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GHSA-w58w-79xv-6vcj BIT-tensorflow-2022-41883 CVE-2022-41883 PYSEC-2026-1042 PYSEC-2026-3251 PYSEC-2026-3376

Out of bounds segmentation fault due to unequal op inputs in Tensorflow

Medium PyPI
Severity
6.8 / 10
Medium · CVSS 3.1
Exploit chance (EPSS)
0.38%
Chance of exploitation in the next 30 days. Higher than 32.47% 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

tf.raw_ops.DynamicStitch specifies input sizes when it is registered.

REGISTER_OP("DynamicStitch")
    .Input("indices: N * int32")
    .Input("data: N * T")
    .Output("merged: T")
    .Attr("N : int >= 1")
    .Attr("T : type")
    .SetShapeFn(DynamicStitchShapeFunction);

When it receives a differing number of inputs, such as when it is called with an indices size 1 and a data size 2, it will crash.

import tensorflow as tf

# indices = 1*[tf.random.uniform([1,2], dtype=tf.dtypes.int32, maxval=100)]
indices = [tf.constant([[0, 1]]),]

# data = 2*[tf.random.uniform([1,2], dtype=tf.dtypes.float32, maxval=100)]
data = [tf.constant([[5, 6]]), tf.constant([[7, 8]])]

tf.raw_ops.DynamicStitch(
    indices=indices, 
    data=data)

Patches

We have patched the issue in GitHub commit f5381e0e10b5a61344109c1b7c174c68110f7629.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 as this is also affected.

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 Zizhuang Deng of IIE, UCAS

Affected versions

PyPI

PackageVulnerableFixed in
tensorflow
>= 2.10.0, < 2.10.1
2.10.1
tensorflow-cpu
>= 2.10.0, < 2.10.1
2.10.1
tensorflow-gpu
>= 2.10.0, < 2.10.1
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 → 2.10.1

pip
pip install --upgrade "tensorflow>=2.10.1"
poetry
poetry add "tensorflow>=2.10.1"
uv
uv add "tensorflow>=2.10.1"

tensorflow-cpu PyPI → 2.10.1

pip
pip install --upgrade "tensorflow-cpu>=2.10.1"
poetry
poetry add "tensorflow-cpu>=2.10.1"
uv
uv add "tensorflow-cpu>=2.10.1"

tensorflow-gpu PyPI → 2.10.1

pip
pip install --upgrade "tensorflow-gpu>=2.10.1"
poetry
poetry add "tensorflow-gpu>=2.10.1"
uv
uv add "tensorflow-gpu>=2.10.1"

Severity breakdown

CVSS 3.1 6.8

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

Attack vector
Network
Attack complexity
Low
Privileges required
Low
User interaction
Required
Scope
Unchanged
Confidentiality
Low
Integrity
Low
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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