FKIE_CVE-2022-42965
Vulnerability from fkie_nvd - Published: 2022-11-09 20:15 - Updated: 2026-06-17 05:05
Severity
3.7 (Low) - CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:L
7.5 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
7.5 (High) - CVSS:3.1/
Summary
An exponential ReDoS (Regular Expression Denial of Service) can be triggered in the snowflake-connector-python PyPI package, when an attacker is able to supply arbitrary input to the undocumented get_file_transfer_type method
References
| URL | Tags | ||
|---|---|---|---|
| reefs@jfrog.com | https://research.jfrog.com/vulnerabilities/snowflake-connector-python-redos-xray-257185/ | Exploit, Third Party Advisory | |
| af854a3a-2127-422b-91ae-364da2661108 | https://research.jfrog.com/vulnerabilities/snowflake-connector-python-redos-xray-257185/ | Exploit, Third Party Advisory |
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| snowflake | snowflake_connector | * |
{
"affected": [
{
"affectedData": [
{
"product": "snowflake-connector-python",
"vendor": "snowflake-connector-python",
"versions": [
{
"lessThan": "2.8.2",
"status": "affected",
"version": "unspecified",
"versionType": "custom"
}
]
}
],
"source": "reefs@jfrog.com"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:snowflake:snowflake_connector:*:*:*:*:*:python:*:*",
"matchCriteriaId": "AC30A819-F232-471C-8988-C1D3A25B4500",
"versionEndExcluding": "2.8.2",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "An exponential ReDoS (Regular Expression Denial of Service) can be triggered in the snowflake-connector-python PyPI package, when an attacker is able to supply arbitrary input to the undocumented get_file_transfer_type method"
},
{
"lang": "es",
"value": "Se puede activar un ReDoS exponencial (Denegaci\u00f3n de Servicio de Expresi\u00f3n Regular) en el paquete PyPI snowflake-connector-python, cuando un atacante puede proporcionar entradas arbitrarias al m\u00e9todo get_file_transfer_type no documentado."
}
],
"id": "CVE-2022-42965",
"lastModified": "2026-06-17T05:05:40.890",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "HIGH",
"attackVector": "NETWORK",
"availabilityImpact": "LOW",
"baseScore": 3.7,
"baseSeverity": "LOW",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:L",
"version": "3.1"
},
"exploitabilityScore": 2.2,
"impactScore": 1.4,
"source": "reefs@jfrog.com",
"type": "Secondary"
},
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 3.6,
"source": "nvd@nist.gov",
"type": "Primary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2022-42965",
"options": [
{
"exploitation": "poc"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2025-05-01T19:19:00.841362Z",
"version": "2.0.3"
}
}
]
},
"published": "2022-11-09T20:15:10.820",
"references": [
{
"source": "reefs@jfrog.com",
"tags": [
"Exploit",
"Third Party Advisory"
],
"url": "https://research.jfrog.com/vulnerabilities/snowflake-connector-python-redos-xray-257185/"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Exploit",
"Third Party Advisory"
],
"url": "https://research.jfrog.com/vulnerabilities/snowflake-connector-python-redos-xray-257185/"
}
],
"sourceIdentifier": "reefs@jfrog.com",
"vulnStatus": "Analyzed",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-1333"
}
],
"source": "reefs@jfrog.com",
"type": "Secondary"
},
{
"description": [
{
"lang": "en",
"value": "CWE-1333"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
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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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