GHSA-xrqm-fpgr-6hhx BIT-tensorflow-2021-41202 CVE-2021-41202 PYSEC-2021-395 PYSEC-2021-612 PYSEC-2021-810
Overflow/crash in `tf.range`
- Severity
- 0.0 / 10
- Medium · CVSS 4.0
- Exploit chance (EPSS)
- 0.21%
- Chance of exploitation in the next 30 days. Higher than 11.10% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-681 Incorrect Conversion between Numeric Types
- Fix
- Available
- 3 packages have a fixed version
Description
Impact
While calculating the size of the output within the tf.range kernel, there is a conditional statement of type int64 = condition ? int64 : double. Due to C++ implicit conversion rules, both branches of the condition will be cast to double and the result would be truncated before the assignment. This result in overflows:
import tensorflow as tf
tf.sparse.eye(num_rows=9223372036854775807, num_columns=None)
Similarly, tf.range would result in crashes due to overflows if the start or end point are too large.
import tensorflow as tf
tf.range(start=-1e+38, limit=1)
Patches
We have patched the issue in GitHub commits 6d94002a09711d297dbba90390d5482b76113899 (merging #51359) and 1b0e0ec27e7895b9985076eab32445026ae5ca94 (merging #51711).
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
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 externally via GitHub issue, GitHub issue and GitHub issue.
Affected versions
PyPI
| Package | Vulnerable | Fixed in |
|---|---|---|
| tensorflow | >= 2.6.0, < 2.6.1 |
2.6.1 |
| tensorflow | >= 2.5.0, < 2.5.2 |
2.5.2 |
| tensorflow | < 2.4.4 |
2.4.4 |
| tensorflow | < 2.4.4>= 2.5.0, < 2.5.2>= 2.6.0, < 2.6.1>= 2.7.0rc0, < 2.7.0 |
2.4.42.5.22.6.12.7.0 |
| tensorflow-cpu | >= 2.6.0, < 2.6.1 |
2.6.1 |
| tensorflow-cpu | >= 2.5.0, < 2.5.2 |
2.5.2 |
| tensorflow-cpu | < 2.4.4 |
2.4.4 |
| tensorflow-cpu | < 2.4.4>= 2.5.0, < 2.5.2>= 2.6.0, < 2.6.1>= 2.7.0rc0, < 2.7.0 |
2.4.42.5.22.6.12.7.0 |
| tensorflow-gpu | >= 2.6.0, < 2.6.1 |
2.6.1 |
| tensorflow-gpu | >= 2.5.0, < 2.5.2 |
2.5.2 |
| tensorflow-gpu | < 2.4.4 |
2.4.4 |
| tensorflow-gpu | < 2.4.4>= 2.5.0, < 2.5.2>= 2.6.0, < 2.6.1>= 2.7.0rc0, < 2.7.0 |
2.4.42.5.22.6.12.7.0 |
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 → 2.6.1
- pip
pip install --upgrade "tensorflow>=2.6.1"- poetry
poetry add "tensorflow>=2.6.1"- uv
uv add "tensorflow>=2.6.1"
tensorflow-cpu PyPI → 2.6.1
- pip
pip install --upgrade "tensorflow-cpu>=2.6.1"- poetry
poetry add "tensorflow-cpu>=2.6.1"- uv
uv add "tensorflow-cpu>=2.6.1"
tensorflow-gpu PyPI → 2.6.1
- pip
pip install --upgrade "tensorflow-gpu>=2.6.1"- poetry
poetry add "tensorflow-gpu>=2.6.1"- uv
uv add "tensorflow-gpu>=2.6.1"
Severity breakdown
CVSS 4.0 0.0
CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/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)
- None
- Availability (vulnerable system)
- High
- Confidentiality (subsequent systems)
- None
- Integrity (subsequent systems)
- None
- Availability (subsequent systems)
- None
CVSS 3.1 5.5
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
- Attack vector
- Local
- Attack complexity
- Low
- Privileges required
- Low
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- None
- Integrity
- None
- Availability
- High
Weakness
CWE-681: Incorrect Conversion between Numeric Types
When converting from one data type to another, such as long to integer, data can be omitted or translated in a way that produces unexpected values. If the resulting values are used in a sensitive context, then dangerous behaviors may occur.
How to prevent it
- Implementation: Avoid making conversion between numeric types. Always check for the allowed ranges.
References
Advisories
Packages
Other links
- github.com/tensorflow/tensorflow/security/advisories/GHSA-xrqm-fpgr-6hhx
- github.com/tensorflow/tensorflow/issues/46889
- github.com/tensorflow/tensorflow/issues/46912
- github.com/tensorflow/tensorflow/commit/1b0e0ec27e7895b9985076eab32445026ae5ca94
- github.com/tensorflow/tensorflow/commit/6d94002a09711d297dbba90390d5482b76113899
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-612…
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-810…
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-395.yaml
Timeline
- Published
- Last updated by the source
- Latest EPSS score
Related advisories
| Advisory | Severity | Why 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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