CVE-2023-23619 in Modelina
Summary
by MITRE • 01/26/2023
Modelina is a library for generating data models based on inputs such as AsyncAPI, OpenAPI, or JSON Schema documents. Versions prior to 1.0.0 are vulnerable to Code injection. This issue affects anyone who is using the default presets and/or does not handle the functionality themself. This issue has been partially mitigated in version 1.0.0, with the maintainer's GitHub Security Advisory (GHSA) noting "It is impossible to fully guard against this, because users have access to the original raw information. However, as of version 1, if you only access the constrained models, you will not encounter this issue. Further similar situations are NOT seen as a security issue, but intended behavior." The suggested workaround from the maintainers is "Fully custom presets that change the entire rendering process which can then escape the user input."
If you want to get best quality of vulnerability data, you may have to visit VulDB.
Analysis
by VulDB Data Team • 02/19/2023
CVE-2023-23619 represents a code injection vulnerability in Modelina, a library designed to generate data models from various schema formats including AsyncAPI, OpenAPI, and JSON Schema. This vulnerability stems from insufficient input validation and sanitization within the library's default presets, creating a pathway for malicious code execution when processing untrusted schema inputs. The flaw exists at the intersection of data model generation and code rendering processes, where user-provided schema information can be inadvertently executed as code during the model generation phase. The vulnerability is classified under CWE-94, which encompasses "Improper Control of Generation of Code" and falls within the broader category of code injection attacks that have been extensively documented in cybersecurity frameworks.
The technical implementation of this vulnerability occurs when Modelina processes schema documents through its default rendering presets without adequate sanitization of user inputs. Attackers can craft malicious schema content that, when processed by the library, results in the execution of unintended code within the context of the application using Modelina. This issue is particularly concerning because it affects the library's default functionality, meaning that developers who rely on standard presets without implementing additional security measures are automatically exposed to this risk. The vulnerability's impact extends beyond simple code execution, as it can potentially enable attackers to perform arbitrary code injection attacks that could compromise the entire application environment.
From an operational perspective, this vulnerability creates significant security risks for applications that depend on Modelina for automated data model generation. The attack surface is broad since any application using the library with default settings becomes vulnerable, regardless of the application's own security posture. The partial mitigation implemented in version 1.0.0 addresses the immediate threat by introducing constrained model access, but the maintainers explicitly note that complete protection is impossible due to the fundamental nature of the library's purpose. This vulnerability aligns with ATT&CK technique T1059, "Command and Scripting Interpreter," and demonstrates how legitimate library functionality can be weaponized for malicious purposes when proper input validation is absent. Organizations using Modelina must carefully evaluate their threat model and consider the implications of relying on default presets without implementing additional security controls.
The suggested mitigation strategies from the maintainers emphasize the importance of customizing the rendering process through fully customized presets that can properly escape user input. This approach aligns with security best practices outlined in the OWASP Top Ten, particularly the principle of input validation and sanitization. The maintainers' acknowledgment that similar situations are not considered security issues but intended behavior reflects the inherent challenge of balancing library functionality with security considerations. This vulnerability serves as a reminder that code generation libraries, while powerful tools for automation, require careful security consideration when processing untrusted inputs. The issue demonstrates how even well-intentioned library features can create security vulnerabilities when they enable dynamic code generation without proper safeguards, making it essential for developers to understand the security implications of the tools they integrate into their applications. Organizations should implement comprehensive testing procedures and consider alternative approaches for processing untrusted schema data to prevent exploitation of this vulnerability.