CVE-2021-37652

Use after free in boosted trees creation

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Attribution

This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

The implementation for tf.raw_ops.BoostedTreesCreateEnsemble can result in a use after free error if an attacker supplies specially crafted arguments:

import tensorflow as tf

v= tf.Variable([0.0])
tf.raw_ops.BoostedTreesCreateEnsemble(
  tree_ensemble_handle=v.handle,
  stamp_token=[0],
  tree_ensemble_serialized=['0']) 

The implementation uses a reference counted resource and decrements the refcount if the initialization fails, as it should. However, when the code was written, the resource was represented as a naked pointer but later refactoring has changed it to be a smart pointer. Thus, when the pointer leaves the scope, a subsequent free-ing of the resource occurs, but this fails to take into account that the refcount has already reached 0, thus the resource has been already freed. During this double-free process, members of the resource object are accessed for cleanup but they are invalid as the entire resource has been freed.

Memory is accessed after it has been freed, leading to undefined behavior in native code. Typical impact: memory corruption, crash, or potential code execution.

CVE-2021-37652 has a CVSS score of 7.8 (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 (2.3.4, 2.4.3, 2.5.1); upgrading removes the vulnerable code path.

Kodem intelligence

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We have patched the issue in GitHub commit 5ecec9c6fbdbc6be03295685190a45e7eee726ab.

The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

1. What is CVE-2021-37652? CVE-2021-37652 is a high-severity use after free vulnerability in tensorflow (pip), affecting versions < 2.3.4. It is fixed in 2.3.4, 2.4.3, 2.5.1. Memory is accessed after it has been freed, leading to undefined behavior in native code.
2. How severe is CVE-2021-37652? CVE-2021-37652 has a CVSS score of 7.8 (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.
3. Which packages are affected by CVE-2021-37652?
   • tensorflow (pip) (versions < 2.3.4)
   • tensorflow-cpu (pip) (versions < 2.3.4)
   • tensorflow-gpu (pip) (versions < 2.3.4)
4. Is there a fix for CVE-2021-37652? Yes. CVE-2021-37652 is fixed in 2.3.4, 2.4.3, 2.5.1. Upgrade to this version or later.
5. Is CVE-2021-37652 exploitable, and should I be worried? Whether CVE-2021-37652 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
6. What actually determines whether CVE-2021-37652 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.
7. How do I fix CVE-2021-37652?
   • Upgrade tensorflow to 2.3.4 or later
   • Upgrade tensorflow to 2.4.3 or later
   • Upgrade tensorflow to 2.5.1 or later
   • Upgrade tensorflow-cpu to 2.3.4 or later
   • Upgrade tensorflow-cpu to 2.4.3 or later
   • Upgrade tensorflow-cpu to 2.5.1 or later
   • Upgrade tensorflow-gpu to 2.3.4 or later
   • Upgrade tensorflow-gpu to 2.4.3 or later
   • Upgrade tensorflow-gpu to 2.5.1 or later

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