Raw vector
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:XSummary
CVE-2025-27779 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Applio Applio. Its CVSS base score is 8.9 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 45% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
Applio, an open-source voice conversion tool, is affected by unsafe deserialization in versions 3.2.8-bugfix and earlier. The flaw resides in model_blender.py, where torch.load is called on lines 20-21 without safeguards; user-controlled model paths supplied via voice_blender.py functions model_fusion_a and model_fusion_b are passed through run_model_blender_script directly into this deserialization routine, triggering CWE-502.
An unauthenticated remote attacker can supply a malicious model path over the network and achieve arbitrary code execution on the server, as the CVSS 8.9 vector indicates no authentication or user interaction is required and impacts confidentiality, integrity, and availability.
The referenced GitHub advisory and commit 11d1395 on the main branch describe a patch that addresses the unsafe torch.load calls; practitioners should update to a build containing this change or apply equivalent input validation and safe-loading practices before processing externally supplied models.
The EPSS score rose from a low baseline to a peak of 0.1463, indicating emerging exploitation interest after disclosure. The issue occurs in a machine-learning workflow that loads untrusted PyTorch artifacts, a pattern that has produced similar remote-code-execution flaws in other model-handling codebases.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-6791
Vulnerability Data
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…
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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.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
PR.PS-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Requiring vetted libraries, regular updates and SAST before release reduces the likelihood that deserialization logic will accept and act on attacker-controlled serialized objects.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.
Mandatory malware scanning of data received over networks or storage media intercepts malicious serialized payloads before they are deserialized by the target application.