CVE-2026-50756 in next-ai-draw-io
Summary
by MITRE • 07/21/2026
An issue in DayuanJiang next-ai-draw-io 0.4.13 allows a remote attacker to obtain sensitive information via the x-ai-provider component
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Analysis
by VulDB Data Team • 07/21/2026
The vulnerability identified in DayuanJiang next-ai-draw-io version 0.4.13 represents a critical information disclosure flaw within the x-ai-provider component that enables remote attackers to access sensitive data. This issue stems from inadequate input validation and improper access controls within the ai provider module, which processes requests from external clients without sufficient sanitization measures. The vulnerability manifests when the system fails to properly validate or sanitize user-provided parameters before processing them through the ai drawing functionality, creating an opportunity for unauthorized data retrieval.
The technical implementation of this flaw involves the x-ai-provider component failing to enforce proper authentication and authorization checks when handling incoming requests. Attackers can exploit this weakness by crafting malicious requests that bypass normal access controls, potentially exposing configuration files, API keys, database connection details, or other sensitive system information. The vulnerability is particularly concerning because it operates at the application layer, allowing remote exploitation without requiring local system access or elevated privileges. This type of flaw aligns with CWE-200, which addresses improper exposure of sensitive information, and represents a classic example of insufficient input validation that can lead to data leakage.
The operational impact of this vulnerability extends beyond simple information disclosure, as the exposed sensitive data could enable attackers to escalate their privileges or launch subsequent attacks. The x-ai-provider component likely handles authentication tokens, system credentials, or other confidential parameters that when disclosed could provide attackers with unauthorized access to backend systems or services. Organizations using this software may face regulatory compliance violations, reputational damage, and potential financial losses if the exposed information includes customer data, proprietary algorithms, or infrastructure credentials. The vulnerability's remote exploitability means that attackers can target systems from anywhere on the internet without requiring physical access or network proximity.
Mitigation strategies should focus on implementing robust input validation mechanisms within the x-ai-provider component to ensure all external inputs are properly sanitized before processing. Organizations should enforce strict authentication and authorization checks for all ai provider endpoints, implementing principle of least privilege access controls that limit data exposure based on user roles and permissions. Regular security audits and code reviews specifically targeting the ai drawing module would help identify similar vulnerabilities in other components. Additionally, network segmentation and firewall rules should be implemented to restrict access to the x-ai-provider component, while logging and monitoring systems should be enhanced to detect anomalous access patterns that may indicate exploitation attempts. The solution aligns with ATT&CK technique T1566 which covers credential access through information discovery and data manipulation attacks.
Security patches should address the root cause by implementing proper parameter validation, input sanitization, and access control enforcement within the x-ai-provider module. Organizations should conduct thorough vulnerability assessments to identify if similar issues exist in other components of the next-ai-draw-io application or related systems. The remediation process must include updating the software to a patched version that properly validates all user inputs and implements comprehensive access controls for sensitive data exposure prevention. Continuous monitoring and regular security updates remain essential to protect against evolving threats targeting ai-enabled applications and their underlying infrastructure components.