CVE-2020-15265

Segfault in tf.quantization.quantizeanddequantize

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Attribution

This vulnerability has been reported in #42105

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.

A read operation accesses a memory location beyond the intended buffer boundary. Typical impact: sensitive data disclosure or crash.

CVE-2020-15265 has a CVSS score of 5.9 (High). The vector is network-reachable, no privileges required, and no user interaction. A CVSS score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether this affects your application depends on whether the vulnerable code is present and reachable in your environment. A fixed version is available (2.4.0); upgrading removes the vulnerable code path.

Kodem intelligence

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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.

1. What is CVE-2020-15265? CVE-2020-15265 is a high-severity out-of-bounds read vulnerability in tensorflow (pip), affecting versions < 2.4.0. It is fixed in 2.4.0. A read operation accesses a memory location beyond the intended buffer boundary.
2. How severe is CVE-2020-15265? CVE-2020-15265 has a CVSS score of 5.9 (High). This score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether it represents real risk in your environment depends on whether the vulnerable code is present and reachable.
3. Which packages are affected by CVE-2020-15265?
   • tensorflow (pip) (versions < 2.4.0)
   • tensorflow-cpu (pip) (versions < 2.4.0)
   • tensorflow-gpu (pip) (versions < 2.4.0)
4. Is there a fix for CVE-2020-15265? Yes. CVE-2020-15265 is fixed in 2.4.0. Upgrade to this version or later.
5. Is CVE-2020-15265 exploitable, and should I be worried? Whether CVE-2020-15265 is exploitable in your environment depends on whether the vulnerable code is present and reachable. A CVSS score is a worst-case rating; it does not account for your specific deployment, configuration, or usage patterns. Kodem, an Intelligent Application Security platform, uses runtime intelligence to show which vulnerabilities actually execute in production, so you can focus on the ones that represent real risk. Get a demo
6. What actually determines whether CVE-2020-15265 is exploitable, and how bad it is? Exploitability and impact are not fixed properties of a CVE. They depend on runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A high CVSS score on a dependency that never runs is not the same as real risk. Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter.
7. How do I fix CVE-2020-15265?
   • Upgrade tensorflow to 2.4.0 or later
   • Upgrade tensorflow-cpu to 2.4.0 or later
   • Upgrade tensorflow-gpu to 2.4.0 or later

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