GSD-2022-45908
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
In PaddlePaddle before 2.4, paddle.audio.functional.get_window is vulnerable to code injection because it calls eval on a user-supplied winstr. This may lead to arbitrary code execution.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-45908",
"description": "In PaddlePaddle before 2.4, paddle.audio.functional.get_window is vulnerable to code injection because it calls eval on a user-supplied winstr. This may lead to arbitrary code execution.",
"id": "GSD-2022-45908"
},
"gsd": {
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"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
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"id": "GSD-2022-45908",
"modified": "2023-12-13T01:19:24.385982Z",
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"cwe_ids": [
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"description": "In PaddlePaddle before 2.4, paddle.audio.functional.get_window is vulnerable to code injection because it calls eval on a user-supplied winstr. This may lead to arbitrary code execution.",
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"not_impacted": "All versions starting from 2.4",
"package_slug": "pypi/paddlepaddle",
"pubdate": "2022-11-26",
"solution": "Upgrade to version 2.4.0 or above.",
"title": "Improper Control of Generation of Code (\u0027Code Injection\u0027)",
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"baseScore": 9.8,
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"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
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"exploitabilityScore": 3.9,
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},
"lastModifiedDate": "2022-12-01T17:59Z",
"publishedDate": "2022-11-26T02:15Z"
}
}
}
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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
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- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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