An Empirical Analysis of Vulnerabilities in Python Packages for Web Applications

This paper examines software vulnerabilities in common Python packages used particularly for web development. The empirical dataset is based on the PyPI package repository and the so-called Safety DB used to track vulnerabilities in selected packages within the repository. The methodological approach builds on a release-based time series analysis of the conditional probabilities for the releases of the packages to be vulnerable. According to the results, many of the Python vulnerabilities observed seem to be only modestly severe; input validation and cross-site scripting have been the most typical vulnerabilities. In terms of the time series analysis based on the release histories, only the recent past is observed to be relevant for statistical predictions; the classical Markov property holds.Comment: Forthcoming in: Proceedings of the 9th International Workshop on Empirical Software Engineering in Practice (IWESEP 2018), Nara, IEE

Security assessment framework for educational ERP systems

The educational ERP systems have vulnerabilities at the different layers such as version-specific vulnerabilities, configuration level vulnerabilities and vulnerabilities of the underlying infrastructure. This research has identified security vulnerabilities in an educational ERP system with the help of automated tools; penetration testing tool and public vulnerability repositories (CVE, CCE) at all layers. The identified vulnerabilities are analyzed for any false positives and then clustered with mitigation techniques, available publicly in security vulnerability solution repository like CCE and CWE. These mitigation techniques are mapped over reported vulnerabilities using mapping algorithms. Security vulnerabilities are then prioritized based on the Common Vulnerability Scoring System (CVSS). Finally, open standards-based vulnerability mitigation recommendations are discussed