CVE-2025-62708 is a vulnerability classified under CWE-409 (Improper Handling of Highly Compressed Data) affecting the py-pdf pypdf library, a pure Python PDF processing library widely used for reading and manipulating PDF files. The flaw exists in versions prior to 6.1.3 and arises when the library parses the content stream of a PDF page that uses the LZWDecode filter, a compression method for encoding data streams within PDFs. An attacker can craft a malicious PDF file with highly compressed data that, when processed by the vulnerable pypdf versions, triggers excessive memory consumption due to data amplification. This can lead to denial of service (DoS) conditions by exhausting system memory resources. The vulnerability is remotely exploitable without requiring any authentication or user interaction, as it only requires the vulnerable system to parse the crafted PDF. The CVSS 4.0 base score of 6.6 reflects a medium severity, considering the network attack vector, low attack complexity, no privileges or user interaction needed, and a high impact on availability. No known exploits have been reported in the wild as of the publication date (October 22, 2025). The issue was addressed and fixed in pypdf version 6.1.3 by improving the handling of LZWDecode streams to prevent excessive memory usage. Organizations that use pypdf for automated PDF processing, document management, or any service that parses untrusted PDFs are at risk if they run vulnerable versions.

For European organizations, the primary impact is the risk of denial of service due to resource exhaustion when processing maliciously crafted PDFs. This can disrupt business operations, especially in sectors relying heavily on automated document workflows, such as legal, financial, healthcare, and government services. Memory exhaustion can lead to application crashes or system instability, potentially affecting availability of critical services. Since the vulnerability can be triggered remotely without authentication, exposed services that accept PDF uploads or parse PDFs from untrusted sources are particularly vulnerable. The impact on confidentiality and integrity is minimal, as the vulnerability does not enable code execution or data manipulation directly. However, service disruption can indirectly affect business continuity and user trust. Organizations using pypdf embedded in larger software stacks or cloud services should assess their exposure and update accordingly to maintain service reliability.

The most effective mitigation is to upgrade all instances of pypdf to version 6.1.3 or later, where the vulnerability is fixed. Organizations should audit their software dependencies to identify any use of vulnerable pypdf versions, including indirect dependencies in third-party applications. Implement strict input validation and sandboxing for PDF processing components to limit resource consumption and isolate failures. Employ resource usage monitoring and limits (e.g., memory and CPU quotas) on PDF parsing processes to prevent system-wide impact from malicious files. Where possible, restrict PDF uploads or processing to trusted sources or apply pre-processing filters to detect anomalous compression patterns. Maintain updated threat intelligence feeds to monitor for any emerging exploits targeting this vulnerability. Finally, integrate vulnerability scanning into the software development lifecycle to catch outdated library versions proactively.