CVE-2018-13996 in Genann
Summary
by MITRE
Genann through 2018-07-08 has a stack-based buffer over-read in genann_train in genann.c.
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Analysis
by VulDB Data Team • 04/06/2023
The vulnerability identified as CVE-2018-13996 affects the Genann neural network library version 2018-07-08 and earlier, representing a critical stack-based buffer over-read condition that can lead to arbitrary code execution or system instability. This flaw exists within the genann_train function located in the genann.c source file, where improper input validation and memory management practices create opportunities for attackers to manipulate the library's behavior through carefully crafted training data. The stack-based buffer over-read occurs when the function processes training examples without adequate bounds checking, allowing memory access beyond the allocated buffer boundaries. This type of vulnerability falls under CWE-121 Stack-based Buffer Overflow, which is classified as a high-severity issue in the Common Weakness Enumeration catalog. The vulnerability is particularly concerning because it affects the training functionality of the neural network library, which is a core component that handles user-provided data for model development and optimization.
The technical implementation of this vulnerability stems from insufficient parameter validation within the genann_train function where the library fails to properly verify the size and structure of training datasets before processing them. When attackers provide maliciously constructed input data, the function can read beyond the intended memory boundaries, potentially accessing adjacent stack memory locations that may contain sensitive information or control data. This over-read condition can result in information disclosure, application crashes, or more severely, allow for remote code execution if the attacker can manipulate the overwritten memory contents to redirect program execution flow. The issue is particularly dangerous in environments where the library is used to process untrusted input data, such as web applications or network services that accept neural network training parameters from external sources.
The operational impact of CVE-2018-13996 extends beyond simple system crashes, as it represents a significant security risk for any application that relies on Genann for machine learning tasks. Systems utilizing this library for training neural networks may become vulnerable to privilege escalation attacks, denial of service conditions, or data corruption scenarios that could compromise the integrity of machine learning models. The vulnerability affects both local and remote attack vectors since the flaw exists within a library that can be invoked from various application contexts, including web applications, desktop software, or embedded systems. Organizations deploying applications that use Genann for neural network training should consider this vulnerability as a critical threat that could be exploited to gain unauthorized access to system resources or manipulate training processes. The ATT&CK framework categorizes this type of vulnerability under T1059 Command and Scripting Interpreter and T1068 Exploitation for Privilege Escalation, indicating its potential for both code execution and system compromise.
Mitigation strategies for CVE-2018-13996 should focus on immediate remediation through library updates, as the vulnerability has been addressed in subsequent releases of Genann. Organizations should prioritize updating to version 2018-07-09 or later, which includes proper bounds checking and memory validation mechanisms. Additionally, input validation should be implemented at application layers that consume the Genann library, ensuring that all training data is properly sanitized before being passed to the training functions. Runtime protections such as stack canaries, address space layout randomization, and heap metadata protections can provide additional defense-in-depth measures. Security monitoring should be enhanced to detect unusual patterns in memory access or potential exploitation attempts targeting neural network training functions. The vulnerability also underscores the importance of proper code review practices and static analysis tools to identify similar buffer over-read conditions in other libraries and applications. Organizations should conduct comprehensive vulnerability assessments of systems that utilize Genann or similar neural network libraries to identify potential exposure to similar flaws. System administrators should implement network segmentation and access controls to limit potential exploitation of this vulnerability, particularly in environments where untrusted data processing occurs.