CVE-2026-59938 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 declared image size values that are much too large compared to the actual data, causing large memory usage in pypdf image parsing. 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 classic resource exhaustion flaw that demonstrates the critical importance of input validation in document processing libraries. This issue affects versions prior to 6.14.0 and specifically targets the library's image parsing functionality where it fails to properly validate declared image dimensions against actual data size. The flaw allows malicious actors to craft PDF files containing metadata that specifies image dimensions far exceeding the actual binary data contained within those images, creating a scenario where the parser allocates excessive memory resources. Such a vulnerability falls under CWE-400 which categorizes resource exhaustion issues and aligns with ATT&CK technique T1499.200 focusing on resource consumption attacks through malformed input.
The technical execution of this attack involves constructing PDF files with deliberately inflated image size parameters in their metadata structures. When pypdf processes these documents, it attempts to allocate memory buffers based on the declared dimensions rather than the actual data contained within the image streams. This creates a discrepancy where memory allocation scales exponentially with the declared values rather than the actual storage requirements, potentially leading to system instability or denial of service conditions. The vulnerability is particularly concerning in environments where pypdf processes untrusted PDF content as part of automated workflows or user-uploaded documents.
The operational impact of this vulnerability extends beyond simple performance degradation to potential system compromise in high-availability environments. When exploited, the memory consumption can escalate rapidly during document parsing, potentially exhausting available system resources and causing application crashes or server unresponsiveness. This creates an avenue for attackers to perform denial-of-service attacks against systems relying on pypdf for PDF processing without requiring any special privileges or authentication. The issue is particularly relevant in web applications, document management systems, and automated processing pipelines where PDF files are routinely handled and validated.
Mitigation strategies for this vulnerability center around immediate version upgrading to 6.14.0 or later, which implements proper bounds checking and validation of image dimensions during parsing. Organizations should also implement additional defensive measures including memory limits on processing containers, input sanitization layers, and monitoring for unusual memory consumption patterns during PDF processing operations. The fix demonstrates the importance of robust input validation in document libraries and aligns with security best practices outlined in industry standards such as those promoted by the Open Web Application Security Project and NIST guidelines for secure coding practices in document processing applications.