What is CVE-2020-15211?

CVE-2020-15211 is a medium-severity out-of-bounds read 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. A read operation accesses a memory location beyond the intended buffer boundary.

How severe is CVE-2020-15211?

CVE-2020-15211 has a CVSS score of 4.8 (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-2020-15211?

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-2020-15211?

Yes. CVE-2020-15211 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-2020-15211 exploitable, and should I be worried?

Whether CVE-2020-15211 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-2020-15211 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-2020-15211?

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-2020-15211 in tensorflow

Q: How severe is CVE-2020-15211?

CVE-2020-15211 has a CVSS score of 4.8 (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-2020-15211?

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-2020-15211?

Yes. CVE-2020-15211 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-2020-15211 exploitable, and should I be worried?

Whether CVE-2020-15211 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-2020-15211 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-2020-15211?

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

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 only operators which accept optional inputs use the -1 special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the 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 reported by members of the Aivul Team from Qihoo 360.

Impact

In TensorFlow Lite, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/kernel_util.cc#L36

However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative -1 value as index for these tensors:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/c/common.h#L82

This results in special casing during validation at model loading time: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/core/subgraph.cc#L566-L580

Unfortunately, this means that the -1 index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays.

This results in both read and write gadgets, albeit very limited in scope.

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

CVE-2020-15211 has a CVSS score of 4.8 (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 several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83). We will release patch releases for all versions between 1.15 and 2.3.

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-2020-15211? CVE-2020-15211 is a medium-severity out-of-bounds read 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. A read operation accesses a memory location beyond the intended buffer boundary.
How severe is CVE-2020-15211? CVE-2020-15211 has a CVSS score of 4.8 (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-2020-15211?
- 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-2020-15211? Yes. CVE-2020-15211 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-2020-15211 exploitable, and should I be worried? Whether CVE-2020-15211 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-2020-15211 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-2020-15211?
- 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