CVE-2026-108673 in JeecgBoot
Summary
by MITRE • 10/11/2026
JeecgBoot through 3.9.5 contains a missing authorization vulnerability in the AiragPromptsController exportXls handler that allows any authenticated user to export all AI prompts. Low-privileged attackers can request /airag/prompts/exportXls to download every user's prompts, including prompt content, model ids, and parameters, as an Excel workbook.
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Analysis
by VulDB Data Team • 10/11/2026
The vulnerability identified in JeecgBoot versions up to 3.9.5 represents a critical failure in access control mechanisms within the AiragPromptsController module. Specifically, the exportXls handler lacks proper authorization checks, allowing any authenticated user with low privileges to bypass intended restrictions and access sensitive data belonging to other users or system administrators. This flaw is categorized under CWE-284, which describes Improper Access Control, indicating that the application fails to enforce appropriate policies regarding who can perform specific actions on protected resources. The absence of these checks means that the security boundary between different user roles is effectively non-existent for this particular endpoint, undermining the principle of least privilege that should govern enterprise applications.
From a technical perspective, the vulnerability manifests when an attacker sends a GET or POST request to the /airag/prompts/exportXls path with valid authentication credentials but insufficient privileges according to standard role-based access control models. The server processes this request without verifying whether the requesting user has permission to export data for all prompts in the system rather than just those associated with their own account. Consequently, the application generates and returns an Excel workbook containing a comprehensive dump of AI prompt configurations. This includes not only the textual content of the prompts but also sensitive metadata such as model identifiers and specific parameters used by these artificial intelligence models. The lack of input validation or role verification at this stage allows for unrestricted data extraction through standard HTTP requests, requiring no complex exploitation techniques beyond basic web interaction capabilities.
The operational impact of this vulnerability is significant due to the nature of the exposed data. AI prompts often contain proprietary logic, business rules, and contextual instructions that define how an application interacts with large language models or other AI systems. Exposing these details can lead to intellectual property theft, as competitors could reverse-engineer unique prompt engineering strategies. Furthermore, knowledge of model IDs and parameters may facilitate further attacks, such as prompt injection attempts where attackers craft inputs designed to manipulate the AI into revealing restricted information or executing unintended actions. The exposure of internal system configurations also aids in reconnaissance efforts, allowing adversaries to map out the technology stack and identify potential weaknesses for subsequent exploitation phases. This aligns with ATT&CK technique T1005, which involves Data from Local System Retrieval, as well as potentially T1498, Network Denial of Service if the export function is abused to exhaust server resources through repeated large file generation requests.
Mitigation strategies must focus on implementing robust authorization checks at both the controller and service layers. Developers should ensure that every endpoint requiring access to user-specific or system-wide data verifies the requester's identity and permissions before processing the request. For this specific vulnerability, adding a check to confirm that the authenticated user has administrative privileges or explicit permission to export all prompts is essential. Additionally, implementing rate limiting on the exportXls endpoint can help mitigate potential denial-of-service attacks resulting from abuse of this functionality. Regular security audits and static application security testing should be employed to detect similar missing authorization flaws across other controllers in the JeecgBoot framework. Upgrading to a patched version that addresses these access control deficiencies is the most effective immediate remediation step for organizations relying on affected versions.