CVE-2026-103000 in pypdf
Summary
by MITRE • 10/01/2026
pypdf is a free and open-source pure-python PDF library. Prior to 6.19.0, a crafted PDF can provide unusually large alphabetical page-label values that cause pypdf/_page_labels.py to generate strings beyond a reasonable page-label length when an application retrieves document page labels, consuming excessive memory and potentially making the application unavailable. This issue is fixed in version 6.19.0.
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Analysis
by VulDB Data Team • 10/01/2026
The vulnerability identified within the pypdf library represents a significant resource exhaustion risk stemming from inadequate input validation of PDF metadata structures. Specifically, the flaw resides in the page label processing logic located in the _page_labels.py module. When an application utilizes this open-source Python library to parse documents containing crafted or maliciously constructed PDF files, it may encounter unusually large alphabetical values designated for page labels. These values are not subject to strict length constraints during the parsing phase, allowing attackers to inject data that forces the internal string generation mechanisms to allocate excessive amounts of memory. This behavior directly impacts the stability and availability of any application relying on pypdf for document analysis or rendering tasks.
From a technical perspective, this issue is classified as an inefficient resource allocation vulnerability where the software fails to enforce reasonable limits on input size before processing. The attacker crafts a PDF file with page label entries that contain excessively long alphabetical sequences. When the library attempts to process these labels, it generates strings of unreasonable length without checking against predefined thresholds or system memory constraints. This leads to rapid consumption of available RAM, potentially triggering out-of-memory errors or causing the operating system's kernel to terminate the application via an Out-Of-Memory killer mechanism. The result is a denial of service condition where legitimate users are unable to access the affected application due to resource saturation caused by malicious input data.
In terms of industry standard classifications, this vulnerability aligns with CWE-789: Memory Allocation with Excessive Size Value and CWE-400: Uncontrolled Resource Consumption. The attack vector leverages the trust placed in PDF metadata structures, exploiting the parser's lack of bounds checking on string lengths derived from document properties. This behavior is consistent with patterns observed in ATT&CK technique T1496: Resource Hijacking, where an adversary consumes system resources to degrade performance or cause a denial of service. The vulnerability highlights the critical importance of validating and sanitizing all inputs extracted from external file formats, particularly when those files are processed by libraries intended for general-purpose use across various applications with varying security postures.
The operational impact extends beyond simple application crashes. In server-side environments where pypdf is used to process uploaded documents, such as document management systems or email gateways processing attachments, this vulnerability can be exploited remotely without authentication if the library processes untrusted files directly. An attacker could send a malicious PDF attachment that triggers memory exhaustion upon parsing, effectively taking down the service and disrupting business operations. Furthermore, in client-side applications, repeated exposure to such crafted documents could degrade system performance over time or cause immediate crashes during document opening workflows. The lack of input validation on page label lengths serves as an entry point for resource-based attacks that are difficult to detect through traditional signature-based security controls because the behavior appears normal until memory limits are breached.
Mitigation strategies primarily involve upgrading to version 6.19.0 or later, where this issue has been resolved by implementing appropriate length checks and input sanitization within the page label processing logic. For organizations unable to immediately upgrade their dependencies, defensive coding practices should be adopted at the application layer. This includes wrapping pypdf operations in try-except blocks that catch memory-related exceptions and implement circuit breakers or timeouts to prevent prolonged resource consumption. Additionally, deploying web application firewalls with deep packet inspection capabilities capable of detecting anomalous PDF structures may provide a secondary layer of defense by blocking requests containing excessively large metadata fields before they reach the vulnerable library. Regular security audits of third-party dependencies and continuous monitoring for unusual memory usage patterns in production environments are also recommended to detect potential exploitation attempts early.