What is GHSA-H6GW-R52C-724R?

GHSA-H6GW-R52C-724R is a critical-severity null pointer dereference vulnerability in tensorflow (pip), affecting versions < 2.5.3. It is fixed in 2.5.3, 2.6.3, 2.7.1. The application dereferences a null pointer, causing a crash.

How severe is GHSA-H6GW-R52C-724R?

GHSA-H6GW-R52C-724R has a CVSS score of 9.3 (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 GHSA-H6GW-R52C-724R?

tensorflow (pip) versions < 2.5.3; tensorflow-cpu (pip) versions < 2.5.3; tensorflow-gpu (pip) versions < 2.5.3.

Is there a fix for GHSA-H6GW-R52C-724R?

Yes. GHSA-H6GW-R52C-724R is fixed in 2.5.3, 2.6.3, 2.7.1. Upgrade to this version or later.

Is GHSA-H6GW-R52C-724R exploitable, and should I be worried?

Whether GHSA-H6GW-R52C-724R 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 GHSA-H6GW-R52C-724R 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 GHSA-H6GW-R52C-724R?

Upgrade: tensorflow to 2.5.3 or later, tensorflow to 2.6.3 or later, tensorflow to 2.7.1 or later, tensorflow-cpu to 2.5.3 or later, tensorflow-cpu to 2.6.3 or later, tensorflow-cpu to 2.7.1 or later, tensorflow-gpu to 2.5.3 or later, tensorflow-gpu to 2.6.3 or later, tensorflow-gpu to 2.7.1 or later.

GHSA-H6GW-R52C-724R in tensorflow

Q: How severe is GHSA-H6GW-R52C-724R?

GHSA-H6GW-R52C-724R has a CVSS score of 9.3 (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.

Q: Which packages are affected by GHSA-H6GW-R52C-724R?

tensorflow (pip) versions < 2.5.3; tensorflow-cpu (pip) versions < 2.5.3; tensorflow-gpu (pip) versions < 2.5.3.

Q: Is there a fix for GHSA-H6GW-R52C-724R?

Yes. GHSA-H6GW-R52C-724R is fixed in 2.5.3, 2.6.3, 2.7.1. Upgrade to this version or later.

Q: Is GHSA-H6GW-R52C-724R exploitable, and should I be worried?

Whether GHSA-H6GW-R52C-724R 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 GHSA-H6GW-R52C-724R 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 GHSA-H6GW-R52C-724R?

Upgrade: tensorflow to 2.5.3 or later, tensorflow to 2.6.3 or later, tensorflow to 2.7.1 or later, tensorflow-cpu to 2.5.3 or later, tensorflow-cpu to 2.6.3 or later, tensorflow-cpu to 2.7.1 or later, tensorflow-gpu to 2.5.3 or later, tensorflow-gpu to 2.6.3 or later, tensorflow-gpu to 2.7.1 or later.

Summary

NULL Pointer Dereference and Access of Uninitialized Pointer 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

These vulnerabilities have been reported by Yu Tian of Qihoo 360 AIVul Team and Faysal Hossain Shezan from University of Virginia. Some of the issues have been discovered internally after a careful audit of the APIs.

Impact

The code for boosted trees in TensorFlow is still missing validation. This allows malicious users to read and write outside of bounds of heap allocated data as well as trigger denial of service (via dereferencing nullptrs or via CHECK-failures).

This follows after CVE-2021-41208 where these APIs were still vulnerable to multiple security issues.

Note: Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. Instead, please use the downstream TensorFlow Decision Forests project which is newer and supports more features.

These APIs are now deprecated in TensorFlow 2.8. We will remove TensorFlow's boosted trees APIs in subsequent releases.

The application dereferences a null pointer, causing a crash. Typical impact: denial of service via crash.

GHSA-H6GW-R52C-724R has a CVSS score of 9.3 (Critical). The vector is requires local access, 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 (2.5.3, 2.6.3, 2.7.1); upgrading removes the vulnerable code path.

Affected versions

tensorflow (< 2.5.3) tensorflow (>= 2.6.0, < 2.6.3) tensorflow (= 2.7.0) tensorflow-cpu (< 2.5.3) tensorflow-cpu (>= 2.6.0, < 2.6.3) tensorflow-cpu (= 2.7.0) tensorflow-gpu (< 2.5.3) tensorflow-gpu (>= 2.6.0, < 2.6.3) tensorflow-gpu (= 2.7.0)

Security releases

tensorflow → 2.5.3 (pip) tensorflow → 2.6.3 (pip) tensorflow → 2.7.1 (pip) tensorflow-cpu → 2.5.3 (pip) tensorflow-cpu → 2.6.3 (pip) tensorflow-cpu → 2.7.1 (pip) tensorflow-gpu → 2.5.3 (pip) tensorflow-gpu → 2.6.3 (pip) tensorflow-gpu → 2.7.1 (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 known issues in multiple GitHub commits.

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

This should allow users to use existing boosted trees APIs for a while until they migrate to TensorFlow Decision Forests, while guaranteeing that known vulnerabilities are fixed.

Frequently Asked Questions

What is GHSA-H6GW-R52C-724R? GHSA-H6GW-R52C-724R is a critical-severity null pointer dereference vulnerability in tensorflow (pip), affecting versions < 2.5.3. It is fixed in 2.5.3, 2.6.3, 2.7.1. The application dereferences a null pointer, causing a crash.
How severe is GHSA-H6GW-R52C-724R? GHSA-H6GW-R52C-724R has a CVSS score of 9.3 (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 GHSA-H6GW-R52C-724R?
- tensorflow (pip) (versions < 2.5.3)
- tensorflow-cpu (pip) (versions < 2.5.3)
- tensorflow-gpu (pip) (versions < 2.5.3)
Is there a fix for GHSA-H6GW-R52C-724R? Yes. GHSA-H6GW-R52C-724R is fixed in 2.5.3, 2.6.3, 2.7.1. Upgrade to this version or later.
Is GHSA-H6GW-R52C-724R exploitable, and should I be worried? Whether GHSA-H6GW-R52C-724R 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 GHSA-H6GW-R52C-724R 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 GHSA-H6GW-R52C-724R?
- Upgrade tensorflow to 2.5.3 or later
- Upgrade tensorflow to 2.6.3 or later
- Upgrade tensorflow to 2.7.1 or later
- Upgrade tensorflow-cpu to 2.5.3 or later
- Upgrade tensorflow-cpu to 2.6.3 or later
- Upgrade tensorflow-cpu to 2.7.1 or later
- Upgrade tensorflow-gpu to 2.5.3 or later
- Upgrade tensorflow-gpu to 2.6.3 or later
- Upgrade tensorflow-gpu to 2.7.1 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