CVE-2018-13997 in Genann
Summary
by MITRE
Genann through 2018-07-08 has a SEGV in genann_run in genann.c.
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Analysis
by VulDB Data Team • 04/06/2023
The vulnerability identified as CVE-2018-13997 affects the Genann neural network library version 2018-07-08 and earlier, presenting a critical segmentation fault condition that can lead to application crashes and potential denial of service scenarios. This issue manifests specifically within the genann_run function located in the genann.c source file, where improper memory handling or input validation allows malicious actors to trigger a segmentation violation that terminates the executing process.
The technical flaw stems from insufficient bounds checking and memory management within the neural network inference routine. When the genann_run function processes input data, it fails to adequately validate the dimensions and content of the input arrays, leading to memory access violations that result in segmentation faults. This vulnerability aligns with CWE-125, which addresses out-of-bounds read conditions, and CWE-129, which covers insufficient validation of array indices. The flaw operates at the intersection of memory safety and input validation, where the library does not properly sanitize user-provided neural network parameters or input data before processing.
From an operational perspective, this vulnerability presents significant risks to applications that rely on Genann for machine learning inference tasks. Systems utilizing this library for real-time processing, automated decision making, or embedded applications could experience unexpected termination when processing malformed input data, potentially leading to service disruption or complete system failure. The impact extends beyond simple crashes as adversaries could exploit this weakness to cause denial of service against neural network powered applications, particularly in environments where continuous operation is critical such as financial trading platforms, autonomous systems, or industrial control networks.
The attack surface for this vulnerability encompasses any application that integrates the Genann library and accepts external input for neural network processing. This includes web applications, mobile apps, and embedded systems that utilize machine learning for pattern recognition, classification, or prediction tasks. The exploitability of this weakness is relatively straightforward, as it requires only the ability to provide malformed input data to trigger the segmentation fault condition, making it particularly dangerous in environments where input validation is insufficient.
Mitigation strategies should prioritize immediate patching of the Genann library to the latest version that addresses this segmentation fault vulnerability. Organizations should implement comprehensive input validation measures that verify array dimensions and memory boundaries before processing neural network data. Additionally, deployment of defensive programming practices such as stack canaries, address space layout randomization, and memory protection mechanisms can provide additional layers of defense. The remediation approach should also include regular security assessments of neural network libraries and dependencies, with particular attention to memory safety issues and input validation controls. This vulnerability demonstrates the critical importance of memory safety in machine learning libraries and the potential for seemingly benign input processing to cause catastrophic system failures, aligning with ATT&CK technique T1499.004 for network denial of service and highlighting the need for robust software security practices in artificial intelligence applications.