CVE-2026-26020 in AutoGPT
Summary
by MITRE • 02/12/2026
AutoGPT is a platform that allows users to create, deploy, and manage continuous artificial intelligence agents that automate complex workflows. Prior to 0.6.48, an authenticated user could achieve Remote Code Execution (RCE) on the backend server by embedding a disabled block inside a graph. The BlockInstallationBlock — a development tool capable of writing and importing arbitrary Python code — was marked disabled=True, but graph validation did not enforce this flag. This allowed any authenticated user to bypass the restriction by including the block as a node in a graph, rather than calling the block's execution endpoint directly (which did enforce the flag). This vulnerability is fixed in 0.6.48.
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Analysis
by VulDB Data Team • 02/18/2026
The vulnerability described in CVE-2026-26020 represents a critical security flaw in the AutoGPT platform that enables authenticated users to achieve remote code execution on backend servers. This issue stems from a fundamental design oversight in the platform's validation mechanisms, specifically concerning how disabled blocks are handled within graph structures. The AutoGPT platform is designed to facilitate the creation and deployment of AI agents that automate complex workflows, making it a sophisticated environment where such vulnerabilities could have severe operational consequences.
The technical flaw manifests through the BlockInstallationBlock, which serves as a development tool capable of writing and importing arbitrary Python code. This block was intentionally marked with disabled=True to prevent unauthorized code execution, yet the graph validation system failed to enforce this security restriction. The vulnerability occurs because the platform's validation logic does not properly check the disabled status of blocks when they are embedded within graph structures, allowing attackers to bypass the intended security controls. This represents a classic case of inadequate input validation and privilege escalation, where the system's trust model is violated through improper enforcement of security flags.
The operational impact of this vulnerability is substantial, as it transforms what should be a restricted development tool into a potential weapon for remote code execution. An authenticated user can leverage this flaw by simply embedding the disabled block within a graph structure, thereby circumventing the direct execution endpoint that properly enforces the disabled flag. This approach effectively creates a backdoor that allows attackers to execute arbitrary code on the backend server, potentially leading to complete system compromise, data exfiltration, and further lateral movement within the network. The vulnerability affects all versions prior to 0.6.48, indicating that a significant portion of the user base was exposed to this risk.
This vulnerability aligns with several cybersecurity standards and frameworks, particularly CWE-862 which addresses insufficient authorization and CWE-20 which covers improper input validation. The attack pattern follows the techniques described in the MITRE ATT&CK framework under T1059 for command and scripting interpreter and T1078 for valid accounts, as it leverages authenticated access to escalate privileges. The exploitation demonstrates a sophisticated understanding of how application-level security controls can be bypassed through creative manipulation of system interfaces. The fix implemented in version 0.6.48 addresses the core validation issue by ensuring that disabled blocks are properly enforced regardless of their inclusion method within graph structures.
Organizations using AutoGPT platforms should immediately upgrade to version 0.6.48 or later to remediate this vulnerability. Additional mitigations include implementing network segmentation, monitoring for unusual graph creation patterns, and conducting regular security audits of automated workflows. The vulnerability serves as a reminder of the critical importance of comprehensive input validation and the need to enforce security controls consistently across all application interfaces, particularly in systems that handle code execution capabilities. Security teams should also consider implementing runtime application self-protection mechanisms and anomaly detection systems to identify potential exploitation attempts of similar vulnerabilities.