CVE-2026-102993 in pypdf
Summary
by MITRE • 09/30/2026
pypdf is a free and open-source pure-python PDF library. Prior to 6.17.0, a crafted PDF can provide unusually large Roman page-label values that cause pypdf/_page_labels.py to generate excessively large numeral strings when an application retrieves document page labels, consuming large amounts of memory and potentially making the application unavailable. This issue is fixed in version 6.17.0.
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Analysis
by VulDB Data Team • 09/30/2026
The vulnerability identified within the pypdf library represents a significant resource exhaustion risk stemming from improper validation of input data during PDF parsing operations. As an open-source Python library designed for manipulating and extracting content from Portable Document Format files, pypdf is frequently integrated into applications that process user-uploaded documents or automated document workflows. The specific flaw resides in the page label handling logic, particularly within the _page_labels.py module. When a maliciously crafted PDF file containing unusually large Roman numeral values for page labels is processed, the library attempts to convert these values into their corresponding Arabic numeral representations without enforcing reasonable bounds on the size of the resulting string. This lack of input validation allows an attacker to trigger a scenario where the application allocates excessive amounts of memory to store and process these excessively long numeral strings.
From a technical perspective, this issue is classified as a Denial of Service vulnerability caused by uncontrolled resource consumption. The core mechanism involves the recursive or iterative conversion logic used to translate Roman numerals into integers for internal page indexing. In standard PDF structures, page labels are typically short identifiers such as i, ii, iii, or A, B, C. However, an attacker can construct a PDF with a label sequence that forces the parser to generate a numeral string of impractical length, potentially reaching megabytes in size depending on the specific Roman numeral configuration provided. This behavior aligns closely with CWE-789, which describes Uncontrolled Memory Allocation, as well as CWE-400, concerning Uncontrolled Resource Consumption. The vulnerability exploits the assumption that input data will adhere to expected structural norms, failing to implement strict length limits or sanity checks before performing computationally expensive string manipulations.
The operational impact of this vulnerability is severe for any application relying on pypdf for document processing services. By triggering this memory exhaustion condition, an attacker can cause the hosting process to consume all available system RAM, leading to a crash of the Python interpreter or the entire service if not properly isolated. In cloud-native environments or shared hosting architectures, this could result in cascading failures affecting other tenants or services running on the same infrastructure. The availability impact is direct and immediate, as the application becomes unresponsive while attempting to allocate memory for the oversized string, effectively rendering the document processing capability unavailable until the process is restarted or killed by an external watchdog mechanism. This type of attack requires minimal effort from the adversary, who only needs to provide a single crafted PDF file through any interface that accepts uploads and subsequently parses them using pypdf.
Mitigation strategies primarily involve upgrading to version 6.17.0 of the pypdf library, which includes patches to enforce strict limits on page label sizes during parsing. For organizations unable to immediately upgrade their dependencies due to compatibility constraints or deployment cycles, several defensive measures can be implemented at the application layer. One effective approach is to wrap the PDF processing logic in a sandboxed environment with explicit memory and CPU usage quotas, such as using Python's resource module to set hard limits on virtual memory size before invoking pypdf functions. Additionally, implementing input validation checks that reject PDF files containing page labels exceeding a predefined character length threshold can prevent the trigger condition from being reached. It is also advisable to deploy web application firewalls or API gateways that inspect incoming file uploads for known malicious patterns and restrict the types of documents accepted by the service.
This vulnerability highlights the importance of defensive coding practices in libraries that handle untrusted binary data formats like PDFs, which are inherently complex and prone to parsing ambiguities. The incident serves as a reminder that even pure-Python implementations must rigorously validate input constraints to prevent resource exhaustion attacks. Security teams should ensure that their dependency management processes include regular audits for such vulnerabilities, particularly in libraries with high visibility and widespread adoption within the Python ecosystem. By maintaining up-to-date dependencies and implementing layered defense mechanisms including memory limits and input sanitization, organizations can significantly reduce their attack surface against this class of denial-of-service threats.