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High severityNVD Advisory· Published Feb 3, 2022· Updated May 5, 2025

Division by zero in TFLite

CVE-2022-21741

Description

A division by zero in TensorFlow's depthwise convolution implementation allows an attacker to cause a denial of service via a crafted TFLite model.

AI Insight

LLM-synthesized narrative grounded in this CVE's description and references.

A division by zero in TensorFlow's depthwise convolution implementation allows an attacker to cause a denial of service via a crafted TFLite model.

Vulnerability

A division by zero vulnerability exists in the implementation of depthwise convolutions in TensorFlow (TFLite). The parameters of the convolution are user-controlled and are used in a division operation to determine the size of padding to add before applying the convolution. There is no check that the divisor is strictly positive, leading to a division by zero. Affected versions include TensorFlow 2.5.0 through 2.5.2, 2.6.0 through 2.6.2, and 2.7.0. The fix is included in TensorFlow 2.8.0 and cherry-picked to 2.7.1, 2.6.3, and 2.5.3 [1].

Exploitation

An attacker can craft a TFLite model with specific convolution parameters that trigger a division by zero. No authentication or special privileges are required; the attacker only needs to supply the malicious model to a TensorFlow application that processes it. The division occurs during model loading or inference, causing a crash [1].

Impact

Successful exploitation results in a denial of service (DoS) due to the division by zero, causing the TensorFlow process to crash. No code execution or data disclosure is indicated [1].

Mitigation

Users should upgrade to TensorFlow 2.8.0, 2.7.1, 2.6.3, or 2.5.3, which contain the fix. No workaround is available for unpatched versions [1].

References
  1. NVD - CVE-2022-21741

AI Insight generated on May 21, 2026. Synthesized from this CVE's description and the cited reference URLs; citations are validated against the source bundle.

Affected packages

Versions sourced from the GitHub Security Advisory.

PackageAffected versionsPatched versions
tensorflowPyPI
< 2.5.32.5.3
tensorflowPyPI
>= 2.6.0, < 2.6.32.6.3
tensorflowPyPI
>= 2.7.0, < 2.7.12.7.1
tensorflow-cpuPyPI
< 2.5.32.5.3
tensorflow-cpuPyPI
>= 2.6.0, < 2.6.32.6.3
tensorflow-cpuPyPI
>= 2.7.0, < 2.7.12.7.1
tensorflow-gpuPyPI
< 2.5.32.5.3
tensorflow-gpuPyPI
>= 2.6.0, < 2.6.32.6.3
tensorflow-gpuPyPI
>= 2.7.0, < 2.7.12.7.1

Affected products

5

Patches

1
e5b0eec199c2

[lite] Add validation check for dilation height/width to be positive integers.

https://github.com/tensorflow/tensorflowKarim NosirDec 15, 2021via ghsa
commit
1 file changed · +2 −0
  • tensorflow/lite/kernels/depthwise_conv.cc+2 −0 modified
    @@ -115,6 +115,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 
   TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
   TF_LITE_ENSURE_EQ(context, NumDimensions(filter), 4);
+  TF_LITE_ENSURE(context, params->dilation_height_factor > 0);
+  TF_LITE_ENSURE(context, params->dilation_width_factor > 0);
 
   const TfLiteType data_type = input->type;

Vulnerability mechanics

Generated on May 9, 2026. Inputs: CWE entries + fix-commit diffs from this CVE's patches. Citations validated against bundle.

References

7

News mentions

0

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