CVE-2023-25668

criticalPriorität · verfolgen · 39EPSS 0.008 · 58. PerzentilEUUS
grimoire priorityP2 · Scheduled
Exploitable?
—
no known exploitation
Likely / severe?
0.84% (58th) EPSS
CVSS 9.8 · critical
Is it mine?
6 products
Prüfe deine Angriffsfläche
Who says so?
4 sources
cnnvd · enisa_euvd · osv · nvd

EPSS · bewertet 2026-10-27

TensorFlow is an open source platform for machine learning. Attackers using Tensorflow prior to 2.12.0 or 2.11.1 can access heap memory which is not in the control of user, leading to a crash or remote code execution. The fix will be included in TensorFlow version 2.12.0 and will also cherrypick this commit on TensorFlow version 2.11.1.

01

cvss

Technischer Schweregrad, 0–10. Eine Zeile je Quelle.

9.8
010

Basiswert 0–10 · höher = schwerwiegender · Vektor überfahren zum Auflösen

v3.19.8criticalCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:Hprimary · NVD / NIST USAusnutzbarkeit 3.9 · Auswirkung 5.9
v3.19.8criticalCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:Hsecondary · ENISA EU
v3.19.8criticalCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:Hsecondary · security-advisories@github.comAusnutzbarkeit 3.9 · Auswirkung 5.9
v3.19.8criticalCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:Hsecondary · OSV
v3.19criticalCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H/E:Usecondary · CVSS-BT (t0sche)
02

SSVC

Eine Handlungsentscheidung — aus Ausnutzung und Auswirkung.

Exploitation: none · Automatable: yes · Technical Impact: totalcoordinatorcisa_ssvcUS
03

schwächen

Die CWE-Schwächeklassen, zu denen dies gehört.

CWE-122USCWE-122USCWE-125USCWE-125US
04

betroffene Produkte (6)

Betroffene Produkte und Versionsbereiche.

05

Advisories (5)

Hersteller- und Ökosystem-Meldungen dazu.

  • GHSA-gw97-ff7c-9v96TensorFlow has a heap out-of-buffer read vulnerability in the QuantizeAndDequantize operation2023-03-24ghsa · osvUS

    ### Impact Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or RCE. When axis is larger than the dim of input, c->Dim(input,axis) goes out of bound. Same problem occurs in the QuantizeAndDequantizeV2/V3/V4/V4Grad operations too. ```python import tensorflow as tf @tf.function def test(): tf.raw_ops.QuantizeAndDequantizeV2(input=[2.5], input_min=[1.0], input_max=[10.0], signed_input=True, num_bits=1, range_given=True, round_mode='HALF_TO_EVEN', narrow_range=True, axis=0x7fffffff) test() ``` ### Patches We have patched the issue in GitHub commit [7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb](https://github.com/tensorflow/tensorflow/commit/7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb). 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](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.

  • BIT-tensorflow-2023-25668TensorFlow vulnerable to heap out-of-buffer read in the QuantizeAndDequantize operation2024-03-06vendor · osvUS

    TensorFlow is an open source platform for machine learning. Attackers using Tensorflow prior to 2.12.0 or 2.11.1 can access heap memory which is not in the control of user, leading to a crash or remote code execution. The fix will be included in TensorFlow version 2.12.0 and will also cherrypick this commit on TensorFlow version 2.11.1.

  • PYSEC-2026-548TensorFlow has a heap out-of-buffer read vulnerability in the QuantizeAndDequantize operation2026-06-29vendor · osvUS

    ### Impact Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or RCE. When axis is larger than the dim of input, c->Dim(input,axis) goes out of bound. Same problem occurs in the QuantizeAndDequantizeV2/V3/V4/V4Grad operations too. ```python import tensorflow as tf @tf.function def test(): tf.raw_ops.QuantizeAndDequantizeV2(input=[2.5], input_min=[1.0], input_max=[10.0], signed_input=True, num_bits=1, range_given=True, round_mode='HALF_TO_EVEN', narrow_range=True, axis=0x7fffffff) test() ``` ### Patches We have patched the issue in GitHub commit [7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb](https://github.com/tensorflow/tensorflow/commit/7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb). 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](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.

  • PYSEC-2026-549TensorFlow has a heap out-of-buffer read vulnerability in the QuantizeAndDequantize operation2026-06-29vendor · osvUS

    ### Impact Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or RCE. When axis is larger than the dim of input, c->Dim(input,axis) goes out of bound. Same problem occurs in the QuantizeAndDequantizeV2/V3/V4/V4Grad operations too. ```python import tensorflow as tf @tf.function def test(): tf.raw_ops.QuantizeAndDequantizeV2(input=[2.5], input_min=[1.0], input_max=[10.0], signed_input=True, num_bits=1, range_given=True, round_mode='HALF_TO_EVEN', narrow_range=True, axis=0x7fffffff) test() ``` ### Patches We have patched the issue in GitHub commit [7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb](https://github.com/tensorflow/tensorflow/commit/7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb). 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](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.

  • PYSEC-2026-550TensorFlow has a heap out-of-buffer read vulnerability in the QuantizeAndDequantize operation2026-06-29vendor · osvUS

    ### Impact Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or RCE. When axis is larger than the dim of input, c->Dim(input,axis) goes out of bound. Same problem occurs in the QuantizeAndDequantizeV2/V3/V4/V4Grad operations too. ```python import tensorflow as tf @tf.function def test(): tf.raw_ops.QuantizeAndDequantizeV2(input=[2.5], input_min=[1.0], input_max=[10.0], signed_input=True, num_bits=1, range_given=True, round_mode='HALF_TO_EVEN', narrow_range=True, axis=0x7fffffff) test() ``` ### Patches We have patched the issue in GitHub commit [7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb](https://github.com/tensorflow/tensorflow/commit/7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb). 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](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.

06

auch bekannt als (11)

Weitere Namen und Kennungen dieser Entität.

07

quellen

Welche Quellen diesen Datensatz belegen und wann.

cnnvdenisa_euvdosvnvdNVDCVE.orgMITRE
veröffentlicht 2023-03-25geändert 2026-06-17
08

Referenzen (7)

Externe Belege — Patches, Meldungen, Analysen.

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