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CVE-2022-23559

HIGH
Published 2022-02-04T22:32:37.000Z
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CVSS Score

V3.1
8.8
/10
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Base Score Metrics
Exploitability: N/A Impact: N/A

EPSS Score

v2025.03.14
0.005
probability
of exploitation in the wild

There is a 0.5% chance that this vulnerability will be exploited in the wild within the next 30 days.

Updated: 2025-06-25
Exploit Probability
Percentile: 0.645
Higher than 64.5% of all CVEs

Attack Vector Metrics

Attack Vector
NETWORK
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED

Impact Metrics

Confidentiality
HIGH
Integrity
HIGH
Availability
HIGH

Description

Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations. Both `embedding_size` and `lookup_size` are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication. In certain scenarios, this can then result in heap OOB read/write. Users are advised to upgrade to a patched version.

Available Exploits

No exploits available for this CVE.

Related News

No news articles found for this CVE.

Affected Products

GitHub Security Advisories

Community-driven vulnerability intelligence from GitHub

✓ GitHub Reviewed HIGH

Integer overflow in TFLite

GHSA-98p5-x8x4-c9m5

Advisory Details

### Impact An attacker can craft a TFLite model that would cause an integer overflow [in embedding lookup operations](https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/embedding_lookup_sparse.cc#L179-L189): ```cc int embedding_size = 1; int lookup_size = 1; for (int i = 0; i < lookup_rank - 1; i++, k++) { const int dim = dense_shape->data.i32[i]; lookup_size *= dim; output_shape->data[k] = dim; } for (int i = 1; i < embedding_rank; i++, k++) { const int dim = SizeOfDimension(value, i); embedding_size *= dim; output_shape->data[k] = dim; } ``` Both `embedding_size` and `lookup_size` are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication. In certain scenarios, this can then result in heap OOB read/write. ### Patches We have patched the issue in GitHub commits [f19be71717c497723ba0cea0379e84f061a75e01](https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01), [1de49725a5fc4e48f1a3b902ec3599ee99283043](https://github.com/tensorflow/tensorflow/commit/1de49725a5fc4e48f1a3b902ec3599ee99283043) and [a4e401da71458d253b05e41f28637b65baf64be4](https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4). The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Wang Xuan of Qihoo 360 AIVul Team.

Affected Packages

PyPI tensorflow
ECOSYSTEM: ≥0 <2.5.3
PyPI tensorflow
ECOSYSTEM: ≥2.6.0 <2.6.3
PyPI tensorflow
ECOSYSTEM: ≥2.7.0 <2.7.1
PyPI tensorflow-cpu
ECOSYSTEM: ≥0 <2.5.3
PyPI tensorflow-cpu
ECOSYSTEM: ≥2.6.0 <2.6.3
PyPI tensorflow-cpu
ECOSYSTEM: ≥2.7.0 <2.7.1
PyPI tensorflow-gpu
ECOSYSTEM: ≥0 <2.5.3
PyPI tensorflow-gpu
ECOSYSTEM: ≥2.6.0 <2.6.3
PyPI tensorflow-gpu
ECOSYSTEM: ≥2.7.0 <2.7.1

CVSS Scoring

CVSS Score

7.5

CVSS Vector

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Advisory provided by GitHub Security Advisory Database. Published: February 9, 2022, Modified: November 13, 2024

References

Published: 2022-02-04T22:32:37.000Z
Last Modified: 2025-04-22T18:23:20.273Z
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