CVE-2020-26266

Uninitialized memory access in TensorFlow

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Under certain cases, a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen:

struct QUInt8 {
  QUInt8() {}
  // ...
  uint8_t value;
};

struct QInt16 {
  QInt16() {}
  // ...
  int16_t value;
};

struct QUInt16 {
  QUInt16() {}
  // ...
  uint16_t value;
};

struct QInt32 {
  QInt32() {}
  // ...
  int32_t value;
};

CVE-2020-26266 has a CVSS score of 4.4 (Medium). The vector is requires local access, low 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 (1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2); upgrading removes the vulnerable code path.

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We have patched the issue in GitHub commit ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2 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.

1. What is CVE-2020-26266? CVE-2020-26266 is a medium-severity security vulnerability in tensorflow (pip), affecting versions < 1.15.5. It is fixed in 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2.
2. How severe is CVE-2020-26266? CVE-2020-26266 has a CVSS score of 4.4 (Medium). 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-2020-26266?
   • tensorflow (pip) (versions < 1.15.5)
   • tensorflow-cpu (pip) (versions < 1.15.5)
   • tensorflow-gpu (pip) (versions < 1.15.5)
4. Is there a fix for CVE-2020-26266? Yes. CVE-2020-26266 is fixed in 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2. Upgrade to this version or later.
5. Is CVE-2020-26266 exploitable, and should I be worried? Whether CVE-2020-26266 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-2020-26266 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-2020-26266?
   • Upgrade tensorflow to 1.15.5 or later
   • Upgrade tensorflow to 2.0.4 or later
   • Upgrade tensorflow to 2.1.3 or later
   • Upgrade tensorflow to 2.2.2 or later
   • Upgrade tensorflow to 2.3.2 or later
   • Upgrade tensorflow-cpu to 1.15.5 or later
   • Upgrade tensorflow-cpu to 2.0.4 or later
   • Upgrade tensorflow-cpu to 2.1.3 or later
   • Upgrade tensorflow-cpu to 2.2.2 or later
   • Upgrade tensorflow-cpu to 2.3.2 or later
   • Upgrade tensorflow-gpu to 1.15.5 or later
   • Upgrade tensorflow-gpu to 2.0.4 or later
   • Upgrade tensorflow-gpu to 2.1.3 or later
   • Upgrade tensorflow-gpu to 2.2.2 or later
   • Upgrade tensorflow-gpu to 2.3.2 or later

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