CVE-2026-51869 in DB-GPT
Summary
by MITRE • 10/01/2026
DB-GPT v0.8.0 sandbox API silently falls back to LocalRuntime and executes code on host.
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Analysis
by VulDB Data Team • 10/01/2026
The vulnerability identified in DB-GPT version 0.8.0 represents a critical security flaw within the application's execution environment, specifically affecting how user-supplied Python code is processed through its sandboxing mechanisms. The core issue lies in the failure of the isolation layer to enforce strict boundaries between untrusted input and the host system's resources. When users submit code snippets for analysis or generation via the API, the intended behavior involves executing this code within a restricted containerized environment known as LocalRuntime. However, due to improper error handling and configuration validation logic, the application silently degrades its security posture by falling back to an unrestricted execution context on the host machine when specific conditions are met. This silent fallback occurs without notifying the user or logging sufficient diagnostic information that would alert administrators to the loss of isolation, effectively bypassing all intended sandbox protections.
From a technical perspective, this flaw constitutes a classic case of insecure default configuration combined with insufficient error handling during runtime initialization. The application likely attempts to instantiate a secure execution environment but encounters an exception related to container availability, resource constraints, or misconfigured paths. Instead of rejecting the request and returning a clear security error, the code catches the exception and proceeds to execute the payload using standard Python interpreters available on the host operating system. This behavior violates the principle of least privilege by granting untrusted user input full access to the underlying infrastructure's file system, network interfaces, and process space. The absence of explicit validation checks before falling back allows attackers who can submit arbitrary code through the API to achieve remote code execution with the privileges of the DB-GPT service account.
The operational impact of this vulnerability is severe, as it transforms a data processing tool into an entry point for full system compromise. An authenticated attacker or even an unauthenticated user if authentication is not strictly enforced on the sandbox endpoint can execute arbitrary commands, exfiltrate sensitive data stored in accessible directories, pivot to other internal systems via network requests initiated from within the compromised host, and potentially escalate privileges depending on the permissions of the service account running DB-GPT. This undermines the fundamental trust model of any AI-driven development platform that relies on code execution features for testing or debugging generated scripts. The silent nature of the fallback makes detection difficult through standard monitoring tools unless specific process creation logs are actively audited, as the activity appears to be a legitimate internal function rather than an external attack vector initially.
This vulnerability aligns with CWE-94 Improper Control of Generation of Code and CWE-200 Exposure of Sensitive Information to an Unauthorized Actor if sensitive data is accessed during execution. In terms of offensive security frameworks, it maps directly to MITRE ATT&CK technique T1059 Command and Scripting Interpreter, specifically the Python subcategory, as well as T1610 Deploy Capabilities which involves establishing a foothold for further exploitation. The lack of proper isolation also relates to CWE-284 Improper Access Control regarding the failure to restrict access to critical system resources within the execution context.
Mitigation strategies must focus on restoring strict enforcement of sandbox boundaries and improving visibility into runtime failures. Administrators should immediately upgrade DB-GPT to a patched version where this fallback logic has been removed or hardened to fail securely rather than failing open. If upgrading is not immediately feasible, it is critical to disable the code execution API endpoints entirely if they are not strictly required for business operations. Network segmentation should be implemented to isolate any systems running vulnerable versions of DB-GPT from sensitive internal networks and databases. Additionally, enabling comprehensive audit logging that captures exceptions during sandbox initialization will help detect attempts to trigger this fallback mechanism in real-time. Security teams must also review the permissions assigned to the user account under which the DB-GPT service runs, ensuring it operates with minimal privileges necessary for its function, thereby limiting the blast radius should an exploitation attempt succeed before a patch is applied.