CVE-2023-25662: Tensorflow TensorFlow Vulnerable To Integer Overflow Vulnerability
HIGHCVE-2023-25662 is a HIGH (CVSS 7.5) tensorflow vulnerable to integer overflow vulnerability (CWE-190: Integer Overflow or Wraparound) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves tensorflow vulnerable to integer overflow in editdistance. Remediation is available by upgrading affected packages to version 2.11.1.
CVE-2023-25662 at a Glance
Security Impact
CVSS: 7.5
The security impact metrics below are parsed directly from the CVSS v3.1 vector string (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H).
How to Fix CVE-2023-25662
To remediate this vulnerability, upgrade all affected Tensorflow dependencies to a patched release. Both direct and transitive dependencies should be checked, as frameworks or security libraries may pull in vulnerable versions transitively.
pip install tensorflow>=2.11.1pip install tensorflow-cpu>=2.11.1pip install tensorflow-gpu>=2.11.1Run the following command in your project directory to verify that all resolved dependencies use the patched release:
pip show tensorflowHow to Check if You Are Affected
To determine whether your Python environment is affected by CVE-2023-25662, inspect your installed packages using pip:
pip show tensorflowAnalyze the terminal output to locate any version of Tensorflow in your dependency tree. If any resolved version matches the vulnerable ranges listed in the table below (for example, versions earlier than 2.11.1), your application is affected.
Note: The pip show tensorflow command checks currently installed packages in your active Python environment.
Technical Details
Vulnerability Mechanism & Technical Analysis
This vulnerability is classified under CWE-190: Integer Overflow or Wraparound, where the product performs a calculation that can produce an integer overflow or wraparound when the logic assumes that the resulting value will always be larger than the original value. this occurs when an integer value is incremented to a value that is too large to store in the associated representation. when this occurs, the value may become a very small or negative number. In Tensorflow, software state validation or input boundaries are circumvented during processing. Review the official source advisory statement below for specific affected components, classes, and reproduction details.
Source Advisory Statement
CVE-2023-25662 Affected Tensorflow Versions
3 component(s)| Product / Package | Ecosystem | Affected Versions | Fixed Version |
|---|---|---|---|
|
tensorflow
|
PyPI |
< 2.11.1
|
2.11.1
|
|
tensorflow-cpu
|
PyPI |
< 2.11.1
|
2.11.1
|
|
tensorflow-gpu
|
PyPI |
< 2.11.1
|
2.11.1
|
References
Frequently Asked Questions
What is CVE-2023-25662?
Which Tensorflow versions are affected?
What version fixes CVE-2023-25662?
Is tensorflow affected?
Is tensorflow-cpu affected?
What is the CVSS score of CVE-2023-25662?
What CWE is associated with CVE-2023-25662?
How can I check PyPI dependencies for CVE-2023-25662?
Machine-readable OSV JSON
OSV v1.5 Schema
{
"schema_version" : "1.4.0",
"id" : "GHSA-7jvm-xxmr-v5cw",
"modified" : "2023-03-27T21:58:27Z",
"published" : "2023-03-24T21:58:31Z",
"aliases" : [ "CVE-2023-25662" ],
"summary" : "TensorFlow vulnerable to integer overflow in EditDistance",
"details" : "### Impact\nTFversion 2.11.0 //tensorflow/core/ops/array_ops.cc:1067 const Tensor* hypothesis_shape_t = c->input_tensor(2); std::vector<DimensionHandle> dims(hypothesis_shape_t->NumElements() - 1); for (int i = 0; i < dims.size(); ++i) { dims[i] = c->MakeDim(std::max(h_values(i), t_values(i))); }\n\nif hypothesis_shape_t is empty, hypothesis_shape_t->NumElements() - 1 will be integer overflow, and the it will deadlock\n```python\nimport tensorflow as tf\npara={\n 'hypothesis_indices': [[]],\n 'hypothesis_values': ['tmp/'],\n 'hypothesis_shape': [],\n 'truth_indices': [[]],\n 'truth_values': [''],\n 'truth_shape': [],\n 'normalize': False\n }\ntf.raw_ops.EditDistance(**para)\n```\n\n### Patches\nWe have patched the issue in GitHub commit [08b8e18643d6dcde00890733b270ff8d9960c56c](https://github.com/tensorflow/tensorflow/commit/08b8e18643d6dcde00890733b270ff8d9960c56c).\n\nThe fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1\n\n\n### For more information\nPlease 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.\n\n\n### Attribution\nThis vulnerability has been reported by r3pwnx",
"severity" : [ {
"type" : "CVSS_V3",
"score" : "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H"
} ],
"affected" : [ {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "0"
}, {
"fixed" : "2.11.1"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-cpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "0"
}, {
"fixed" : "2.11.1"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-gpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "0"
}, {
"fixed" : "2.11.1"
} ]
} ]
} ],
"references" : [ {
"type" : "WEB",
"url" : "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7jvm-xxmr-v5cw"
}, {
"type" : "ADVISORY",
"url" : "https://nvd.nist.gov/vuln/detail/CVE-2023-25662"
}, {
"type" : "WEB",
"url" : "https://github.com/tensorflow/tensorflow/commit/08b8e18643d6dcde00890733b270ff8d9960c56c"
}, {
"type" : "PACKAGE",
"url" : "https://github.com/tensorflow/tensorflow"
} ],
"database_specific" : {
"cwe_ids" : [ "CWE-190" ],
"severity" : "HIGH",
"github_reviewed" : true,
"github_reviewed_at" : "2023-03-24T21:58:31Z",
"nvd_published_at" : "2023-03-25T00:15:00Z"
}
}