CVE-2025-46656 is a vulnerability identified in the python-markdownify library, a Python package used to convert HTML content into Markdown format. The issue arises from improper validation of the specified quantity in input, specifically related to headline tags. The library, prior to version 0.14.1, accepts headline prefixes beyond the standard HTML range of <h1> through <h6>, allowing extremely large headline tags such as <h9999999>. This improper input validation leads to excessive memory consumption when processing such malformed or maliciously crafted HTML inputs. The vulnerability is classified under CWE-1284, which pertains to improper validation of specified quantities in input, causing resource exhaustion. The CVSS v3.1 score is 2.9, indicating a low severity level. The attack vector is local (AV:L), requiring high attack complexity (AC:H), no privileges (PR:N), and no user interaction (UI:N). The impact is limited to availability (A:L) with no confidentiality or integrity impact. There are no known exploits in the wild, and no patches have been linked yet. The vulnerability could be triggered by feeding the vulnerable library HTML content containing excessively large headline tags, causing the application to consume large amounts of memory, potentially leading to denial of service due to resource exhaustion. Since the vulnerability requires local access and high complexity to exploit, it is less likely to be exploited remotely or at scale. However, applications that use python-markdownify to process untrusted or user-supplied HTML content could be at risk if an attacker can influence the input.

For European organizations, the primary impact of this vulnerability is a potential denial of service (DoS) condition caused by resource exhaustion. Organizations that use python-markdownify in their web applications, content management systems, or data processing pipelines to convert HTML to Markdown may experience application crashes or degraded performance if maliciously crafted inputs with large headline tags are processed. This could disrupt services, especially in environments where user-generated content is processed automatically. However, the low CVSS score and the requirement for local access and high attack complexity reduce the likelihood of widespread impact. Confidentiality and integrity of data are not affected, limiting the scope to availability concerns. Organizations in sectors with high reliance on content processing, such as media, publishing, and software development, may be more sensitive to service disruptions. Additionally, if the vulnerable library is used in automated workflows or batch processing, the impact could extend to operational delays. Given the lack of known exploits in the wild, the immediate risk is low, but awareness and proactive mitigation are recommended to prevent future exploitation.

1. Upgrade python-markdownify to version 0.14.1 or later, where this vulnerability has been addressed. 2. Implement input validation and sanitization at the application level to reject or limit HTML headline tags beyond the standard <h1> to <h6> range before passing content to python-markdownify. 3. Employ resource usage monitoring and limits (e.g., memory and CPU quotas) on processes that use python-markdownify to prevent excessive resource consumption from malformed inputs. 4. Restrict the source of HTML content processed by python-markdownify to trusted inputs where possible, minimizing exposure to untrusted user data. 5. Conduct code reviews and security testing focused on input validation for all components that handle HTML to Markdown conversion. 6. Use application-layer firewalls or content security policies to detect and block suspicious payloads containing abnormal HTML tags. 7. Maintain an inventory of applications and services using python-markdownify to ensure timely updates and vulnerability management. These steps go beyond generic advice by emphasizing proactive input validation, resource control, and strict content source management tailored to the nature of this vulnerability.