CVE-2025-27780 (GCVE-0-2025-27780)
Vulnerability from cvelistv5 – Published: 2025-03-19 20:16 – Updated: 2025-03-20 19:24
VLAI
EPSS
VEX
Title
Applio allows unsafe deserialization in model_information.py
Summary
Applio is a voice conversion tool. Versions 3.2.8-bugfix and prior are vulnerable to unsafe deserialization in model_information.py. `model_name` in model_information.py takes user-supplied input (e.g. a path to a model) and pass that value to the `run_model_information_script` and later to `model_information` function, which loads that model with `torch.load` in rvc/train/process/model_information.py (on line 16 in 3.2.8-bugfix), which is vulnerable to unsafe deserialization. The issue can lead to remote code execution. A patch is available in the `main` branch of the repository.
Severity
SSVC
Exploitation: none
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-03-20 19:24 UTC
CWE
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://securitylab.github.com/advisories/GHSL-20… | x_refsource_CONFIRM |
| https://github.com/IAHispano/Applio/commit/11d139… | x_refsource_MISC |
| https://github.com/IAHispano/Applio/blob/29b4a00e… | x_refsource_MISC |
| https://github.com/IAHispano/Applio/blob/29b4a00e… | x_refsource_MISC |
Impacted products
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"value": "Applio is a voice conversion tool. Versions 3.2.8-bugfix and prior are vulnerable to unsafe deserialization in model_information.py. `model_name` in model_information.py takes user-supplied input (e.g. a path to a model) and pass that value to the `run_model_information_script` and later to `model_information` function, which loads that model with `torch.load` in rvc/train/process/model_information.py (on line 16 in 3.2.8-bugfix), which is vulnerable to unsafe deserialization. The issue can lead to remote code execution. A patch is available in the `main` branch of the repository."
},
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"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 model_information.py. `model_name` en model_information.py toma la entrada proporcionada por el usuario (por ejemplo, la ruta a un modelo) y pasa ese valor a la funci\u00f3n `run_model_information_script` y, posteriormente, a la funci\u00f3n `model_information`, que carga dicho modelo con `torch.load` en rvc/train/process/model_information.py (en la l\u00ednea 16 de 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."
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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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