What is CVE-2020-15190?

CVE-2020-15190 is a medium-severity improper input validation vulnerability in tensorflow (pip), affecting versions < 1.15.4. It is fixed in 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1. The application does not adequately validate input before processing it, allowing unexpected values to reach sensitive code paths.

How severe is CVE-2020-15190?

CVE-2020-15190 has a CVSS score of 5.3 (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.

Which packages are affected by CVE-2020-15190?

tensorflow (pip) versions < 1.15.4; tensorflow-cpu (pip) versions < 1.15.4; tensorflow-gpu (pip) versions < 1.15.4.

Is there a fix for CVE-2020-15190?

Yes. CVE-2020-15190 is fixed in 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1. Upgrade to this version or later.

Is CVE-2020-15190 exploitable, and should I be worried?

Whether CVE-2020-15190 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.

What actually determines whether CVE-2020-15190 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.

How do I fix CVE-2020-15190?

Upgrade: tensorflow to 1.15.4 or later, tensorflow to 2.0.3 or later, tensorflow to 2.1.2 or later, tensorflow to 2.2.1 or later, tensorflow to 2.3.1 or later, tensorflow-cpu to 1.15.4 or later, tensorflow-cpu to 2.0.3 or later, tensorflow-cpu to 2.1.2 or later, tensorflow-cpu to 2.2.1 or later, tensorflow-cpu to 2.3.1 or later, tensorflow-gpu to 1.15.4 or later, tensorflow-gpu to 2.0.3 or later, tensorflow-gpu to 2.1.2 or later, tensorflow-gpu to 2.2.1 or later, tensorflow-gpu to 2.3.1 or later.

CVE-2020-15190 in tensorflow

Q: How severe is CVE-2020-15190?

CVE-2020-15190 has a CVSS score of 5.3 (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.

Q: Which packages are affected by CVE-2020-15190?

tensorflow (pip) versions < 1.15.4; tensorflow-cpu (pip) versions < 1.15.4; tensorflow-gpu (pip) versions < 1.15.4.

Q: Is there a fix for CVE-2020-15190?

Yes. CVE-2020-15190 is fixed in 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1. Upgrade to this version or later.

Q: Is CVE-2020-15190 exploitable, and should I be worried?

Whether CVE-2020-15190 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.

Q: What actually determines whether CVE-2020-15190 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.

Q: How do I fix CVE-2020-15190?

Upgrade: tensorflow to 1.15.4 or later, tensorflow to 2.0.3 or later, tensorflow to 2.1.2 or later, tensorflow to 2.2.1 or later, tensorflow to 2.3.1 or later, tensorflow-cpu to 1.15.4 or later, tensorflow-cpu to 2.0.3 or later, tensorflow-cpu to 2.1.2 or later, tensorflow-cpu to 2.2.1 or later, tensorflow-cpu to 2.3.1 or later, tensorflow-gpu to 1.15.4 or later, tensorflow-gpu to 2.0.3 or later, tensorflow-gpu to 2.1.2 or later, tensorflow-gpu to 2.2.1 or later, tensorflow-gpu to 2.3.1 or later.

Summary

Segfault in Tensorflow

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.

Impact

The tf.raw_ops.Switch operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor.

However, the eager runtime traverses all tensors in the output:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/common_runtime/eager/kernel_and_device.cc#L308-L313

Since only one of the tensors is defined, the other one is nullptr, hence we are binding a reference to nullptr. This is undefined behavior and reported as an error if compiling with -fsanitize=null. In this case, this results in a segmentation fault

The application does not adequately validate input before processing it, allowing unexpected values to reach sensitive code paths. Typical impact: varies by context: data corruption, logic bypass, or denial of service.

CVE-2020-15190 has a CVSS score of 5.3 (Medium). 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 (1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1); upgrading removes the vulnerable code path.

Affected versions

tensorflow (< 1.15.4) tensorflow (>= 2.0.0, < 2.0.3) tensorflow (>= 2.1.0, < 2.1.2) tensorflow (= 2.2.0) tensorflow (= 2.3.0) tensorflow-cpu (< 1.15.4) tensorflow-cpu (>= 2.0.0, < 2.0.3) tensorflow-cpu (>= 2.1.0, < 2.1.2) tensorflow-cpu (= 2.2.0) tensorflow-cpu (= 2.3.0) tensorflow-gpu (< 1.15.4) tensorflow-gpu (>= 2.0.0, < 2.0.3) tensorflow-gpu (>= 2.1.0, < 2.1.2) tensorflow-gpu (= 2.2.0) tensorflow-gpu (= 2.3.0)

Security releases

tensorflow → 1.15.4 (pip) tensorflow → 2.0.3 (pip) tensorflow → 2.1.2 (pip) tensorflow → 2.2.1 (pip) tensorflow → 2.3.1 (pip) tensorflow-cpu → 1.15.4 (pip) tensorflow-cpu → 2.0.3 (pip) tensorflow-cpu → 2.1.2 (pip) tensorflow-cpu → 2.2.1 (pip) tensorflow-cpu → 2.3.1 (pip) tensorflow-gpu → 1.15.4 (pip) tensorflow-gpu → 2.0.3 (pip) tensorflow-gpu → 2.1.2 (pip) tensorflow-gpu → 2.2.1 (pip) tensorflow-gpu → 2.3.1 (pip)

Kodem intelligence

Severity tells you how bad this could be in the worst case. It does not tell you whether you are exposed. Exploitability and impact are functions of runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A vulnerable package can sit in your dependency tree and never run.

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. Kodem's runtime-powered SCA identifies whether this CVE is reachable in your applications.

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Remediation advice

We have patched the issue in da8558533d925694483d2c136a9220d6d49d843c and will release a patch release for all affected versions.

We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Frequently Asked Questions

What is CVE-2020-15190? CVE-2020-15190 is a medium-severity improper input validation vulnerability in tensorflow (pip), affecting versions < 1.15.4. It is fixed in 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1. The application does not adequately validate input before processing it, allowing unexpected values to reach sensitive code paths.
How severe is CVE-2020-15190? CVE-2020-15190 has a CVSS score of 5.3 (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.
Which packages are affected by CVE-2020-15190?
- tensorflow (pip) (versions < 1.15.4)
- tensorflow-cpu (pip) (versions < 1.15.4)
- tensorflow-gpu (pip) (versions < 1.15.4)
Is there a fix for CVE-2020-15190? Yes. CVE-2020-15190 is fixed in 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1. Upgrade to this version or later.
Is CVE-2020-15190 exploitable, and should I be worried? Whether CVE-2020-15190 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
What actually determines whether CVE-2020-15190 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.
How do I fix CVE-2020-15190?
- Upgrade tensorflow to 1.15.4 or later
- Upgrade tensorflow to 2.0.3 or later
- Upgrade tensorflow to 2.1.2 or later
- Upgrade tensorflow to 2.2.1 or later
- Upgrade tensorflow to 2.3.1 or later
- Upgrade tensorflow-cpu to 1.15.4 or later
- Upgrade tensorflow-cpu to 2.0.3 or later
- Upgrade tensorflow-cpu to 2.1.2 or later
- Upgrade tensorflow-cpu to 2.2.1 or later
- Upgrade tensorflow-cpu to 2.3.1 or later
- Upgrade tensorflow-gpu to 1.15.4 or later
- Upgrade tensorflow-gpu to 2.0.3 or later
- Upgrade tensorflow-gpu to 2.1.2 or later
- Upgrade tensorflow-gpu to 2.2.1 or later
- Upgrade tensorflow-gpu to 2.3.1 or later

Other vulnerabilities in tensorflow

CVE-IDSeverityPackage summary

CVE-2023-33976Hightensorflow: TensorFlow has segfault in array_ops.upper_bound CVE-2023-25661Mediumtensorflow: TensorFlow Denial of Service vulnerability CVE-2023-25659Hightensorflow: TensorFlow vulnerable to Out-of-Bounds Read in DynamicStitch CVE-2023-25660Hightensorflow: TensorFlow vulnerable to seg fault in `tf.raw_ops.Print`CVE-2023-25662Hightensorflow: TensorFlow vulnerable to integer overflow in EditDistance