CVE-2020-15265 is a high-severity (CVSS 7.5) Out-of-bounds Read vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. A fix is available for tensorflow — see the affected versions and patch details below.
Segfault in `tf.quantization.quantize_and_dequantize`
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
CVE-2020-15265 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-cpu🐍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 pass an invalid axis value to tf.quantization.quantize_and_dequantize:
tf.quantization.quantize_and_dequantize(
input=[2.5, 2.5], input_min=[0,0], input_max=[1,1], axis=10)
This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation:
const int depth = (axis_ == -1) ? 1 : input.dim_size(axis_);
However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array:
int64 TensorShapeBase<Shape>::dim_size(int d) const {
DCHECK_GE(d, 0);
DCHECK_LT(d, dims());
DoStuffWith(dims_[d]);
}
Since in normal builds, DCHECK-like macros are no-ops, this results in segfault and access out of bounds of the array.
Patches
We have patched the issue in eccb7ec454e6617738554a255d77f08e60ee0808 and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
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 in #42105
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.4.0pip install --upgrade 'tensorflow==2.4.0' |
| 🐍PyPI | tensorflow-cpu | all versions | 2.4.0pip install --upgrade 'tensorflow-cpu==2.4.0' |
| 🐍PyPI | tensorflow-gpu | all versions | 2.4.0pip install --upgrade 'tensorflow-gpu==2.4.0' |
Affected Products
tensorflowgoogleResearch 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, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update tensorflow to 2.4.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2020-15265 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-2020-15265 in your dependencies?
Find it across PyPI, including transitive dependencies.