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.
Affected software
Click a technology to see all vulnerabilities affecting it.
| Platform | Package | Vulnerable versions | Fixed in |
|---|---|---|---|
| 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 |
References
- 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
- CVSS vector
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N- Weakness (CWE)
CWE-125- Platform
- Python (PyPI)
- Package
tensorflow, tensorflow-cpu, tensorflow-gpu- Disclosed
- 12.08.2021
- Updated
- 11.10.2026 13:19
Sources and licenses
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
Vulnerability history: tensorflow
All published vulnerabilities of this package (Python (PyPI)), newest first. Shows whether this is a one-off slip or a package that needs watching.
- Total entries
- 433
- First → latest
- 04.2019 07.2024
- Without a fix
- 0
- Exploited in attacks
- 0
Severity breakdown
- Critical 6
- High 112
- Medium 210
- Low 105
Disclosures per year
- 2019: 7
- 2020: 35
- 2021: 200
- 2022: 169
- 2023: 21
- 2024: 1
- 2025: 0
- 2026: 0
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Heap OOB in `UpperBound` and `LowerBound` this entry
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