CVE-2022-35965 is a medium-severity (CVSS 5.9) NULL Pointer Dereference vulnerability in tensorflow. A fix is available for tensorflow — see the affected versions and patch details below.
Segfault in `LowerBound` and `UpperBound` in TensorFlow
Exploitation Status
No confirmed exploitation observed yet
- CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.
Exploitation and automatability from CISA’s SSVC triage for CVE-2022-35965.
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
Where this sits among everything scored
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
If LowerBound or UpperBound is given an emptysorted_inputs input, it results in a nullptr dereference, leading to a segfault that can be used to trigger a denial of service attack.
import tensorflow as tf
out_type = tf.int32
sorted_inputs = tf.constant([], shape=[10,0], dtype=tf.float32)
values = tf.constant([], shape=[10,10,0,10,0], dtype=tf.float32)
tf.raw_ops.LowerBound(sorted_inputs=sorted_inputs, values=values, out_type=out_type)
import tensorflow as tf
out_type = tf.int64
sorted_inputs = tf.constant([], shape=[2,2,0,0,0,0,0,2], dtype=tf.float32)
values = tf.constant(0.372660398, shape=[2,4], dtype=tf.float32)
tf.raw_ops.UpperBound(sorted_inputs=sorted_inputs, values=values, out_type=out_type)
Patches
We have patched the issue in GitHub commit bce3717eaef4f769019fd18e990464ca4a2efeea.
The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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 Neophytos Christou, Secure Systems Labs, Brown University.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.7.2pip install --upgrade 'tensorflow==2.7.2' |
| 🐍PyPI | tensorflow | ≥ 2.8.0&&< 2.8.1 | 2.8.1pip install --upgrade 'tensorflow==2.8.1' |
| 🐍PyPI | tensorflow | ≥ 2.9.0&&< 2.9.1 | 2.9.1pip install --upgrade 'tensorflow==2.9.1' |
| 🐍PyPI | tensorflow-cpu | all versions | 2.7.2pip install --upgrade 'tensorflow-cpu==2.7.2' |
| 🐍PyPI | tensorflow-cpu | ≥ 2.8.0&&< 2.8.1 | 2.8.1pip install --upgrade 'tensorflow-cpu==2.8.1' |
| 🐍PyPI | tensorflow-cpu | ≥ 2.9.0&&< 2.9.1 | 2.9.1pip install --upgrade 'tensorflow-cpu==2.9.1' |
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 2.7.2 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2022-35965 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-2022-35965 in your dependencies?
Find it across PyPI, including transitive dependencies.