CVE-2026-119206 in pypdf
Summary
by MITRE • 10/29/2026
pypdf is a free and open-source pure-python PDF library. Prior to 6.21.3, pypdf can take excessive time to retrieve page labels from a crafted PDF containing a flat page-label number tree with many ranges. The page_labels property resolves each page independently and repeatedly scans the same /Nums array from the beginning, causing work to grow with both the page count and the number of label ranges. This issue is fixed in version 6.21.3.
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Analysis
by VulDB Data Team • 10/29/2026
The vulnerability identified within the pypdf library represents a significant performance degradation flaw rooted in inefficient algorithmic design for processing PDF metadata structures. Specifically, this issue affects versions prior to 6.21.3 and involves the handling of page label definitions contained within crafted Portable Document Format files. The core technical deficiency lies in how the library resolves the page_labels property when encountering flat page-label number trees that contain a large quantity of ranges. Instead of employing an optimized lookup mechanism or caching strategies, the implementation iterates through each page individually to determine its corresponding label. This approach necessitates scanning the /Nums array from the beginning for every single page request, resulting in redundant computational operations that scale poorly with file complexity.
From a technical perspective, this behavior constitutes a classic example of inefficient resource utilization due to poor algorithmic complexity. The time required to process these labels grows quadratically or worse relative to both the total number of pages and the density of label ranges within the document structure. As the parser repeatedly traverses the same data segment without memoization or binary search optimization, it creates a substantial processing bottleneck. This inefficiency is not merely a minor delay but can lead to severe performance degradation where legitimate operations are delayed indefinitely due to excessive CPU consumption during metadata extraction tasks.
The operational impact of this vulnerability primarily manifests as a Denial of Service condition through resource exhaustion. An attacker could craft a malicious PDF file containing an excessively large number of page label ranges designed specifically to trigger this inefficient parsing path. When such a document is processed by an application utilizing the vulnerable version of pypdf, the system may experience prolonged latency or complete unresponsiveness while attempting to retrieve metadata. This can disrupt automated workflows that rely on timely processing of PDF documents, potentially causing service outages in environments where high-throughput document analysis is required. The lack of a timeout mechanism further exacerbates this risk, allowing the process to consume resources indefinitely until system limits are reached or manual intervention occurs.
This vulnerability aligns with CWE-400, which describes Uncontrolled Resource Consumption, as the application fails to limit the amount of computational effort expended during parsing operations. Furthermore, it relates to CWE-756, Missing Custom Error Handling, insofar as the library does not implement safeguards against pathological input structures that exploit algorithmic weaknesses. In terms of threat modeling and adversary behavior, this flaw supports techniques associated with ATT&CK T1496, Resource Hijacking, where an attacker uses a crafted file to consume system resources such as CPU or memory for disruptive purposes rather than direct data exfiltration or execution.
To mitigate the risks associated with this vulnerability, organizations must ensure that all instances of pypdf are upgraded to version 6.21.3 or later, which contains the necessary algorithmic improvements to handle page label resolution efficiently. For environments where immediate patching is not feasible, implementing input validation and size limits on incoming PDF files can help prevent the processing of excessively complex documents before they reach the vulnerable parser layer. Additionally, integrating timeout mechanisms for metadata extraction processes can limit the impact of such attacks by terminating prolonged operations that exceed expected execution thresholds. Regular security audits focusing on third-party library dependencies are essential to maintain resilience against these types of performance-based exploits in modern software ecosystems.