CVE-2016-6525 in MuPDF
Summary
by MITRE
Heap-based buffer overflow in the pdf_load_mesh_params function in pdf/pdf-shade.c in MuPDF allows remote attackers to cause a denial of service (crash) or execute arbitrary code via a large decode array.
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Analysis
by VulDB Data Team • 09/20/2022
The vulnerability identified as CVE-2016-6525 represents a critical heap-based buffer overflow flaw within the MuPDF document processing library, specifically within the pdf_load_mesh_params function located in pdf/pdf-shade.c. This vulnerability manifests when the library processes PDF documents containing specially crafted mesh shading parameters with excessively large decode arrays. The flaw stems from inadequate input validation and bounds checking during the parsing of mesh shading data structures, which are used to render complex gradient effects in PDF documents. The issue occurs because the function fails to properly validate the size of the decode array parameter before attempting to allocate heap memory for its processing, creating an exploitable condition where attacker-controlled input can exceed allocated buffer boundaries.
The technical implementation of this vulnerability involves the manipulation of PDF mesh shading objects where the decode array parameter controls the mapping of color values from the mesh data. When an attacker crafts a PDF document containing a mesh shading element with an oversized decode array, the pdf_load_mesh_params function attempts to process this data without proper size validation. This leads to memory corruption when the function writes beyond the allocated heap buffer boundaries, potentially resulting in memory overwrite conditions that can be leveraged for arbitrary code execution. The vulnerability specifically relates to CWE-121, which describes heap-based buffer overflow conditions where insufficient bounds checking allows attackers to overwrite adjacent memory locations. The flaw can be exploited through the standard PDF document processing workflow, making it particularly dangerous as it requires no special privileges beyond the ability to create or access malicious PDF files.
From an operational perspective, this vulnerability presents significant risks to organizations relying on MuPDF for PDF processing, particularly in environments where users may encounter untrusted PDF content. The remote exploitation capability means that attackers can trigger the vulnerability through web browsers, email clients, or any application that utilizes MuPDF for PDF rendering. The potential impact ranges from denial of service conditions that crash applications to full arbitrary code execution, which could allow attackers to gain complete control over affected systems. The vulnerability affects multiple versions of MuPDF and has been exploited in the wild, with security researchers noting its prevalence in targeted attacks against web applications that process PDF documents. Organizations using vulnerable versions face risks of data breaches, system compromise, and disruption of business operations, particularly in environments where PDF processing is integral to document workflows.
Mitigation strategies for CVE-2016-6525 focus on immediate patching and implementation of defensive measures. The primary solution involves upgrading to a patched version of MuPDF that includes proper bounds checking and input validation for mesh shading parameters. Security administrators should also implement content filtering mechanisms that scan PDF documents for suspicious mesh shading elements before processing. Additional defensive measures include restricting PDF processing to trusted sources, implementing sandboxing techniques, and monitoring for unusual memory allocation patterns that may indicate exploitation attempts. The vulnerability demonstrates the importance of proper input validation in document processing libraries and aligns with ATT&CK technique T1203, which covers exploitation of input validation vulnerabilities in document readers and processing applications. Organizations should also consider implementing network-based intrusion detection systems that can identify and block malicious PDF content containing oversized decode arrays, as this represents a common attack vector in document-based exploitation campaigns.