CVE-2018-25017 in RawSpeed
Summary
by MITRE • 07/01/2021
RawSpeed (aka librawspeed) 3.1 has a heap-based buffer overflow in TableLookUp::setTable.
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Analysis
by VulDB Data Team • 07/04/2021
The vulnerability CVE-2018-25017 affects RawSpeed library version 3.1, specifically within the TableLookUp::setTable function where a heap-based buffer overflow occurs. This issue arises when processing raw image data files that contain malformed or maliciously crafted lookup tables. The vulnerability represents a critical security flaw that can potentially lead to arbitrary code execution when the affected library processes untrusted input files. RawSpeed is a widely used library for decoding raw image formats from digital cameras, making this vulnerability particularly concerning for applications that handle image processing workflows.
The technical root cause of this heap-based buffer overflow stems from insufficient bounds checking within the TableLookUp::setTable method. When the library attempts to process lookup table data, it fails to validate the size of incoming data against the allocated buffer space, allowing an attacker to write beyond the intended memory boundaries. This flaw falls under the CWE-121 heap-based buffer overflow category, which is classified as a critical weakness in memory safety. The vulnerability specifically manifests when the library encounters improperly formatted lookup table entries that exceed the expected buffer dimensions, creating a condition where adjacent memory regions can be overwritten.
The operational impact of this vulnerability extends across multiple domains where RawSpeed is integrated, including digital photography applications, image processing software, and camera raw file viewers. An attacker could exploit this vulnerability by crafting a malicious raw image file with oversized lookup table data, which when processed by an application using the vulnerable library would trigger the buffer overflow. This exploitation scenario aligns with ATT&CK technique T1203, where adversaries leverage buffer overflow vulnerabilities to execute arbitrary code. The consequences could include complete system compromise, denial of service conditions, or unauthorized access to sensitive data within applications that rely on RawSpeed for image processing.
Mitigation strategies for CVE-2018-25017 primarily involve updating to a patched version of the RawSpeed library where proper bounds checking has been implemented in the TableLookUp::setTable function. Organizations should prioritize patching all systems that utilize RawSpeed, particularly those handling untrusted image files from external sources. Additional defensive measures include implementing input validation controls, deploying sandboxing mechanisms for image processing operations, and establishing robust monitoring for unusual memory access patterns. Security teams should also consider network segmentation to limit the potential impact of exploitation and maintain comprehensive logging of image processing activities to detect anomalous behavior that might indicate attempted exploitation. The vulnerability demonstrates the importance of memory safety practices in image processing libraries and underscores the need for regular security assessments of third-party components used in critical applications.