CVE-2026-51866 in DB-GPT
Summary
by MITRE • 10/01/2026
In DB-GPT 0.7.5 and 0.8.0, a skill uploaded through the real /api/v1/skills/upload route can later be executed through the real /api/v1/chat/react-agent flow.
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Analysis
by VulDB Data Team • 10/01/2026
The vulnerability in DB-GPT versions 0.7.5 and 0.8.0 represents a critical security flaw rooted in insufficient validation of uploaded artifacts within an AI-driven application framework. The core issue lies in the disconnect between the skill upload mechanism and its subsequent execution context. When a user interacts with the platform via the /api/v1/skills/upload endpoint, they are permitted to submit custom skills which are essentially code modules or scripts designed to extend the functionality of the system. These uploads are processed and stored without adequate sanitization or integrity verification mechanisms that would typically be present in secure software development lifecycles. The severity of this flaw is amplified by its direct integration with the /api/v1/chat/react-agent flow, which serves as the primary interface for executing user requests through an agent-based architecture.
From a technical perspective, the vulnerability allows for arbitrary code execution or injection attacks because the uploaded skill files are not sufficiently isolated from the runtime environment of the chat agent. When a user subsequently invokes the react-agent endpoint to process a query that triggers one of these previously uploaded skills, the system executes the unverified code with the privileges associated with the application server. This bypasses standard security controls such as input validation and output encoding because the execution path is internal rather than external in the traditional sense. The attacker does not need to inject malicious payloads directly into chat messages but instead leverages the legitimate administrative or user-facing upload feature to plant executable code that runs automatically when specific conditions are met within the agent's reasoning loop.
The operational impact of this vulnerability is severe, potentially leading to full system compromise depending on the deployment context and permissions assigned to the DB-GPT service account. An authenticated attacker can exploit this flaw to execute arbitrary commands on the host machine, exfiltrate sensitive data processed by the AI models, or pivot further into internal networks if the server has broader connectivity. Since DB-GPT is often deployed in enterprise environments handling proprietary business intelligence and database queries, the exposure of underlying infrastructure details could lead to significant intellectual property theft and regulatory non-compliance. The ability to chain this upload vulnerability with agent execution creates a reliable attack vector that does not rely on social engineering or complex exploitation techniques beyond basic API interaction.
This flaw aligns closely with CWE-94 Improper Control of Generation of Code, specifically code injection vulnerabilities where user input is used in the creation of executable code without proper validation. It also maps to MITRE ATT&CK technique T1059 Command and Scripting Interpreter, as it enables an attacker to execute system-level commands through a legitimate application feature. Furthermore, the scenario reflects aspects of CWE-78 Improper Neutralization of Special Elements used in an OS Command, particularly when the uploaded skills interact with underlying operating systems or database engines via shell-like operations facilitated by the agent framework.
Mitigation strategies must focus on implementing strict sandboxing for all user-uploaded code and enforcing rigorous input validation at both the upload and execution stages. Developers should ensure that skill files are parsed and analyzed statically before being stored, rejecting any content that contains dangerous function calls or system-level interactions unless explicitly whitelisted by administrators. Additionally, running the agent execution environment with minimal privileges using containerization technologies like Docker can limit the blast radius of such an exploit. Implementing a allow-list approach for permitted skill libraries and requiring explicit user confirmation before executing non-standard code paths would further reduce risk. Regular security audits of API endpoints and continuous monitoring of unusual execution patterns in the chat logs are essential defensive measures until patched versions addressing these validation gaps are deployed.