Summary
Integer division by 0 in tf.raw_ops.AllToAll
For more information
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Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
Impact
The shape inference code for AllToAll can be made to execute a division by 0:
import tensorflow as tf
@tf.function
def func():
return tf.raw_ops.AllToAll(
input=[0.0, 0.1652, 0.6543],
group_assignment=[1, -1],
concat_dimension=0,
split_dimension=0,
split_count=0)
func()
This occurs whenever the split_count argument is 0:
TF_RETURN_IF_ERROR(c->GetAttr("split_count", &split_count));
...
for (int32_t i = 0; i < rank; ++i) {
...
dims[i] = c->MakeDim(c->Value(dims[i]) / split_count);
...
}
CVE-2021-41218 has a CVSS score of 5.5 (Medium). 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 (2.6.1, 2.5.2, 2.4.4); upgrading removes the vulnerable code path.
Affected versions
Security releases
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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We have patched the issue in GitHub commit a8ad3e5e79c75f36edb81e0ba3f3c0c5442aeddc.
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
Frequently Asked Questions
- What is CVE-2021-41218? CVE-2021-41218 is a medium-severity security vulnerability in tensorflow (pip), affecting versions >= 2.6.0, < 2.6.1. It is fixed in 2.6.1, 2.5.2, 2.4.4.
- How severe is CVE-2021-41218? CVE-2021-41218 has a CVSS score of 5.5 (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-2021-41218?
tensorflow(pip) (versions >= 2.6.0, < 2.6.1)tensorflow-cpu(pip) (versions >= 2.6.0, < 2.6.1)tensorflow-gpu(pip) (versions >= 2.6.0, < 2.6.1)
- Is there a fix for CVE-2021-41218? Yes. CVE-2021-41218 is fixed in 2.6.1, 2.5.2, 2.4.4. Upgrade to this version or later.
- Is CVE-2021-41218 exploitable, and should I be worried? Whether CVE-2021-41218 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-2021-41218 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-2021-41218?
- Upgrade
tensorflowto 2.6.1 or later - Upgrade
tensorflowto 2.5.2 or later - Upgrade
tensorflowto 2.4.4 or later - Upgrade
tensorflow-cputo 2.6.1 or later - Upgrade
tensorflow-cputo 2.5.2 or later - Upgrade
tensorflow-cputo 2.4.4 or later - Upgrade
tensorflow-gputo 2.6.1 or later - Upgrade
tensorflow-gputo 2.5.2 or later - Upgrade
tensorflow-gputo 2.4.4 or later
- Upgrade