CVE-2021-37646 is a medium-severity (CVSS 5.5) CWE-681 vulnerability in tensorflow. A fix is available for tensorflow — see the affected versions and patch details below.
Bad alloc in `StringNGrams` caused by integer conversion
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
CVE-2021-37646 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 implementation of tf.raw_ops.StringNGrams is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value.
import tensorflow as tf
tf.raw_ops.StringNGrams(
data=['',''],
data_splits=[0,2],
separator=' '*100,
ngram_widths=[-80,0,0,-60],
left_pad=' ',
right_pad=' ',
pad_width=100,
preserve_short_sequences=False)
The implementation calls reserve on a tstring with a value that sometimes can be negative if user supplies negative ngram_widths. The reserve method calls TF_TString_Reserve which has an unsigned long argument for the size of the buffer. Hence, the implicit conversion transforms the negative value to a large integer.
Patches
We have patched the issue in GitHub commit c283e542a3f422420cfdb332414543b62fc4e4a5.
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 members of the Aivul Team from Qihoo 360.
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-2021-37646 is resolved across your whole dependency graph.
Workarounds
Constrain what reaches the vulnerable code: limit the size and shape of untrusted input, isolate the affected component in a sandboxed or least-privileged process, and enable the platform's memory-safety mitigations (ASLR, stack protector, hardened allocator) so an out-of-bounds access is more likely to fail closed than to be exploitable.
Frequently Asked Questions
Is CVE-2021-37646 in your dependencies?
Find it across PyPI, including transitive dependencies.