Null pointer dereference in TFLite MLIR optimizations
High7.8CVE-2021-37689 · Published Aug 25, 2021 · updated Sep 10, 2026
### 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: This is caused by the MLIR optimization of `L2NormalizeReduceAxis` operator. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/compiler/mlir/lite/transforms/optimize.cc#L67-L70) unconditionally dereferences a pointer to an iterator to a vector without checking that the vector has elements: ```cc bool L2NormalizeReduceAxis(Value sq_op, DenseElementsAttr axis) { if (sq_op.getType().cast<ShapedType>().getRank() - 1 == *axis.getValues<int>().begin() || *axis.getValues<int>().begin() == -1) { // ... } // ... } ``` ### Patches We have patched the issue in GitHub commit [d6b57f461b39fd1aa8c1b870f1b974aac3554955](https://github.com/tensorflow/tensorflow/commit/d6b57f461b39fd1aa8c1b870f1b974aac3554955). 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 s...
Affected versions
| Package | Affected | Fixed in |
|---|---|---|
| tensorflow PyPI | < 2.3.4 | 2.3.4 |
| >= 2.4.0, < 2.4.3 | 2.4.3 | |
| >= 2.5.0, < 2.5.1 | 2.5.1 |
Details and references
### 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: This is caused by the MLIR optimization of `L2NormalizeReduceAxis` operator. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/compiler/mlir/lite/transforms/optimize.cc#L67-L70) unconditionally dereferences a pointer to an iterator to a vector without checking that the vector has elements: ```cc bool L2NormalizeReduceAxis(Value sq_op, DenseElementsAttr axis) { if (sq_op.getType().cast<ShapedType>().getRank() - 1 == *axis.getValues<int>().begin() || *axis.getValues<int>().begin() == -1) { // ... } // ... } ``` ### Patches We have patched the issue in GitHub commit [d6b57f461b39fd1aa8c1b870f1b974aac3554955](https://github.com/tensorflow/tensorflow/commit/d6b57f461b39fd1aa8c1b870f1b974aac3554955). 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.
- CVSS 3.1
- CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
- Severity from
- GitHub (reviewed advisory)
- Weakness
- CWE-476
- Also known as
- BIT-tensorflow-2021-37689, CVE-2021-37689, PYSEC-2021-311, PYSEC-2021-602, PYSEC-2021-800
- github.com/tensorflow/tensorflow/security/advisories/GHSA-wf5p-c75w-w3wh
- nvd.nist.gov/vuln/detail/CVE-2021-37689
- github.com/tensorflow/tensorflow/commit/d6b57f461b39fd1aa8c1b870f1b974aac3554955
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-602.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-800.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-311.yaml
- github.com/tensorflow/tensorflow
- github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/compiler/mlir/lite/transforms/optimize.cc#L67-L70
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