GHSA-hhvc-g5hv-48c6
MEDIUMWrite to immutable memory region in TensorFlow
Blast Radius
tensorflow🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu+7 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 tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area:
>>> import tensorflow as tf
>>> with open('/tmp/test.txt','w') as f: f.write('a'*128)
>>> tf.raw_ops.ImmutableConst(dtype=tf.string,shape=2,
memory_region_name='/tmp/test.txt')
If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault.
This is because the alocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overriden.
Patches
We have patched the issue in GitHub commit c1e1fc899ad5f8c725dcbb6470069890b5060bc7 and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.
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 | 1.15.5 |
| 🐍PyPI | tensorflow | ≥ 2.0.0&&< 2.0.4 | 2.0.4 |
| 🐍PyPI | tensorflow | ≥ 2.1.0&&< 2.1.3 | 2.1.3 |
| 🐍PyPI | tensorflow | ≥ 2.2.0&&< 2.2.2 | 2.2.2 |
| 🐍PyPI | tensorflow | ≥ 2.3.0&&< 2.3.2 | 2.3.2 |
| 🐍PyPI | tensorflow-cpu | all versions | 1.15.5 |
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 1.15.5 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-hhvc-g5hv-48c6 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-hhvc-g5hv-48c6 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-hhvc-g5hv-48c6. 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-hhvc-g5hv-48c6 in your dependencies?
O3 detects GHSA-hhvc-g5hv-48c6 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.