CVE-2021-37677 is a medium-severity (CVSS 5.5) Improper Input Validation vulnerability in tensorflow. A fix is available for tensorflow — see the affected versions and patch details below.
Missing validation in shape inference for `Dequantize`
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
CVE-2021-37677 by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.
Where this sits among everything scored
Of 385,738 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Counts from FIRST.org, log-scaled.
Real-World Exposure
tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpu+1 moreReal-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.
Description
Impact
The shape inference code for tf.raw_ops.Dequantize has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments:
import tensorflow as tf
tf.compat.v1.disable_v2_behavior()
tf.raw_ops.Dequantize(
input_tensor = tf.constant(-10.0, dtype=tf.float32),
input_tensor = tf.cast(input_tensor, dtype=tf.quint8),
min_range = tf.constant([], shape=[0], dtype=tf.float32),
max_range = tf.constant([], shape=[0], dtype=tf.float32),
mode = 'MIN_COMBINED',
narrow_range=False,
axis=-10,
dtype=tf.dtypes.float32)
The shape inference implementation uses axis to select between two different values for minmax_rank which is then used to retrieve tensor dimensions. However, code assumes that axis can be either -1 or a value greater than -1, with no validation for the other values.
Patches
We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 by Yakun Zhang of Baidu Security.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.3.4pip install --upgrade 'tensorflow==2.3.4' |
| 🐍PyPI | tensorflow | ≥ 2.4.0&&< 2.4.3 | 2.4.3pip install --upgrade 'tensorflow==2.4.3' |
| 🐍PyPI | tensorflow | ≥ 2.5.0&&< 2.5.1 | 2.5.1pip install --upgrade 'tensorflow==2.5.1' |
| 🐍PyPI | tensorflow-cpu | all versions | 2.3.4pip install --upgrade 'tensorflow-cpu==2.3.4' |
| 🐍PyPI | tensorflow-cpu | ≥ 2.4.0&&< 2.4.3 | 2.4.3pip install --upgrade 'tensorflow-cpu==2.4.3' |
| 🐍PyPI | tensorflow-cpu | ≥ 2.5.0&&< 2.5.1 | 2.5.1pip install --upgrade 'tensorflow-cpu==2.5.1' |
Affected Products
tensorflowgoogleDetection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for tensorflow, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update tensorflow to 2.3.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2021-37677 is resolved across your whole dependency graph.
Workarounds
Cap what an attacker can consume: apply request size, rate and timeout limits in front of the affected component, and run it with memory and CPU limits so exhaustion degrades one worker rather than the whole service.
Frequently Asked Questions
Is CVE-2021-37677 in your dependencies?
Find it across PyPI, including transitive dependencies.