CVE-2026-113790 in pypdf
Summary
by MITRE • 10/21/2026
pypdf is a free and open-source pure-python PDF library. Prior to 6.21.0, an attacker can craft a PDF containing arrays or dictionaries with many elements, causing parsing to consume large amounts of memory. This issue is fixed in version 6.21.0.
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Analysis
by VulDB Data Team • 10/21/2026
The vulnerability identified in the pypdf library represents a significant resource exhaustion risk stemming from insufficient validation and size constraints on input data structures during PDF parsing operations. As an open-source Python library designed for reading, writing, and manipulating PDF documents, pypdf is frequently utilized by developers to automate document processing tasks such as text extraction, metadata modification, and page merging. The core issue lies in the parser's handling of complex internal objects within a maliciously crafted PDF file, specifically arrays or dictionaries that contain an excessive number of elements. When the library processes these structures without implementing strict limits on their size or depth, it attempts to allocate memory proportional to the sheer volume of items contained within them. This lack of boundary checking allows an attacker to construct a PDF document where nested arrays or dictionaries are populated with millions of entries, effectively turning a standard parsing operation into a denial-of-service event by consuming all available system resources.
From a technical perspective, this flaw is classified as a resource exhaustion vulnerability, aligning closely with CWE-400: Uncontrolled Resource Consumption. The underlying mechanism involves the recursive or iterative expansion of these data structures in memory without prior validation against expected maximum sizes for PDF objects. In many programming languages and libraries, parsing complex nested structures can lead to exponential memory growth if not carefully managed. By injecting a file containing arrays or dictionaries with an artificially inflated number of elements, an attacker forces the pypdf engine into a state where it continuously allocates heap memory until the process is terminated by the operating system due to out-of-memory conditions. This behavior does not involve code execution or arbitrary data leakage but rather focuses on disrupting service availability through computational and resource-based abuse.
The operational impact of this vulnerability is primarily centered around denial of service, which corresponds to the Impact category in the MITRE ATT&CK framework under Resource Hijacking. For applications that rely on pypdf to process user-uploaded PDF documents, such as document management systems, email gateways scanning attachments, or automated report generation tools, this flaw can lead to severe performance degradation or complete service unavailability. If a server processes multiple maliciously crafted files concurrently, the cumulative memory consumption could crash not only the specific worker thread handling the request but potentially affect other services running on the same host if resource isolation is not strictly enforced. This makes the vulnerability particularly dangerous in multi-tenant environments or cloud-native deployments where resource limits are critical for maintaining stability and service level agreements.
Mitigation strategies focus heavily on upgrading to version 6.21.0 of pypdf, which implements fixes that enforce reasonable limits on the size and complexity of parsed objects. Developers integrating this library into their applications should ensure that dependency management systems are configured to automatically pull these security patches. In addition to updating dependencies, organizations employing pypdf for processing untrusted input should implement defense-in-depth measures such as setting strict memory quotas for parsing processes using containerization or process isolation techniques like cgroups in Linux environments. Furthermore, validating the structural complexity of incoming PDFs before full parsing can provide an additional layer of protection against similar resource exhaustion attacks targeting other components within a software stack.