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GHSA-frqp-wp83-qggv BIT-tensorflow-2022-41910 CVE-2022-41910 PYSEC-2026-3178 PYSEC-2026-3323 PYSEC-2026-992

Heap overflow in `QuantizeAndDequantizeV2`

Medium PyPI
Severity
4.8 / 10
Medium · CVSS 3.1
Exploit chance (EPSS)
0.44%
Chance of exploitation in the next 30 days. Higher than 38.14% of scored vulnerabilities.
Known exploited
No
Not in CISA's catalog
Weakness
CWE-125 Out-of-bounds Read
Fix
Available
3 packages have a fixed version

Description

Impact

The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered.

import tensorflow as tf
@tf.function
def test():
    tf.raw_ops.QuantizeAndDequantizeV2(input=[2.5],
    								   input_min=[1.0],
    								   input_max=[10.0],
    								   signed_input=True,
    								   num_bits=1,
    								   range_given=True,
    								   round_mode='HALF_TO_EVEN',
    								   narrow_range=True,
    								   axis=0x7fffffff)
test()

Patches

We have patched the issue in GitHub commit 7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb.

The fix will be included in TensorFlow 2.11.0. We will also cherrypick this commit on TensorFlow 2.10.1.

For more information

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

Affected versions

PyPI

PackageVulnerableFixed in
tensorflow
< 2.8.4
2.8.4
tensorflow
>= 2.9.0, < 2.9.3
2.9.3
tensorflow
>= 2.10.0, < 2.10.1
2.10.1
tensorflow
< 2.8.4
>= 2.9.0, < 2.9.3
>= 2.10.0, < 2.10.1
2.8.4
2.9.3
2.10.1
tensorflow-cpu
< 2.8.4
2.8.4
tensorflow-cpu
>= 2.9.0, < 2.9.3
2.9.3
tensorflow-cpu
>= 2.10.0, < 2.10.1
2.10.1
tensorflow-cpu
< 2.8.4
>= 2.9.0, < 2.9.3
>= 2.10.0, < 2.10.1
2.8.4
2.9.3
2.10.1
tensorflow-gpu
< 2.8.4
2.8.4
tensorflow-gpu
>= 2.9.0, < 2.9.3
2.9.3
tensorflow-gpu
>= 2.10.0, < 2.10.1
2.10.1
tensorflow-gpu
< 2.8.4
>= 2.9.0, < 2.9.3
>= 2.10.0, < 2.10.1
2.8.4
2.9.3
2.10.1

How to fix

Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.

tensorflow PyPI → 2.8.4

pip
pip install --upgrade "tensorflow>=2.8.4"
poetry
poetry add "tensorflow>=2.8.4"
uv
uv add "tensorflow>=2.8.4"

tensorflow-cpu PyPI → 2.8.4

pip
pip install --upgrade "tensorflow-cpu>=2.8.4"
poetry
poetry add "tensorflow-cpu>=2.8.4"
uv
uv add "tensorflow-cpu>=2.8.4"

tensorflow-gpu PyPI → 2.8.4

pip
pip install --upgrade "tensorflow-gpu>=2.8.4"
poetry
poetry add "tensorflow-gpu>=2.8.4"
uv
uv add "tensorflow-gpu>=2.8.4"

Severity breakdown

CVSS 3.1 4.8

CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

Attack vector
Network
Attack complexity
High
Privileges required
Low
User interaction
Required
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High

Weakness

CWE-125: Out-of-bounds Read

The product reads data past the end, or before the beginning, of the intended buffer.

How to prevent it
  • Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright. To reduce the likelihood of introducing an out-of-bounds read, ensure that you validate and ensure correct calculations for any length argument, buffer size calculation, or offset. Be especially careful of relying on a sentinel (i.e. special character such as NUL) in untrusted inputs.
  • Architecture and Design: Use a language that provides appropriate memory abstractions.

References

Advisories

Packages

Other links

Timeline

  1. Published
  2. Last updated by the source
  3. Latest EPSS score
AdvisorySeverityWhy it's related
GHSA-gjh7-xx4r-x345
TensorFlow has segfault in array_ops.upper_bound
High Also affects tensorflow
GHSA-fxgc-95xx-grvq
TensorFlow Denial of Service vulnerability
Medium Also affects tensorflow
GHSA-93vr-9q9m-pj8p
TensorFlow vulnerable to Out-of-Bounds Read in DynamicStitch
High Also affects tensorflow
GHSA-qjqc-vqcf-5qvj
TensorFlow vulnerable to seg fault in `tf.raw_ops.Print`
High Also affects tensorflow
GHSA-7jvm-xxmr-v5cw
TensorFlow vulnerable to integer overflow in EditDistance
High Also affects tensorflow
GHSA-64jg-wjww-7c5w
TensorFlow has Null Pointer Error in TensorArrayConcatV2
High Also affects tensorflow
GHSA-6hg6-5c2q-7rcr
TensorFlow has Heap-buffer-overflow in AvgPoolGrad
High Also affects tensorflow
GHSA-558h-mq8x-7q9g
TensorFlow has Null Pointer Error in SparseSparseMaximum
High Also affects tensorflow
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