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."
This vulnerability carries a CRITICAL severity rating with a CVSS v3.1 score of 9.9, indicating it can be exploited remotely over the network with relatively low complexity without requiring user interaction requiring only low-level privileges . The vulnerability impacts confidentiality (data exposure), integrity (unauthorized modifications), and availability (service disruption) for affected systems. Impacting 1 product from lfprojects organizations running these solutions should prioritize assessment and patching.
Reported in 2023, this vulnerability emerged during an era marked by increased sophistication in supply chain attacks, cloud infrastructure vulnerabilities, and software-as-a-service (SaaS) security challenges. Security practices during this period emphasized zero-trust architectures, container security, and API protection.
2023-01-26T21:18:15.217
2024-11-21T07:46:32.833
Modified
CVSSv3.1: 9.9 (CRITICAL)
| Type | Vendor | Product | Version/Range | Vulnerable? |
|---|---|---|---|---|
| Application | lfprojects | modelina | < 1.0.0 | Yes |
SecUtils normalizes and enriches National Vulnerability Database (NVD) records by standardizing vendor and product identifiers, aggregating vulnerability metadata from both NVD and MITRE sources, and providing structured context for security teams. For lfprojects's affected products, we extract Common Platform Enumeration (CPE) data, Common Weakness Enumeration (CWE) classifications, CVSS severity metrics, and reference data to enable rapid vulnerability prioritization and asset correlation. This record contains no exploit code, proof-of-concept instructions, or attack methodologies—only defensive intelligence necessary for patch management, risk assessment, and security operations.