CVE-2017-18210 in ImageMagick
Summary
by MITRE
In ImageMagick 7.0.7, a NULL pointer dereference vulnerability was found in the function BenchmarkOpenCLDevices in MagickCore/opencl.c because a memory allocation result is not checked.
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Analysis
by VulDB Data Team • 02/16/2023
The vulnerability CVE-2017-18210 represents a critical NULL pointer dereference issue within ImageMagick version 7.0.7, specifically within the BenchmarkOpenCLDevices function located in the MagickCore/opencl.c source file. This flaw arises from inadequate memory allocation error handling, creating a scenario where the application fails to verify whether a memory allocation operation successfully completes before attempting to use the resulting pointer. The vulnerability is particularly concerning as it exists within a core component responsible for OpenCL device benchmarking, which is essential for performance optimization in image processing workflows. When the memory allocation fails and returns NULL, the subsequent code attempts to dereference this null pointer, leading to an application crash or potential exploitation.
The technical implementation of this vulnerability stems from a fundamental lack of input validation and error checking within the OpenCL benchmarking functionality. In the BenchmarkOpenCLDevices function, developers allocated memory for OpenCL device information structures without verifying that the allocation succeeded. This pattern violates standard software development practices and security best practices outlined in CWE-476, which specifically addresses NULL pointer dereference vulnerabilities. The flaw demonstrates poor defensive programming where the assumption is made that memory allocation will always succeed, a dangerous practice in systems programming. When the allocation fails due to insufficient memory or other resource constraints, the program continues execution with a NULL pointer, resulting in immediate termination or unpredictable behavior.
The operational impact of CVE-2017-18210 extends beyond simple application crashes, as it can be leveraged for denial of service attacks against systems processing image files through ImageMagick. Attackers could craft malicious image files that trigger the memory allocation failure condition, causing the application to crash and potentially leading to service disruption for legitimate users. This vulnerability particularly affects web applications and services that utilize ImageMagick for image processing, as these systems often process untrusted user input. The exploitability is enhanced by the fact that the vulnerability occurs during the normal operation of image processing, making it difficult to distinguish between legitimate and malicious input. From an attacker's perspective, this vulnerability aligns with ATT&CK technique T1499.004, which covers network denial of service attacks, as the crash can be triggered through crafted input files.
Mitigation strategies for CVE-2017-18210 should focus on immediate code-level fixes and broader system hardening measures. The primary solution involves implementing proper error checking for all memory allocation operations within the BenchmarkOpenCLDevices function, ensuring that NULL return values are handled gracefully rather than passed to subsequent operations. This fix aligns with CWE-252, which addresses the lack of checks for error conditions, and represents a fundamental requirement for secure coding practices. Organizations should also consider implementing input validation and sanitization for all image processing workflows, particularly for web applications where untrusted input is common. Additionally, deploying application-level sandboxing and resource limiting can help contain the impact of such vulnerabilities. Regular security updates and vulnerability assessments are essential, as this type of flaw demonstrates the importance of comprehensive testing and validation of memory management operations in security-critical applications. The vulnerability serves as a reminder of the critical importance of defensive programming practices and the need for thorough error handling in systems processing untrusted data.