GSD-2022-21647
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
CodeIgniter is an open source PHP full-stack web framework. Deserialization of Untrusted Data was found in the `old()` function in CodeIgniter4. Remote attackers may inject auto-loadable arbitrary objects with this vulnerability, and possibly execute existing PHP code on the server. We are aware of a working exploit, which can lead to SQL injection. Users are advised to upgrade to v4.1.6 or later. Users unable to upgrade as advised to not use the `old()` function and form_helper nor `RedirectResponse::withInput()` and `redirect()->withInput()`.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-21647",
"description": "CodeIgniter is an open source PHP full-stack web framework. Deserialization of Untrusted Data was found in the `old()` function in CodeIgniter4. Remote attackers may inject auto-loadable arbitrary objects with this vulnerability, and possibly execute existing PHP code on the server. We are aware of a working exploit, which can lead to SQL injection. Users are advised to upgrade to v4.1.6 or later. Users unable to upgrade as advised to not use the `old()` function and form_helper nor `RedirectResponse::withInput()` and `redirect()-\u003ewithInput()`.",
"id": "GSD-2022-21647"
},
"gsd": {
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"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
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"id": "GSD-2022-21647",
"modified": "2023-12-13T01:19:14.610686Z",
"schema_version": "1.4.0"
}
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"affects": {
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"baseScore": 7.7,
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"affected_versions": "All versions starting from 4.0.0 before 4.1.6",
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"cwe_ids": [
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],
"date": "2022-01-20",
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"fixed_versions": [
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"identifier": "CVE-2022-21647",
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"GHSA-w6jr-wj64-mc9x"
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"not_impacted": "All versions before 4.0.0, all versions starting from 4.1.6",
"package_slug": "packagist/codeigniter4/framework",
"pubdate": "2022-01-04",
"solution": "Upgrade to version 4.1.6 or above.",
"title": "Deserialization of Untrusted Data",
"urls": [
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"https://github.com/codeigniter4/CodeIgniter4/commit/ce95ed5765256e2f09f3513e7d42790e0d6948f5",
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"impact": {
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"vectorString": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
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"lastModifiedDate": "2022-01-20T15:04Z",
"publishedDate": "2022-01-04T20:15Z"
}
}
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not 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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