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GHSA-qhxx-j73r-qpm2 BIT-tensorflow-2020-26266 CVE-2020-26266 PYSEC-2020-254 PYSEC-2020-297 PYSEC-2020-332

Uninitialized memory access in TensorFlow

Medium PyPI
Severity
0.0 / 10
Medium · CVSS 4.0
Exploit chance (EPSS)
0.26%
Chance of exploitation in the next 30 days. Higher than 17.98% of scored vulnerabilities.
Known exploited
No
Not in CISA's catalog
Weakness
CWE-908 Use of Uninitialized Resource
Fix
Available
3 packages have a fixed version

Description

Impact

Under certain cases, a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen:

struct QUInt8 {
  QUInt8() {}
  // ...
  uint8_t value;
};

struct QInt16 {
  QInt16() {}
  // ...
  int16_t value;
};

struct QUInt16 {
  QUInt16() {}
  // ...
  uint16_t value;
};

struct QInt32 {
  QInt32() {}
  // ...
  int32_t value;
};

Patches

We have patched the issue in GitHub commit ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2 and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.

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
< 1.15.5
1.15.5
tensorflow
>= 2.0.0, < 2.0.4
2.0.4
tensorflow
>= 2.1.0, < 2.1.3
2.1.3
tensorflow
>= 2.2.0, < 2.2.2
2.2.2
tensorflow
>= 2.3.0, < 2.3.2
2.3.2
tensorflow
< 1.15.5
>= 2.0.0, < 2.0.4
>= 2.1.0, < 2.1.3
>= 2.2.0, < 2.2.2
>= 2.3.0, < 2.3.2
1.15.5
2.0.4
2.1.3
2.2.2
2.3.2
tensorflow-cpu
< 1.15.5
1.15.5
tensorflow-cpu
>= 2.0.0, < 2.0.4
2.0.4
tensorflow-cpu
>= 2.1.0, < 2.1.3
2.1.3
tensorflow-cpu
>= 2.2.0, < 2.2.2
2.2.2
tensorflow-cpu
>= 2.3.0, < 2.3.2
2.3.2
tensorflow-cpu
< 1.15.5
>= 2.0.0, < 2.0.4
>= 2.1.0, < 2.1.3
>= 2.2.0, < 2.2.2
>= 2.3.0, < 2.3.2
1.15.5
2.0.4
2.1.3
2.2.2
2.3.2
tensorflow-gpu
< 1.15.5
1.15.5
tensorflow-gpu
>= 2.0.0, < 2.0.4
2.0.4
tensorflow-gpu
>= 2.1.0, < 2.1.3
2.1.3
tensorflow-gpu
>= 2.2.0, < 2.2.2
2.2.2
tensorflow-gpu
>= 2.3.0, < 2.3.2
2.3.2
tensorflow-gpu
< 1.15.5
>= 2.0.0, < 2.0.4
>= 2.1.0, < 2.1.3
>= 2.2.0, < 2.2.2
>= 2.3.0, < 2.3.2
1.15.5
2.0.4
2.1.3
2.2.2
2.3.2

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 → 1.15.5

pip
pip install --upgrade "tensorflow>=1.15.5"
poetry
poetry add "tensorflow>=1.15.5"
uv
uv add "tensorflow>=1.15.5"

tensorflow-cpu PyPI → 1.15.5

pip
pip install --upgrade "tensorflow-cpu>=1.15.5"
poetry
poetry add "tensorflow-cpu>=1.15.5"
uv
uv add "tensorflow-cpu>=1.15.5"

tensorflow-gpu PyPI → 1.15.5

pip
pip install --upgrade "tensorflow-gpu>=1.15.5"
poetry
poetry add "tensorflow-gpu>=1.15.5"
uv
uv add "tensorflow-gpu>=1.15.5"

Severity breakdown

CVSS 4.0 0.0

CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N

Attack vector
Local
Attack complexity
Low
Attack requirements
None
Privileges required
Low
User interaction
None
Confidentiality (vulnerable system)
None
Integrity (vulnerable system)
Low
Availability (vulnerable system)
Low
Confidentiality (subsequent systems)
None
Integrity (subsequent systems)
None
Availability (subsequent systems)
None

CVSS 3.1 4.4

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

Attack vector
Local
Attack complexity
Low
Privileges required
Low
User interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
Low
Availability
Low

Weakness

CWE-908: Use of Uninitialized Resource

The product uses or accesses a resource that has not been initialized.

How to prevent it
  • Implementation: Explicitly initialize the resource before use. If this is performed through an API function or standard procedure, follow all required steps.
  • Implementation: Pay close attention to complex conditionals that affect initialization, since some branches might not perform the initialization.
  • Implementation: Avoid race conditions (CWE-362) during initialization routines.
  • Build and Compilation: Run or compile the product with settings that generate warnings about uninitialized variables or data.

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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