GSD-2020-15094
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
In Symfony before versions 4.4.13 and 5.1.5, the CachingHttpClient class from the HttpClient Symfony component relies on the HttpCache class to handle requests. HttpCache uses internal headers like X-Body-Eval and X-Body-File to control the restoration of cached responses. The class was initially written with surrogate caching and ESI support in mind (all HTTP calls come from a trusted backend in that scenario). But when used by CachingHttpClient and if an attacker can control the response for a request being made by the CachingHttpClient, remote code execution is possible. This has been fixed in versions 4.4.13 and 5.1.5.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2020-15094",
"description": "In Symfony before versions 4.4.13 and 5.1.5, the CachingHttpClient class from the HttpClient Symfony component relies on the HttpCache class to handle requests. HttpCache uses internal headers like X-Body-Eval and X-Body-File to control the restoration of cached responses. The class was initially written with surrogate caching and ESI support in mind (all HTTP calls come from a trusted backend in that scenario). But when used by CachingHttpClient and if an attacker can control the response for a request being made by the CachingHttpClient, remote code execution is possible. This has been fixed in versions 4.4.13 and 5.1.5.",
"id": "GSD-2020-15094"
},
"gsd": {
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"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
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},
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"id": "GSD-2020-15094",
"modified": "2023-12-13T01:21:44.020105Z",
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"cvssV3": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 8.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 5.9
}
},
"lastModifiedDate": "2023-01-24T02:07Z",
"publishedDate": "2020-09-02T18:15Z"
}
}
}
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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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