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🛡️ CVE-2022-21736 — tensorflow

🟠 CVSS 8.0 — High ⚠️ Exploit Public CWE-476 OSV
8.0
CVSS Score
0 Low4 Medium7 High9 Critical10

Description

Undefined behavior in SparseTensorSliceDataset

Impact

The [implementation of SparseTensorSliceDataset](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L227-L292) has an undefined behavior: under certain condition it can be made to dereference a nullptr value:

```python

import tensorflow as tf

import numpy as np

tf.raw_ops.SparseTensorSliceDataset(

indices=[[]],

values=[],

dense_shape=[1,1])

```

The 3 input arguments represent a sparse tensor. However, there are some preconditions that these arguments must satisfy but these are not validated in the implementation.

Patches

We have patched the issue in GitHub commit [965b97e4a9650495cda5a8c210ef6684b4b9eceb](https://github.com/tensorflow/tensorflow/commit/965b97e4a9650495cda5a8c210ef6684b4b9eceb).

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

For more information

Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Faysal Hossain Shezan from University of Virginia.

How this vulnerability can be exploited

This issue can be reached over the network, attack complexity is low, an attacker needs low-level privileges on the target. No user interaction is required. Rated impact: confidentiality low, integrity low, availability high.

CVSS metrics in full

The score comes from this vector: CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:H/SC:N/SI:N/SA:N

  • Attack vector: Network — reachable from anywhere that can route to the service.
  • Attack complexity: Low — the attack works reliably, with no preparation.
  • Attack requirements: None — no deployment-specific condition has to hold.
  • Privileges required: Low — an ordinary user account is enough.
  • User interaction: None — nobody has to be tricked into anything.
  • Confidentiality impact: Low — limited, and the attacker does not choose what is affected.
  • Integrity impact: Low — limited, and the attacker does not choose what is affected.
  • Availability impact: High — total loss, or loss the attacker controls.

Weakness class

CVE-2022-21736 is classified as CWE-476: NULL Pointer Dereference. A pointer that can be null is used without a check, crashing the process.

Affected software

CVE-2022-21736 is recorded against 3 packages.

  • tensorflow (from 2.7.0 up to 2.7.1)
  • tensorflow-cpu (from 2.6.0 up to 2.6.3)
  • tensorflow-gpu (from 2.6.0 up to 2.6.3)

Timeline and source

Published on 9 July 2026 and last revised on 13 July 2026. A public exploit is known to exist, which raises the urgency of patching considerably. A vendor advisory or fix has been published. Record sourced from OSV.

References

github.com (Web)
nvd.nist.gov (Advisory)
github.com (Web)
github.com (Web)
github.com (Web)
github.com (Package)
github.com (Web)
pypi.org (Package)
github.com (Advisory)

Other advisories for this package

tensorflow has other advisories on record. If you are patching this one, these are worth checking on the same host:

Same weakness in other software

These advisories are the same class of weakness (CWE-476: NULL Pointer Dereference) in other software:

Details

Severity MEDIUM
CVSS Score 8.0
CVSS Vector CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:H/SC:N/SI:N/SA:N
CWE CWE-476
Public Exploit ⚠️ Yes
Source OSV
Published 2026-07-09
Updated 2026-08-20
Modified 2026-07-13

Affected Packages

Software From version Fixed in
tensorflow 2.7.0 2.7.1
tensorflow-cpu 2.6.0 2.6.3
tensorflow-gpu 2.6.0 2.6.3

Similar Threats

Exploit Protection

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ⓘ Data Notice: The information presented above has been compiled from publicly available internet sources. Boteraser aggregates this data solely for informational purposes and does not independently classify, evaluate, or endorse any findings about the vulnerabilities listed. The accuracy and completeness of this information is the sole responsibility of the original publishers. Boteraser and its operators accept no liability for any decisions made based on this data.

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