GHSA-7PJR-2RGH-FC5G
Vulnerability from github – Published: 2024-05-14 20:17 – Updated: 2024-05-14 20:17
VLAI
Summary
Anonymous PrestaShop customer can download other customers' invoices
Details
Impact
Since PrestaShop 8.1.5, any invoice can be downloaded from front-office in anonymous mode, by supplying a random secure_key parameter in the url.
Patches
Patched in 8.1.6
Workarounds
Upgrade to 8.1.6
Thank you to Samuel Bodevin, who found this vulnerability and shared it with the PrestaShop team.
Severity
5.3 (Medium)
{
"affected": [
{
"package": {
"ecosystem": "Packagist",
"name": "prestashop/prestashop"
},
"ranges": [
{
"events": [
{
"introduced": "8.1.5"
},
{
"fixed": "8.1.6"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"8.1.5"
]
}
],
"aliases": [
"CVE-2024-34717"
],
"database_specific": {
"cwe_ids": [
"CWE-200"
],
"github_reviewed": true,
"github_reviewed_at": "2024-05-14T20:17:27Z",
"nvd_published_at": "2024-05-14T16:17:28Z",
"severity": "MODERATE"
},
"details": "### Impact\nSince PrestaShop 8.1.5, any invoice can be downloaded from front-office in anonymous mode, by supplying a random secure_key parameter in the url.\n\n### Patches\nPatched in 8.1.6\n\n### Workarounds\nUpgrade to 8.1.6\n\nThank you to Samuel Bodevin, who found this vulnerability and shared it with the PrestaShop team.\n",
"id": "GHSA-7pjr-2rgh-fc5g",
"modified": "2024-05-14T20:17:27Z",
"published": "2024-05-14T20:17:27Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/PrestaShop/PrestaShop/security/advisories/GHSA-7pjr-2rgh-fc5g"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-34717"
},
{
"type": "WEB",
"url": "https://github.com/PrestaShop/PrestaShop/commit/46b9a2b430dd2008ac061fbcbae9f7af55a7920a"
},
{
"type": "PACKAGE",
"url": "https://github.com/PrestaShop/PrestaShop"
},
{
"type": "WEB",
"url": "https://github.com/PrestaShop/PrestaShop/releases/tag/8.1.6"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N",
"type": "CVSS_V3"
}
],
"summary": "Anonymous PrestaShop customer can download other customers\u0027 invoices"
}
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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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