CVE-2021-37679

Heap OOB in nested tf.map_fn with RaggedTensors

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Attribution

This vulnerability has been reported by Haris Sahovic.

It is possible to nest a tf.map_fn within another tf.map_fn call. However, if the input tensor is a RaggedTensor and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap:

import tensorflow as tf
x = tf.ragged.constant([[1,2,3], [4,5], [6]])
t = tf.map_fn(lambda r: tf.map_fn(lambda y: r, r), x)
z = tf.ragged.constant([[[1,2,3],[1,2,3],[1,2,3]],[[4,5],[4,5]],[[6]]])

The t and z outputs should be identical, however this is not the case. The last row of t contains data from the heap which can be used to leak other memory information.

The bug lies in the conversion from a Variant tensor to a RaggedTensor. The implementation does not check that all inner shapes match and this results in the additional dimensions in the above example.

The same implementation can result in data loss, if input tensor is tweaked:

import tensorflow as tf
x = tf.ragged.constant([[1,2], [3,4,5], [6]])
t = tf.map_fn(lambda r: tf.map_fn(lambda y: r, r), x) 

Here, the output tensor will only have 2 elements for each inner dimension.

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

CVE-2021-37679 has a CVSS score of 7.1 (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 4e2565483d0ffcadc719bd44893fb7f609bb5f12.

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-2021-37679? CVE-2021-37679 is a high-severity out-of-bounds read vulnerability in tensorflow (pip), affecting versions < 2.3.4. It is fixed in 2.3.4, 2.4.3, 2.5.1. A read operation accesses a memory location beyond the intended buffer boundary.
2. How severe is CVE-2021-37679? CVE-2021-37679 has a CVSS score of 7.1 (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-2021-37679?
   • 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-2021-37679? Yes. CVE-2021-37679 is fixed in 2.3.4, 2.4.3, 2.5.1. Upgrade to this version or later.
5. Is CVE-2021-37679 exploitable, and should I be worried? Whether CVE-2021-37679 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-2021-37679 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-2021-37679?
   • 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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