CVE-2017-9160 in AutoTrace
Summary
by MITRE
libautotrace.a in AutoTrace 0.31.1 has a stack-based buffer overflow in the pnmscanner_gettoken function in input-pnm.c:458:12.
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Analysis
by VulDB Data Team • 10/02/2020
The vulnerability identified as CVE-2017-9160 represents a critical stack-based buffer overflow in the AutoTrace 0.31.1 library component libautotrace.a. This flaw specifically manifests within the pnmscanner_gettoken function located in the input-pnm.c file at line 458, where the software fails to properly validate input data length before copying it into a fixed-size stack buffer. The issue arises during the processing of PNM (Portable AnyMap) image format files, which are commonly used in computer graphics and image processing applications. When AutoTrace attempts to parse malformed or specially crafted PNM files, the function does not perform adequate bounds checking on the token data being read from the input stream, creating an exploitable condition that can be leveraged by malicious actors to overwrite adjacent stack memory.
The technical exploitation of this vulnerability follows a classic stack buffer overflow pattern that aligns with CWE-121, which describes stack-based buffer overflow conditions where insufficient bounds checking allows an attacker to overwrite adjacent memory locations on the program stack. The pnmscanner_gettoken function appears to use a fixed-size buffer to store token data read from PNM files without verifying that the incoming data fits within allocated memory boundaries. This flaw creates opportunities for attackers to inject malicious code or manipulate program execution flow by overwriting return addresses, stack canaries, or other critical memory structures. The vulnerability is particularly concerning because it occurs during the input parsing phase of AutoTrace's image processing workflow, meaning that any application or system utilizing this library could be vulnerable to remote code execution when processing untrusted PNM image files.
The operational impact of CVE-2017-9160 extends beyond simple denial of service scenarios, as it presents a significant security risk for systems that process untrusted image data. Attackers could leverage this vulnerability to execute arbitrary code on systems running vulnerable versions of AutoTrace or applications that incorporate the affected library. The vulnerability's presence in a widely used image processing library means that numerous applications could be at risk, including graphic design tools, image conversion utilities, and automated processing systems that handle user-uploaded content. Systems utilizing AutoTrace for batch processing of PNM files, particularly those accepting input from untrusted sources, face elevated risk of compromise. The vulnerability's exploitation potential aligns with ATT&CK technique T1059.007, which covers the execution of malicious code through command and scripting interpreters, as successful exploitation could lead to complete system compromise.
Mitigation strategies for CVE-2017-9160 should prioritize immediate patching of affected AutoTrace installations to version 0.31.2 or later, which contains the necessary fixes for the buffer overflow condition. Organizations should also implement input validation controls to prevent processing of malformed PNM files, particularly in environments where untrusted input is expected. Network segmentation and access controls can help limit the potential impact of exploitation by restricting access to systems that process image data. Additionally, deploying intrusion detection systems with signatures for known exploitation patterns related to buffer overflow vulnerabilities can provide early warning of attempted exploitation. The vulnerability's classification as a stack-based buffer overflow makes it particularly susceptible to exploitation using techniques such as return-oriented programming or stack pivoting, which further emphasizes the importance of immediate remediation. Security teams should also consider implementing runtime protections such as stack canaries, address space layout randomization, and data execution prevention mechanisms to reduce the exploitability of similar vulnerabilities in the broader codebase.