FKIE_CVE-2025-67485
Vulnerability from fkie_nvd - Published: 2025-12-10 01:15 - Updated: 2026-10-14 21:10
Severity
Summary
mad-proxy is a Python-based HTTP/HTTPS proxy server for detection and blocking of malicious web activity using custom security policies. Versions 0.3 and below allow attackers to bypass HTTP/HTTPS traffic interception rules, potentially exposing sensitive traffic. This issue does not have a fix at the time of publication.
References
{
"affected": [
{
"affectedData": [
{
"product": "mad-proxy",
"vendor": "machphy",
"versions": [
{
"status": "affected",
"version": "\u003c= 0.3"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:machphy:mad-proxy:*:*:*:*:*:python:*:*",
"matchCriteriaId": "494A26D7-AF8D-4549-8D5A-09C1E7113717",
"versionEndIncluding": "0.3",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "mad-proxy is a Python-based HTTP/HTTPS proxy server for detection and blocking of malicious web activity using custom security policies. Versions 0.3 and below allow attackers to bypass HTTP/HTTPS traffic interception rules, potentially exposing sensitive traffic. This issue does not have a fix at the time of publication."
},
{
"lang": "es",
"value": "mad-proxy es un servidor proxy HTTP/HTTPS basado en Python para la detecci\u00f3n y el bloqueo de actividad web maliciosa utilizando pol\u00edticas de seguridad personalizadas. Las versiones 0.3 e inferiores permiten a los atacantes eludir las reglas de intercepci\u00f3n de tr\u00e1fico HTTP/HTTPS, exponiendo potencialmente tr\u00e1fico sensible. Este problema no tiene una soluci\u00f3n en el momento de la publicaci\u00f3n."
}
],
"id": "CVE-2025-67485",
"lastModified": "2026-10-14T21:10:00.183",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 5.3,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 1.4,
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2025-67485",
"options": [
{
"exploitation": "none"
},
{
"automatable": "yes"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2025-12-10T15:36:17.171564Z",
"version": "2.0.3"
}
}
]
},
"published": "2025-12-10T01:15:52.137",
"references": [
{
"source": "security-advisories@github.com",
"tags": [
"Vendor Advisory"
],
"url": "https://github.com/machphy/mad-proxy/security/advisories/GHSA-wx63-35hw-2482"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Analyzed",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-693"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
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Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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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.