Integer overflow in TFLite
Impact
An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations:
int embedding_size = 1;
int lookup_size = 1;
for (int i = 0; i < lookup_rank - 1; i++, k++) {
const int dim = dense_shape->data.i32[i];
lookup_size *= dim;
output_shape->data[k] = dim;
}
for (int i = 1; i < embedding_rank; i++, k++) {
const int dim = SizeOfDimension(value, i);
embedding_size *= dim;
output_shape->data[k] = dim;
}Both embedding_size and lookup_size are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication.
In certain scenarios, this can then result in heap OOB read/write.
Patches
We have patched the issue in GitHub commits f19be71717c497723ba0cea0379e84f061a75e01, 1de49725a5fc4e48f1a3b902ec3599ee99283043 and a4e401da71458d253b05e41f28637b65baf64be4.
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by Wang Xuan of Qihoo 360 AIVul Team.
Czego dotyczy
Kliknij technologię, aby zobaczyć wszystkie dotyczące jej podatności.
| Platforma | Paczka | Podatne wersje | Poprawka w wersji |
|---|---|---|---|
| Python (PyPI) | tensorflow | < 2.5.3, >= 2.6.0, < 2.6.3, >= 2.7.0, < 2.7.1 | 2.5.3, 2.6.3, 2.7.1 |
| Python (PyPI) | tensorflow-cpu | < 2.5.3, >= 2.6.0, < 2.6.3 | 2.5.3, 2.6.3 |
| Python (PyPI) | tensorflow-gpu | < 2.5.3, >= 2.6.0, < 2.6.3 | 2.5.3, 2.6.3 |
Odnośniki
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-98p5-x8x4-c9m5
- https://nvd.nist.gov/vuln/detail/CVE-2022-23559
- https://github.com/tensorflow/tensorflow/commit/1de49725a5fc4e48f1a3b902ec3599ee99283043
- https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4
- https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-68.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-123.yaml
- https://github.com/tensorflow/tensorflow
- https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/embedding_lookup_sparse.cc#L179-L189
- https://pypi.org/project/tensorflow
- https://github.com/advisories/GHSA-98p5-x8x4-c9m5
- Wektor CVSS
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H- Słabość (CWE)
CWE-190- Platforma
- Python (PyPI)
- Paczka
tensorflow, tensorflow-cpu, tensorflow-gpu- Ujawniono
- 04.02.2022
- Aktualizacja
- 11.10.2026 14:18
Źródła i licencje
OSV / GitHub Advisory Database - PYSEC-2022-68 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2022-123 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-98p5-x8x4-c9m5 - CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2026-3146 - PyPA Advisory Database; CC BY 4.0
Historia podatności: tensorflow-gpu
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