What is CVE-2020-15212?

CVE-2020-15212 is a critical-severity out-of-bounds write vulnerability in tensorflow (pip), affecting versions = 2.2.0. It is fixed in 2.2.1, 2.3.1. A write operation targets a memory location beyond the intended buffer boundary.

How severe is CVE-2020-15212?

CVE-2020-15212 has a CVSS score of 8.1 (Critical). 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-2020-15212?

tensorflow (pip) versions = 2.2.0; tensorflow-cpu (pip) versions = 2.2.0; tensorflow-gpu (pip) versions = 2.2.0.

Is there a fix for CVE-2020-15212?

Yes. CVE-2020-15212 is fixed in 2.2.1, 2.3.1. Upgrade to this version or later.

Is CVE-2020-15212 exploitable, and should I be worried?

Whether CVE-2020-15212 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-2020-15212 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-2020-15212?

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

CVE-2020-15212 in tensorflow

Summary

Out of bounds access in tensorflow-lite

Workarounds

A potential workaround would be to add a custom Verifier to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model.

A similar validation could be done if the segment ids are generated at runtime between inference steps.

If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

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 discovered from a variant analysis of GHSA-p2cq-cprg-frvm.

Impact

In TensorFlow Lite models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/reference/reference_ops.h#L2625-L2631

Users having access to segment_ids_data can alter output_index and then write to outside of output_data buffer.

This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits.

A write operation targets a memory location beyond the intended buffer boundary. Typical impact: memory corruption, crash, or arbitrary code execution.

CVE-2020-15212 has a CVSS score of 8.1 (Critical). 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 (2.2.1, 2.3.1); upgrading removes the vulnerable code path.

Affected versions

tensorflow (= 2.2.0) tensorflow (= 2.3.0) tensorflow-cpu (= 2.2.0) tensorflow-cpu (= 2.3.0) tensorflow-gpu (= 2.2.0) tensorflow-gpu (= 2.3.0)

Security releases

tensorflow → 2.2.1 (pip) tensorflow → 2.3.1 (pip) tensorflow-cpu → 2.2.1 (pip) tensorflow-cpu → 2.3.1 (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 204945b and will release patch releases for all affected versions.

We recommend users to upgrade to TensorFlow 2.2.1, or 2.3.1.

Frequently Asked Questions

What is CVE-2020-15212? CVE-2020-15212 is a critical-severity out-of-bounds write vulnerability in tensorflow (pip), affecting versions = 2.2.0. It is fixed in 2.2.1, 2.3.1. A write operation targets a memory location beyond the intended buffer boundary.
How severe is CVE-2020-15212? CVE-2020-15212 has a CVSS score of 8.1 (Critical). 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-2020-15212?
- tensorflow (pip) (versions = 2.2.0)
- tensorflow-cpu (pip) (versions = 2.2.0)
- tensorflow-gpu (pip) (versions = 2.2.0)
Is there a fix for CVE-2020-15212? Yes. CVE-2020-15212 is fixed in 2.2.1, 2.3.1. Upgrade to this version or later.
Is CVE-2020-15212 exploitable, and should I be worried? Whether CVE-2020-15212 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-2020-15212 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-2020-15212?
- Upgrade tensorflow to 2.2.1 or later
- Upgrade tensorflow to 2.3.1 or later
- Upgrade tensorflow-cpu to 2.2.1 or later
- Upgrade tensorflow-cpu to 2.3.1 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