CVE-2026-97676 in Langflow OSS
Summary
by MITRE • 10/07/2026
IBM Langflow OSS 1.0.0 through 1.12.2 could allow a remote authenticated attacker to execute arbitrary code due to improper neutralization of special elements used in code, resulting in a sandbox escape.
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Analysis
by VulDB Data Team • 10/07/2026
The vulnerability identified within IBM Langflow versions 1.0.0 through 1.12.2 represents a critical security flaw rooted in the application's handling of user-supplied input during code execution processes. As an open-source framework designed for building and deploying large language model applications, Langflow relies heavily on dynamic code evaluation to facilitate rapid prototyping and workflow automation. The core issue lies in the improper neutralization of special elements used in code generation or interpretation modules. When a remote authenticated attacker interacts with these components, they can inject malicious payloads that bypass intended security boundaries. This failure to properly sanitize input allows for a sandbox escape, where the executed code operates outside the restricted environment typically enforced by the application's runtime context.
From a technical perspective, this flaw is classified under CWE-94 as Improper Control of Generation of Code (Code Injection). The vulnerability arises because the system fails to adequately validate or encode special characters and syntax that could alter the intended logic of the executed script. In many modern AI-driven development tools, user inputs are often directly interpolated into code strings before execution. If these inputs contain escape sequences, command injection patterns, or malicious function calls without rigorous sanitization, an attacker can manipulate the underlying interpreter to execute arbitrary commands on the host system. The authentication requirement indicates that while this is not a fully unauthenticated remote exploit, it significantly lowers the barrier for exploitation since many enterprise environments may have multiple users with varying levels of access who could be tricked into executing malicious workflows or components within their sessions.
The operational impact of this vulnerability is severe, as successful exploitation leads to arbitrary code execution on the server hosting Langflow. This compromises the confidentiality, integrity, and availability of the underlying infrastructure. An attacker gaining a foothold through sandbox escape can potentially access sensitive data processed by the LLM applications, such as proprietary algorithms, customer information, or internal network resources if lateral movement is possible. Furthermore, because Langflow is often used in development pipelines, compromising it could allow an attacker to inject malicious code into deployed AI agents, leading to broader supply chain risks and potential manipulation of automated decision-making processes downstream.
Mitigation strategies must focus on both immediate remediation and long-term architectural improvements. The primary defense is to upgrade IBM Langflow to a version later than 1.12.2 where the vendor has addressed these input validation flaws. In environments where upgrading is not immediately feasible, administrators should enforce strict network segmentation to limit access to the Langflow interface only from trusted IP ranges and authenticated users with minimal privileges. Additionally, implementing Web Application Firewalls (WAF) rules that detect common code injection patterns can provide a layer of defense against exploitation attempts. From a development standpoint, adopting secure coding practices such as using parameterized queries for any database interactions involving generated code, avoiding direct execution of user-supplied strings via eval or similar functions, and employing strict allow-listing for permitted inputs are essential steps to prevent future occurrences of this class of vulnerability aligned with ATT&CK techniques related to command and script interpretation.