CVE-2024-0654 is a medium-severity vulnerability classified under CWE-502 (Deserialization of Untrusted Data) affecting DeepFaceLab, specifically its pretrained model DF.wf.288res.384.92.72.22. The vulnerability resides in an unspecified function within the mainscripts/Util.py file. The issue arises from unsafe deserialization practices, where manipulated input data can be deserialized, potentially leading to arbitrary code execution or other malicious behavior. Exploitation requires local access with at least low privileges (PR:L) and does not require user interaction (UI:N). The attack vector is local (AV:L), meaning an attacker must have some form of access to the system where DeepFaceLab is installed. The vulnerability impacts confidentiality, integrity, and availability to a limited extent, as indicated by the CVSS vector (C:L/I:L/A:L). Although the exploit has been publicly disclosed, there are no known exploits actively used in the wild at this time. No patches or fixes have been linked yet, which suggests that mitigation relies on secure handling of deserialization and restricting local access. DeepFaceLab is a popular deepfake creation tool used for facial manipulation in videos and images, often by researchers, hobbyists, and professionals in media production. The vulnerability could allow an attacker with local access to execute malicious payloads or manipulate the application’s behavior, potentially compromising sensitive data or system stability.

For European organizations, the impact of CVE-2024-0654 depends largely on the extent to which DeepFaceLab is used internally or by contractors. Organizations involved in media production, digital forensics, academic research, or AI development may use DeepFaceLab or similar tools. An attacker exploiting this vulnerability could gain unauthorized code execution capabilities on affected systems, leading to data leakage, tampering with media content, or disruption of workflows. This could damage intellectual property, violate data protection regulations such as GDPR, and harm organizational reputation. Since the attack requires local access, the risk is higher in environments with weak endpoint security, shared workstations, or insufficient user privilege separation. The vulnerability also poses a risk in hybrid or remote work scenarios where endpoint devices may be less controlled. Although no active exploits are reported, the public disclosure increases the risk of opportunistic attacks, especially in environments where pretrained models are shared or downloaded from untrusted sources.

1. Restrict local access to systems running DeepFaceLab to trusted users only, enforcing strict user privilege management and endpoint security controls. 2. Avoid running DeepFaceLab or loading pretrained models from untrusted or unknown sources to reduce the risk of maliciously crafted serialized data. 3. Implement application-level sandboxing or containerization to isolate DeepFaceLab processes and limit the impact of potential exploitation. 4. Monitor file integrity and system logs for unusual deserialization activities or unexpected process behaviors related to DeepFaceLab. 5. Encourage developers or users of DeepFaceLab to update to patched versions once available and to follow secure coding practices for deserialization, such as using safe serialization libraries or validating input data rigorously. 6. Educate users about the risks of loading external pretrained models and the importance of verifying their provenance. 7. Employ endpoint detection and response (EDR) solutions to detect anomalous local activities that could indicate exploitation attempts.