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H - Weakness
- CWE-120, CWE-122
- Updated
- 2026-11-02
OSVGHSA-hc2f-7r5r-r2hg
Heap buffer overflow due to incorrect hash function in TensorFlow
Impact
The TensorKey hash function used total estimated AllocatedBytes(), which (a) is an estimate per tensor, and (b) is a very poor hash function for constants (e.g. int32_t). It also tried to access individual tensor bytes through tensor.data() of size AllocatedBytes(). This led to ASAN failures because the AllocatedBytes() is an estimate of total bytes allocated by a tensor, including any pointed-to constructs (e.g. strings), and does not refer to contiguous bytes in the .data() buffer. We couldn't use this byte vector anyways, since types like tstring include pointers, whereas we need to hash the string values themselves.
Patches
We have patched the issue in GitHub commit 1b85a28d395dc91f4d22b5f9e1e9a22e92ccecd6.
The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, which is the only other affected version.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
- Record
GHSA-hc2f-7r5r-r2hg- CVSS 3.1
- 5.5 Medium
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H - Weakness
- CWE-120, CWE-787
- Updated
- 2026-11-02
Show 4 moreShow less
OSVPYSEC-2026-1007
Heap buffer overflow due to incorrect hash function in TensorFlow
Impact
The TensorKey hash function used total estimated AllocatedBytes(), which (a) is an estimate per tensor, and (b) is a very poor hash function for constants (e.g. int32_t). It also tried to access individual tensor bytes through tensor.data() of size AllocatedBytes(). This led to ASAN failures because the AllocatedBytes() is an estimate of total bytes allocated by a tensor, including any pointed-to constructs (e.g. strings), and does not refer to contiguous bytes in the .data() buffer. We couldn't use this byte vector anyways, since types like tstring include pointers, whereas we need to hash the string values themselves.
Patches
We have patched the issue in GitHub commit 1b85a28d395dc91f4d22b5f9e1e9a22e92ccecd6.
The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, which is the only other affected version.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
- Record
PYSEC-2026-1007- CVSS 3.1
- 5.5 Medium
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H - Updated
- 2026-11-02
OSVPYSEC-2026-3200
Heap buffer overflow due to incorrect hash function in TensorFlow
Impact
The TensorKey hash function used total estimated AllocatedBytes(), which (a) is an estimate per tensor, and (b) is a very poor hash function for constants (e.g. int32_t). It also tried to access individual tensor bytes through tensor.data() of size AllocatedBytes(). This led to ASAN failures because the AllocatedBytes() is an estimate of total bytes allocated by a tensor, including any pointed-to constructs (e.g. strings), and does not refer to contiguous bytes in the .data() buffer. We couldn't use this byte vector anyways, since types like tstring include pointers, whereas we need to hash the string values themselves.
Patches
We have patched the issue in GitHub commit 1b85a28d395dc91f4d22b5f9e1e9a22e92ccecd6.
The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, which is the only other affected version.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
- Record
PYSEC-2026-3200- CVSS 3.1
- 5.5 Medium
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H - Updated
- 2026-11-02
OSVPYSEC-2026-3339
Heap buffer overflow due to incorrect hash function in TensorFlow
Impact
The TensorKey hash function used total estimated AllocatedBytes(), which (a) is an estimate per tensor, and (b) is a very poor hash function for constants (e.g. int32_t). It also tried to access individual tensor bytes through tensor.data() of size AllocatedBytes(). This led to ASAN failures because the AllocatedBytes() is an estimate of total bytes allocated by a tensor, including any pointed-to constructs (e.g. strings), and does not refer to contiguous bytes in the .data() buffer. We couldn't use this byte vector anyways, since types like tstring include pointers, whereas we need to hash the string values themselves.
Patches
We have patched the issue in GitHub commit 1b85a28d395dc91f4d22b5f9e1e9a22e92ccecd6.
The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, which is the only other affected version.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
- Record
PYSEC-2026-3339- CVSS 3.1
- 5.5 Medium
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H - Updated
- 2026-11-02
OSVBIT-tensorflow-2022-29210 No score
Heap buffer overflow due to incorrect hash function in TensorFlow
TensorFlow is an open source platform for machine learning. In version 2.8.0, the TensorKey hash function used total estimated AllocatedBytes(), which (a) is an estimate per tensor, and (b) is a very poor hash function for constants (e.g. int32_t). It also tried to access individual tensor bytes through tensor.data() of size AllocatedBytes(). This led to ASAN failures because the AllocatedBytes() is an estimate of total bytes allocated by a tensor, including any pointed-to constructs (e.g. strings), and does not refer to contiguous bytes in the .data() buffer. The discoverers could not use this byte vector anyway because types such as tstring include pointers, whereas they needed to hash the string values themselves. This issue is patched in Tensorflow versions 2.9.0 and 2.8.1.
- Record
BIT-tensorflow-2022-29210- Updated
- 2026-11-02
Affected products
Affected packages
| Package | Affected | Fixed |
|---|---|---|
tensorflowPyPI |
2.8.0 up to 2.8.1 | 2.8.1 |
tensorflow-cpuPyPI |
2.8.0 up to 2.8.1 | 2.8.1 |
tensorflow-gpuPyPI |
2.8.0 up to 2.8.1 | 2.8.1 |
tensorflowBitnami |
2.8.0 up to 2.8.1 | 2.8.1 |
History
| Recorded | Changed | Sources |
|---|---|---|
| first indexed | CVE List, EUVD, GitHub Advisories, NVD, OSV |
Dates are when Every CVE recorded each change. Upstream dates may differ.
In short
CVE-2022-29210 is a medium vulnerability (CVSS 5.5) in Google Tensorflow, published May 20, 2022. No source lists public exploit code. EPSS puts the chance of exploitation in the next 30 days at 0.23%, the 13th percentile. Fixed in tensorflow 2.8.1.
References
github.com/tensorflow/tensorflow/releases/tag/v2.8.1
nvd.nist.gov/vuln/detail/CVE-2022-29210