CVE-2026-84311 in pypdf
Summary
by MITRE • 09/01/2026
pypdf is a free and open-source pure-python PDF library. Prior to 6.16.1, an attacker can craft a PDF that causes pypdf/_page.py PageObject._extract_text and PageObject.extract_xform_text to traverse a directed acyclic graph of reused form XObjects in which each form invokes a child multiple times, creating exponentially many traversal paths and causing long runtimes and large memory consumption. This issue is fixed in version 6.16.1.
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Analysis
by VulDB Data Team • 09/01/2026
The vulnerability identified in the pypdf library prior to version 6.16.1 represents a significant resource exhaustion risk stemming from inefficient handling of complex PDF structures, specifically involving reused form XObjects within page content streams. As an open-source pure-Python library designed for parsing and manipulating Portable Document Format files, pypdf is frequently utilized in automated document processing pipelines where untrusted or semi-trusted documents are ingested without rigorous pre-validation. The core technical flaw resides in the implementation of text extraction methods, specifically PageObject._extract_text and PageObject.extract_xform_text, which fail to adequately track visited nodes when traversing a directed acyclic graph formed by nested form XObjects. In valid PDF specifications, it is permissible for a single form object to be referenced multiple times across different parts of a document or within its own structure through recursive inclusion mechanisms. However, the vulnerable implementation treats each reference as a distinct traversal path rather than recognizing that the underlying content has already been processed.
This architectural oversight leads to an exponential explosion in computational complexity when processing crafted PDFs where form objects invoke child elements multiple times. Instead of caching results or maintaining a set of visited nodes to prevent redundant processing, the library recursively processes each invocation independently. Consequently, even moderately complex documents with deep nesting and high fan-out ratios can trigger runtimes that scale exponentially relative to the depth and breadth of the graph structure. This behavior effectively transforms what should be a linear or polynomial-time operation into one that consumes disproportionate amounts of CPU cycles and memory resources. The operational impact is severe for any service relying on pypdf for document analysis, as it opens the door to Denial-of-Service attacks where an attacker can craft malicious PDFs designed specifically to hang processing threads or exhaust server memory pools by triggering these infinite-like recursive loops within a finite but large graph structure.
From a classification perspective, this vulnerability aligns with CWE-400, which covers Uncontrolled Resource Consumption, and more specifically CWE-675, Operations on Iterative Inputs with Unrestricted Number of Repetitions. The attack vector leverages the parser's inability to detect redundant processing paths within recursive data structures, allowing an external actor to manipulate input complexity to degrade system availability. In terms of MITRE ATT&CK mapping, this behavior is consistent with techniques found under T1496 Resource Hijacking, where attackers consume computational resources to disrupt service or potentially mine cryptocurrency if the environment allows for such exploitation at scale. The lack of depth limits or memoization in the text extraction logic means that standard defensive programming practices regarding recursive graph traversal were not applied, leaving systems vulnerable to crafted inputs designed to maximize recursion depth and branching factors simultaneously.
Mitigation strategies primarily involve upgrading to pypdf version 6.16.1 or later, where this issue has been resolved through improved tracking of visited form objects during the extraction process. For environments unable to immediately upgrade, implementing a wrapper around the text extraction functions that enforces strict timeouts and memory limits can provide temporary protection against resource exhaustion attacks. Additionally, organizations processing untrusted PDFs should consider deploying sandboxed execution environments with hardened resource quotas for document parsing tasks. It is also advisable to integrate static analysis tools or specialized PDF sanitizers into ingestion pipelines to detect anomalous structural complexity before it reaches the vulnerable parser logic. By treating all incoming documents as potentially hostile and applying strict constraints on processing depth and iteration counts, security teams can significantly reduce the attack surface associated with complex document parsing libraries like pypdf.