CVE-2026-3961 identifies a server-side request forgery vulnerability in the manga-image-translator software developed by zyddnys, specifically in versions beta-0.0 through beta-0.3. The vulnerability resides in the to_pil_image function located in the manga-image-translator-main/server/request_extraction.py file, part of the Translate Endpoints component. SSRF occurs when an attacker can manipulate server-side code to make arbitrary HTTP requests, potentially accessing internal resources or services that are otherwise inaccessible externally. This vulnerability can be triggered remotely without user interaction or elevated privileges, making it relatively easy to exploit. The CVSS 4.0 vector indicates network attack vector (AV:N), low attack complexity (AC:L), no privileges required (PR:L), no user interaction (UI:N), and partial impacts on confidentiality, integrity, and availability (VC:L, VI:L, VA:L). The project maintainers were notified early but have not yet issued a patch or response. While no exploits have been reported in the wild, the public disclosure increases the risk of exploitation attempts. The vulnerability could be leveraged to scan internal networks, access sensitive metadata services, or cause denial of service by abusing internal endpoints. Given the software's niche use in manga image translation, the attack surface is limited but significant for affected users.

The SSRF vulnerability in manga-image-translator can allow attackers to coerce the server into making unauthorized requests to internal or external systems. This can lead to information disclosure if internal services respond with sensitive data, potentially exposing internal network structure or credentials. Integrity could be compromised if the attacker uses SSRF to interact with internal APIs that modify data. Availability might be impacted if the attacker triggers resource exhaustion or denial of service by flooding internal services. Since the vulnerability requires no user interaction and low privileges, exploitation is relatively straightforward. Organizations using this software in production environments or integrating it into larger systems may face risks of lateral movement or data leakage. However, the overall impact is medium due to the limited scope of the software’s deployment and partial impact on CIA triad components. The absence of known exploits in the wild reduces immediate risk but does not eliminate it, especially after public disclosure.

To mitigate CVE-2026-3961, organizations should first isolate the manga-image-translator service within a segmented network environment to limit its ability to reach sensitive internal resources. Implement strict input validation and sanitization on all user-supplied data that influences server requests, particularly in the to_pil_image function or related endpoints. Employ network-level controls such as egress filtering and firewall rules to restrict outbound requests from the application server to only trusted destinations. Monitor logs for unusual outbound request patterns indicative of SSRF attempts. If possible, disable or limit the Translate Endpoints functionality until a patch is available. Engage with the vendor or community to track patch releases and apply updates promptly once available. Additionally, consider deploying web application firewalls (WAFs) with SSRF detection capabilities to provide an additional layer of defense. Conduct regular security assessments and penetration testing focused on SSRF vectors in the application.