What is CVE-2021-29591?

CVE-2021-29591 is a high-severity security vulnerability in tensorflow (pip), affecting versions < 2.1.4. It is fixed in 2.1.4, 2.2.3, 2.3.3, 2.4.2.

How severe is CVE-2021-29591?

CVE-2021-29591 has a CVSS score of 7.3 (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.

Which packages are affected by CVE-2021-29591?

tensorflow (pip) versions < 2.1.4; tensorflow-cpu (pip) versions < 2.1.4; tensorflow-gpu (pip) versions < 2.1.4.

Is there a fix for CVE-2021-29591?

Yes. CVE-2021-29591 is fixed in 2.1.4, 2.2.3, 2.3.3, 2.4.2. Upgrade to this version or later.

Is CVE-2021-29591 exploitable, and should I be worried?

Whether CVE-2021-29591 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-2021-29591 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-2021-29591?

Upgrade: tensorflow to 2.1.4 or later, tensorflow to 2.2.3 or later, tensorflow to 2.3.3 or later, tensorflow to 2.4.2 or later, tensorflow-cpu to 2.1.4 or later, tensorflow-cpu to 2.2.3 or later, tensorflow-cpu to 2.3.3 or later, tensorflow-cpu to 2.4.2 or later, tensorflow-gpu to 2.1.4 or later, tensorflow-gpu to 2.2.3 or later, tensorflow-gpu to 2.3.3 or later, tensorflow-gpu to 2.4.2 or later.

CVE-2021-29591 in tensorflow

Q: How severe is CVE-2021-29591?

CVE-2021-29591 has a CVSS score of 7.3 (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.

Q: Which packages are affected by CVE-2021-29591?

tensorflow (pip) versions < 2.1.4; tensorflow-cpu (pip) versions < 2.1.4; tensorflow-gpu (pip) versions < 2.1.4.

Q: Is there a fix for CVE-2021-29591?

Yes. CVE-2021-29591 is fixed in 2.1.4, 2.2.3, 2.3.3, 2.4.2. Upgrade to this version or later.

Q: Is CVE-2021-29591 exploitable, and should I be worried?

Whether CVE-2021-29591 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-2021-29591 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-2021-29591?

Upgrade: tensorflow to 2.1.4 or later, tensorflow to 2.2.3 or later, tensorflow to 2.3.3 or later, tensorflow to 2.4.2 or later, tensorflow-cpu to 2.1.4 or later, tensorflow-cpu to 2.2.3 or later, tensorflow-cpu to 2.3.3 or later, tensorflow-cpu to 2.4.2 or later, tensorflow-gpu to 2.1.4 or later, tensorflow-gpu to 2.2.3 or later, tensorflow-gpu to 2.3.3 or later, tensorflow-gpu to 2.4.2 or later.

Summary

Stack overflow due to looping TFLite subgraph

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

TFlite graphs must not have loops between nodes. However, this condition was not checked and an attacker could craft models that would result in infinite loop during evaluation. In certain cases, the infinite loop would be replaced by stack overflow due to too many recursive calls.

For example, the While implementation could be tricked into a scneario where both the body and the loop subgraphs are the same. Evaluating one of the subgraphs means calling the Eval function for the other and this quickly exhaust all stack space.

CVE-2021-29591 has a CVSS score of 7.3 (High). 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 (2.1.4, 2.2.3, 2.3.3, 2.4.2); upgrading removes the vulnerable code path.

Affected versions

tensorflow (< 2.1.4) tensorflow (>= 2.2.0, < 2.2.3) tensorflow (>= 2.3.0, < 2.3.3) tensorflow (>= 2.4.0, < 2.4.2) tensorflow-cpu (< 2.1.4) tensorflow-cpu (>= 2.2.0, < 2.2.3) tensorflow-cpu (>= 2.3.0, < 2.3.3) tensorflow-cpu (>= 2.4.0, < 2.4.2) tensorflow-gpu (< 2.1.4) tensorflow-gpu (>= 2.2.0, < 2.2.3) tensorflow-gpu (>= 2.3.0, < 2.3.3) tensorflow-gpu (>= 2.4.0, < 2.4.2)

Security releases

tensorflow → 2.1.4 (pip) tensorflow → 2.2.3 (pip) tensorflow → 2.3.3 (pip) tensorflow → 2.4.2 (pip) tensorflow-cpu → 2.1.4 (pip) tensorflow-cpu → 2.2.3 (pip) tensorflow-cpu → 2.3.3 (pip) tensorflow-cpu → 2.4.2 (pip) tensorflow-gpu → 2.1.4 (pip) tensorflow-gpu → 2.2.3 (pip) tensorflow-gpu → 2.3.3 (pip) tensorflow-gpu → 2.4.2 (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 GitHub commit 9c1dc920d8ffb4893d6c9d27d1f039607b326743 (for the While operator) and in GitHub commit c6173f5fe66cdbab74f4f869311fe6aae2ba35f4 (in general).

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

Frequently Asked Questions

What is CVE-2021-29591? CVE-2021-29591 is a high-severity security vulnerability in tensorflow (pip), affecting versions < 2.1.4. It is fixed in 2.1.4, 2.2.3, 2.3.3, 2.4.2.
How severe is CVE-2021-29591? CVE-2021-29591 has a CVSS score of 7.3 (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.
Which packages are affected by CVE-2021-29591?
- tensorflow (pip) (versions < 2.1.4)
- tensorflow-cpu (pip) (versions < 2.1.4)
- tensorflow-gpu (pip) (versions < 2.1.4)
Is there a fix for CVE-2021-29591? Yes. CVE-2021-29591 is fixed in 2.1.4, 2.2.3, 2.3.3, 2.4.2. Upgrade to this version or later.
Is CVE-2021-29591 exploitable, and should I be worried? Whether CVE-2021-29591 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-2021-29591 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-2021-29591?
- Upgrade tensorflow to 2.1.4 or later
- Upgrade tensorflow to 2.2.3 or later
- Upgrade tensorflow to 2.3.3 or later
- Upgrade tensorflow to 2.4.2 or later
- Upgrade tensorflow-cpu to 2.1.4 or later
- Upgrade tensorflow-cpu to 2.2.3 or later
- Upgrade tensorflow-cpu to 2.3.3 or later
- Upgrade tensorflow-cpu to 2.4.2 or later
- Upgrade tensorflow-gpu to 2.1.4 or later
- Upgrade tensorflow-gpu to 2.2.3 or later
- Upgrade tensorflow-gpu to 2.3.3 or later
- Upgrade tensorflow-gpu to 2.4.2 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