GHSA-wp77-4gmm-7cq8 is a high-severity (CVSS 7.8) NULL Pointer Dereference vulnerability in tensorflow. O3 Security confirms whether GHSA-wp77-4gmm-7cq8 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
Incorrect validation of `SaveV2` inputs
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 code for tf.raw_ops.SaveV2 does not properly validate the inputs and an attacker can trigger a null pointer dereference:
import tensorflow as tf
tf.raw_ops.SaveV2(
prefix=['tensorflow'],
tensor_name=['v'],
shape_and_slices=[],
tensors=[1,2,3])
The implementation uses ValidateInputs to check that the input arguments are valid. This validation would have caught the illegal state represented by the reproducer above.
However, the validation uses OP_REQUIRES which translates to setting the Status object of the current OpKernelContext to an error status, followed by an empty return statement which just terminates the execution of the function it is present in. However, this does not mean that the kernel execution is finalized: instead, execution continues from the next line in Compute that follows the call to ValidateInputs. This is equivalent to lacking the validation.
Patches
We have patched the issue in GitHub commit 9728c60e136912a12d99ca56e106b7cce7af5986.
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 | 2.3.4 |
| 🐍PyPI | tensorflow | ≥ 2.4.0&&< 2.4.3 | 2.4.3 |
| 🐍PyPI | tensorflow | ≥ 2.5.0&&< 2.5.1 | 2.5.1 |
| 🐍PyPI | tensorflow-cpu | all versions | 2.3.4 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.4.0&&< 2.4.3 | 2.4.3 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.5.0&&< 2.5.1 | 2.5.1 |
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 2.3.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-wp77-4gmm-7cq8 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-wp77-4gmm-7cq8 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-wp77-4gmm-7cq8. 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-wp77-4gmm-7cq8 in your dependencies?
O3 detects GHSA-wp77-4gmm-7cq8 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.