CVE-2016-3177 in giflib
Summary
by MITRE
Multiple use-after-free and double-free vulnerabilities in gifcolor.c in GIFLIB 5.1.2 have unspecified impact and attack vectors.
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Analysis
by VulDB Data Team • 05/14/2026
The vulnerability identified as CVE-2016-3177 affects GIFLIB version 5.1.2 and involves multiple use-after-free and double-free conditions within the gifcolor.c file. This critical flaw resides in the library's handling of GIF color table processing, where improper memory management leads to potential arbitrary code execution or denial of service conditions. The vulnerability stems from insufficient validation of color table data during GIF file parsing, creating opportunities for attackers to manipulate memory allocation patterns through malformed input files. These issues are particularly concerning as GIFLIB is widely used across numerous applications and operating systems for image processing, making the attack surface extensive. The unspecified impact and attack vectors indicate that the vulnerability could be exploited in various scenarios, potentially allowing remote code execution or system compromise depending on how the library is integrated into target applications.
The technical implementation of this vulnerability manifests through improper memory deallocation patterns where the gifcolor.c module fails to properly track memory references during color table processing. When parsing malformed GIF files containing crafted color table data, the library performs multiple free operations on the same memory location or accesses memory after it has been freed, creating conditions for memory corruption. This memory management flaw represents a classic use-after-free vulnerability classified under CWE-416, where the system attempts to access memory after it has been freed, and potentially double-free conditions under CWE-415. The vulnerability occurs during the parsing of GIF color maps, where the library allocates memory for color table entries and subsequently fails to maintain proper reference counting or null pointer checks, leading to unpredictable behavior when the same memory regions are accessed or freed multiple times.
The operational impact of CVE-2016-3177 extends across numerous software ecosystems that depend on GIFLIB for image processing functionality, including web browsers, image viewers, content management systems, and various multimedia applications. Attackers could exploit this vulnerability by crafting malicious GIF files that trigger the memory corruption conditions during normal image rendering operations. The attack vector typically involves user interaction through web browsing or file opening scenarios where the vulnerable library processes untrusted input. This vulnerability aligns with ATT&CK technique T1203 by enabling adversaries to execute arbitrary code through memory corruption, and potentially T1059 for command execution if the exploitation leads to full system compromise. The vulnerability's impact is particularly severe in server environments where GIFLIB is used for image processing in web applications, as it could allow remote attackers to execute code on vulnerable systems without requiring user interaction beyond accessing the malicious content.
Mitigation strategies for CVE-2016-3177 should prioritize immediate patching of affected GIFLIB versions, with the release of GIFLIB 5.1.3 containing the necessary memory management fixes. System administrators should implement comprehensive patch management procedures to ensure all applications using GIFLIB receive updates promptly, particularly in environments handling untrusted image content. Network-based mitigations can include filtering GIF files at perimeter defenses, though this approach is less effective than proper patching. Organizations should also consider implementing application sandboxing for image processing functions and monitoring for unusual memory allocation patterns that might indicate exploitation attempts. The vulnerability highlights the importance of proper memory management practices and the need for thorough input validation in image processing libraries. Security teams should conduct vulnerability assessments to identify all systems using affected versions of GIFLIB and prioritize remediation efforts based on risk exposure. Additionally, implementing automated monitoring for memory corruption patterns and maintaining up-to-date security patches for all image processing libraries will help prevent similar vulnerabilities from being exploited in the future, aligning with defensive strategies recommended in the MITRE ATT&CK framework for preventing privilege escalation and code execution attacks.