CVE-2018-19655 in dcraw
Summary
by MITRE
A stack-based buffer overflow in the find_green() function of dcraw through 9.28, as used in ufraw-batch and many other products, may allow a remote attacker to cause a control-flow hijack, denial-of-service, or unspecified other impact via a maliciously crafted raw photo file.
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Analysis
by VulDB Data Team • 06/12/2023
The vulnerability identified as CVE-2018-19655 represents a critical stack-based buffer overflow flaw within the dcraw library version 9.28 and earlier. This issue specifically affects the find_green() function which processes raw photo files, making it a significant concern for applications that handle image data processing. The vulnerability stems from inadequate input validation and memory management within the image parsing routine, creating a potential attack surface that could be exploited by malicious actors. The affected ecosystem includes ufraw-batch and numerous other software products that rely on dcraw for raw image file processing, demonstrating the widespread impact of this flaw across various image processing applications.
The technical exploitation of this vulnerability occurs when a maliciously crafted raw photo file is processed by software utilizing the vulnerable dcraw library. The find_green() function fails to properly bounds-check array accesses during the parsing of raw image data, allowing an attacker to overwrite adjacent stack memory locations. This overflow condition creates opportunities for control-flow hijacking through stack smashing techniques, potentially enabling arbitrary code execution. The vulnerability manifests as a classic stack-based buffer overflow with characteristics aligned with CWE-121, which describes stack-based buffer overflow conditions where insufficient bounds checking allows data to overwrite adjacent stack memory. The attack vector requires the target system to process a specially crafted raw image file, making it a remote code execution vulnerability when the affected applications are used in web-based or automated processing environments.
The operational impact of CVE-2018-19655 extends beyond simple denial-of-service conditions to encompass potential full system compromise. When successfully exploited, the buffer overflow can lead to arbitrary code execution, allowing attackers to gain control of the affected system or application. This vulnerability is particularly concerning in server environments where image processing applications might automatically process user-uploaded raw photo files without proper validation. The remote exploitation capability means that attackers can potentially compromise systems through web applications or services that process raw image formats, making it a significant threat in cloud computing and web-based image processing platforms. The vulnerability also impacts the integrity of the affected systems, as attackers could potentially execute malicious code that modifies system behavior or exfiltrates data. The potential for denial-of-service conditions further compounds the risk, as attackers could disrupt legitimate image processing operations and service availability.
Mitigation strategies for CVE-2018-19655 should prioritize immediate software updates to versions of dcraw that contain patches for the identified buffer overflow vulnerability. Organizations should implement strict input validation and sanitization for all raw image file processing, ensuring that applications reject malformed or suspicious input data before it reaches the vulnerable parsing functions. Network segmentation and access controls should be implemented to limit exposure of vulnerable applications to untrusted input sources. Security monitoring should include detection of unusual image processing activities that might indicate exploitation attempts. The vulnerability's classification aligns with ATT&CK technique T1203, which covers exploitation for execution through the manipulation of memory, and represents a common vector for privilege escalation attacks. Regular security assessments and penetration testing should be conducted to identify other potential buffer overflow conditions within the image processing pipeline, while application developers should adopt secure coding practices including bounds checking, stack canaries, and address space layout randomization to prevent similar vulnerabilities from occurring in future implementations.