CVE-2026-59937 in pypdf
Summary
by MITRE • 07/08/2026
pypdf is a free and open-source pure-python PDF library. Prior to 6.14.0, an attacker can craft a PDF with repeated malformed cross-reference streams that cause pypdf to spend long runtimes recovering broken cross-reference table entries. This issue is fixed in version 6.14.0.
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Analysis
by VulDB Data Team • 07/08/2026
The vulnerability in pypdf represents a significant performance degradation issue that manifests through malformed cross-reference stream handling. This flaw exists within the library's PDF parsing mechanism where it fails to properly validate or limit processing of repeated cross-reference entries, creating an opportunity for attackers to craft malicious PDF files that trigger excessive computational overhead during document parsing operations.
The technical implementation of this vulnerability stems from insufficient input validation and lack of resource limiting mechanisms within the cross-reference table recovery process. When pypdf encounters malformed cross-reference streams with repeated entries, it enters into a computationally expensive recovery loop where it attempts to reconstruct broken references without adequate bounds checking or timeout mechanisms. This behavior creates a denial-of-service condition where legitimate processing time becomes consumed by the recovery algorithm rather than normal document interpretation tasks.
This vulnerability directly maps to CWE-400 which addresses uncontrolled resource consumption, specifically targeting the computational resources of the parsing process. The operational impact extends beyond simple performance degradation to potentially enabling remote attackers to consume system resources indefinitely, making it particularly dangerous in server environments where multiple PDF documents might be processed simultaneously. Attackers can exploit this by crafting PDF files containing carefully constructed repeated cross-reference entries that cause the library to enter extended processing loops.
The fix implemented in version 6.14.0 addresses this issue through enhanced validation of cross-reference stream structures and introduction of resource limits during recovery operations. This mitigation strategy aligns with ATT&CK technique T1496 which covers resource exhaustion attacks, by implementing defensive measures that prevent unlimited computational resource consumption. The remediation approach demonstrates proper input sanitization practices that should be applied to all PDF parsing libraries handling potentially malformed content.
Organizations relying on pypdf for document processing should prioritize immediate upgrade to version 6.14.0 or later to eliminate this vulnerability. Additional mitigations include implementing rate limiting on PDF processing requests, monitoring for unusual processing times during document parsing, and considering alternative PDF libraries that demonstrate more robust handling of malformed input structures. The vulnerability underscores the importance of input validation and resource management in open-source libraries processing untrusted binary data formats such as PDF documents.
Security practitioners should also consider implementing automated scanning for vulnerable versions of pypdf within their environments, particularly in systems processing user-uploaded PDF files where malicious input is more likely to occur. The remediation process requires careful testing to ensure that legitimate PDF documents continue to process correctly while the vulnerability is eliminated. This case study demonstrates how seemingly minor parsing flaws in document libraries can create significant security implications when combined with improper resource management and input validation practices.