FKIE_CVE-2025-27779
Vulnerability from fkie_nvd - Published: 2025-03-19 21:15 - Updated: 2026-06-17 09:04
Severity
Summary
Applio is a voice conversion tool. Versions 3.2.8-bugfix and prior are vulnerable to unsafe deserialization in `model_blender.py` lines 20 and 21. `model_fusion_a` and `model_fusion_b` from voice_blender.py take user-supplied input (e.g. a path to a model) and pass that value to the `run_model_blender_script` and later to `model_blender` function, which loads these two models with `torch.load` in `model_blender.py (on lines 20-21 in 3.2.8-bugfix), which is vulnerable to unsafe deserialization. The issue can lead to remote code execution. A patch is available on the `main` branch of the Applio repository.
References
{
"affected": [
{
"affectedData": [
{
"product": "Applio",
"vendor": "IAHispano",
"versions": [
{
"status": "affected",
"version": "\u003c= 3.2.8-bugfix"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:applio:applio:*:*:*:*:*:*:*:*",
"matchCriteriaId": "B27727D5-2D87-4D8E-B880-98924CFA32C5",
"versionEndIncluding": "3.2.8-bugfix",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Applio is a voice conversion tool. Versions 3.2.8-bugfix and prior are vulnerable to unsafe deserialization in `model_blender.py` lines 20 and 21. `model_fusion_a` and `model_fusion_b` from voice_blender.py take user-supplied input (e.g. a path to a model) and pass that value to the `run_model_blender_script` and later to `model_blender` function, which loads these two models with `torch.load` in `model_blender.py (on lines 20-21 in 3.2.8-bugfix), which is vulnerable to unsafe deserialization. The issue can lead to remote code execution. A patch is available on the `main` branch of the Applio repository."
},
{
"lang": "es",
"value": "Applio es una herramienta de conversi\u00f3n de voz. Las versiones 3.2.8-correcci\u00f3n de errores y anteriores son vulnerables a una deserializaci\u00f3n insegura en las l\u00edneas 20 y 21 de `model_blender.py`. `model_fusion_a` y `model_fusion_b` de `voice_blender.py` toman la entrada proporcionada por el usuario (por ejemplo, la ruta a un modelo) y pasan ese valor a la funci\u00f3n `run_model_blender_script` y, posteriormente, a la funci\u00f3n `model_blender`, que carga estos dos modelos con `torch.load` en `model_blender.py` (en las l\u00edneas 20-21 de la versi\u00f3n 3.2.8-correcci\u00f3n de errores), que es vulnerable a una deserializaci\u00f3n insegura. Este problema puede provocar la ejecuci\u00f3n remota de c\u00f3digo. Hay un parche disponible en la rama `main` del repositorio de Applio."
}
],
"id": "CVE-2025-27779",
"lastModified": "2026-06-17T09:04:12.630",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 9.8,
"baseSeverity": "CRITICAL",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 5.9,
"source": "nvd@nist.gov",
"type": "Primary"
}
],
"cvssMetricV40": [
{
"cvssData": {
"Automatable": "NOT_DEFINED",
"Recovery": "NOT_DEFINED",
"Safety": "NOT_DEFINED",
"attackComplexity": "LOW",
"attackRequirements": "NONE",
"attackVector": "NETWORK",
"availabilityRequirement": "NOT_DEFINED",
"baseScore": 8.9,
"baseSeverity": "HIGH",
"confidentialityRequirement": "NOT_DEFINED",
"exploitMaturity": "PROOF_OF_CONCEPT",
"integrityRequirement": "NOT_DEFINED",
"modifiedAttackComplexity": "NOT_DEFINED",
"modifiedAttackRequirements": "NOT_DEFINED",
"modifiedAttackVector": "NOT_DEFINED",
"modifiedPrivilegesRequired": "NOT_DEFINED",
"modifiedSubAvailabilityImpact": "NOT_DEFINED",
"modifiedSubConfidentialityImpact": "NOT_DEFINED",
"modifiedSubIntegrityImpact": "NOT_DEFINED",
"modifiedUserInteraction": "NOT_DEFINED",
"modifiedVulnAvailabilityImpact": "NOT_DEFINED",
"modifiedVulnConfidentialityImpact": "NOT_DEFINED",
"modifiedVulnIntegrityImpact": "NOT_DEFINED",
"privilegesRequired": "NONE",
"providerUrgency": "NOT_DEFINED",
"subAvailabilityImpact": "NONE",
"subConfidentialityImpact": "NONE",
"subIntegrityImpact": "NONE",
"userInteraction": "NONE",
"valueDensity": "NOT_DEFINED",
"vectorString": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:P/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
"version": "4.0",
"vulnAvailabilityImpact": "HIGH",
"vulnConfidentialityImpact": "HIGH",
"vulnIntegrityImpact": "HIGH",
"vulnerabilityResponseEffort": "NOT_DEFINED"
},
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2025-27779",
"options": [
{
"exploitation": "none"
},
{
"automatable": "yes"
},
{
"technicalImpact": "total"
}
],
"role": "CISA Coordinator",
"timestamp": "2025-03-20T19:12:30.963413Z",
"version": "2.0.3"
}
}
]
},
"published": "2025-03-19T21:15:39.850",
"references": [
{
"source": "security-advisories@github.com",
"tags": [
"Product"
],
"url": "https://github.com/IAHispano/Applio/blob/29b4a00e4be209f9aac51cd9ccffcc632dfb2973/rvc/train/process/model_blender.py#L20-L21"
},
{
"source": "security-advisories@github.com",
"tags": [
"Product"
],
"url": "https://github.com/IAHispano/Applio/blob/29b4a00e4be209f9aac51cd9ccffcc632dfb2973/tabs/voice_blender/voice_blender.py#L39-L56"
},
{
"source": "security-advisories@github.com",
"tags": [
"Patch"
],
"url": "https://github.com/IAHispano/Applio/commit/11d139508d615a6db4d48b76634a443c66170dda"
},
{
"source": "security-advisories@github.com",
"tags": [
"Vendor Advisory"
],
"url": "https://securitylab.github.com/advisories/GHSL-2024-341_GHSL-2024-353_Applio/"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Analyzed",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-502"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
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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
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- 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.
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- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
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- 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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