FKIE_CVE-2020-8164
Vulnerability from fkie_nvd - Published: 2020-06-19 17:15 - Updated: 2026-06-17 03:25
Severity
Summary
A deserialization of untrusted data vulnerability exists in rails < 5.2.4.3, rails < 6.0.3.1 which can allow an attacker to supply information can be inadvertently leaked fromStrong Parameters.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| rubyonrails | rails | * | |
| rubyonrails | rails | * | |
| debian | debian_linux | 8.0 | |
| debian | debian_linux | 9.0 | |
| debian | debian_linux | 10.0 | |
| opensuse | backports_sle | 15.0 | |
| opensuse | leap | 15.1 | |
| opensuse | leap | 15.2 |
{
"affected": [
{
"affectedData": [
{
"product": "https://github.com/rails/rails",
"vendor": "n/a",
"versions": [
{
"status": "affected",
"version": "5.2.4.3, 6.0.3.1"
}
]
}
],
"source": "support@hackerone.com"
}
],
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},
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"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "A deserialization of untrusted data vulnerability exists in rails \u003c 5.2.4.3, rails \u003c 6.0.3.1 which can allow an attacker to supply information can be inadvertently leaked fromStrong Parameters."
},
{
"lang": "es",
"value": "Se presenta una vulnerabilidad de deserializaci\u00f3n de datos no confiables en rails versiones anteriores a 5.2.4.3, rails versiones anteriores a 6.0.3.1, que pueden permitir a un atacante suministrar informaci\u00f3n en la que pueden ser filtrados inadvertidamente par\u00e1metros fromStrong"
}
],
"id": "CVE-2020-8164",
"lastModified": "2026-06-17T03:25:58.280",
"metrics": {
"cvssMetricV2": [
{
"acInsufInfo": false,
"baseSeverity": "MEDIUM",
"cvssData": {
"accessComplexity": "LOW",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "NONE",
"baseScore": 5.0,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:L/Au:N/C:P/I:N/A:N",
"version": "2.0"
},
"exploitabilityScore": 10.0,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"source": "nvd@nist.gov",
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"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N",
"version": "3.1"
},
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"impactScore": 3.6,
"source": "nvd@nist.gov",
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}
]
},
"published": "2020-06-19T17:15:18.677",
"references": [
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},
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"sourceIdentifier": "support@hackerone.com",
"vulnStatus": "Modified",
"weaknesses": [
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"type": "Secondary"
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]
}
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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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