Heap OOB in `UpperBound` and `LowerBound`
Impact
An attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to tf.raw_ops.UpperBound:
import tensorflow as tf
tf.raw_ops.UpperBound(
sorted_input=[1,2,3],
values=tf.constant(value=[[0,0,0],[1,1,1],[2,2,2]],dtype=tf.int64),
out_type=tf.int64)
The implementation does not validate the rank of sorted_input argument:
void Compute(OpKernelContext* ctx) override {
const Tensor& sorted_inputs_t = ctx->input(0);
// ...
OP_REQUIRES(ctx, sorted_inputs_t.dim_size(0) == values_t.dim_size(0),
Status(error::INVALID_ARGUMENT,
"Leading dim_size of both tensors must match."));
// ...
if (output_t->dtype() == DT_INT32) {
OP_REQUIRES(ctx,
FastBoundsCheck(sorted_inputs_t.dim_size(1), ...));
// ...
}
As we access the first two dimensions of sorted_inputs_t tensor, it must have rank at least 2.
A similar issue occurs in tf.raw_ops.LowerBound.
Patches
We have patched the issue in GitHub commit 42459e4273c2e47a3232cc16c4f4fff3b3a35c38.
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 for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
Software afectado
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| Plataforma | Paquete | Versiones vulnerables | Corregida en |
|---|---|---|---|
| Python (PyPI) | tensorflow | < 2.3.4, >= 2.4.0, < 2.4.3, >= 2.5.0, < 2.5.1 | 2.3.4, 2.4.3, 2.5.1 |
| Python (PyPI) | tensorflow-cpu | < 2.3.4, >= 2.4.0, < 2.4.3, >= 2.5.0, < 2.5.1 | 2.3.4, 2.4.3, 2.5.1 |
| Python (PyPI) | tensorflow-gpu | < 2.3.4, >= 2.4.0, < 2.4.3, >= 2.5.0, < 2.5.1 | 2.3.4, 2.4.3, 2.5.1 |
Referencias
- https://github.com/tensorflow/tensorflow/commit/42459e4273c2e47a3232cc16c4f4fff3b3a35c38
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9697-98pf-4rw7
- https://nvd.nist.gov/vuln/detail/CVE-2021-37670
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-583.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-781.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-292.yaml
- https://github.com/tensorflow/tensorflow
- Vector CVSS
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N- Debilidad (CWE)
CWE-125- Plataforma
- Python (PyPI)
- Paquete
tensorflow, tensorflow-cpu, tensorflow-gpu- Publicada
- 12.08.2021
- Actualizado
- 11.10.2026 13:19
Fuentes y licencias
OSV / GitHub Advisory Database - PYSEC-2021-292 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-583 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-781 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-9697-98pf-4rw7 - CC BY 4.0
Historial de vulnerabilidades: tensorflow-gpu
Todas las vulnerabilidades publicadas de este paquete (Python (PyPI)), de la más reciente a la más antigua. Muestra si es un fallo aislado o un paquete que hay que vigilar.
- Entradas en total
- 427
- Primera → última
- 04.2019 07.2024
- Sin corrección
- 0
- Explotada en ataques
- 0
Distribución por gravedad
- Crítica 6
- Alta 109
- Media 207
- Baja 105
Divulgaciones por año
- 2019: 7
- 2020: 35
- 2021: 200
- 2022: 166
- 2023: 18
- 2024: 1
- 2025: 0
- 2026: 0
-
Heap OOB in `UpperBound` and `LowerBound` esta entrada
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