GHSA-qg48-85hg-mqc5 is a low-severity (CVSS 2.5) CWE-369 vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-qg48-85hg-mqc5 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
Division by 0 in `DenseCountSparseOutput`
Real-World Exposure
tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpuReal-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
An attacker can cause a denial of service via a FPE runtime error in tf.raw_ops.DenseCountSparseOutput:
import tensorflow as tf
values = tf.constant([], shape=[0, 0], dtype=tf.int64)
weights = tf.constant([])
tf.raw_ops.DenseCountSparseOutput(
values=values, weights=weights,
minlength=-1, maxlength=58, binary_output=True)
This is because the implementation computes a divisor value from user data but does not check that the result is 0 before doing the division:
int num_batch_elements = 1;
for (int i = 0; i < num_batch_dimensions; ++i) {
num_batch_elements *= data.shape().dim_size(i);
}
int num_value_elements = data.shape().num_elements() / num_batch_elements;
Since data is given by the values argument, num_batch_elements is 0.
Patches
We have patched the issue in GitHub commit da5ff2daf618591f64b2b62d9d9803951b945e9f.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, and TensorFlow 2.3.3, as these are also affected.
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 and Ying Wang of Baidu X-Team.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | ≥ 2.3.0&&< 2.3.3 | 2.3.3 |
| 🐍PyPI | tensorflow | ≥ 2.4.0&&< 2.4.2 | 2.4.2 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.3.0&&< 2.3.3 | 2.3.3 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.4.0&&< 2.4.2 | 2.4.2 |
| 🐍PyPI | tensorflow-gpu | ≥ 2.3.0&&< 2.3.3 | 2.3.3 |
| 🐍PyPI | tensorflow-gpu | ≥ 2.4.0&&< 2.4.2 | 2.4.2 |
Research 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. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.
Fix
Update tensorflow to 2.3.3 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-qg48-85hg-mqc5 is resolved across your whole dependency graph.
Workarounds
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 pinpoints whether GHSA-qg48-85hg-mqc5 is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.
Tailored to GHSA-qg48-85hg-mqc5. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-qg48-85hg-mqc5 in your dependencies?
O3 detects GHSA-qg48-85hg-mqc5 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.