Software Vulnerability Recognition Task

A Software Vulnerability Recognition Task is a recognition task for security flaws in software code.

• Context:
  • It can (typically) be a part of a Software Vulnerability Analysis Task.
  • It can be supported by a Software Vulnerability Recognition System (that implements a software vulnerability recognition algorithm).
  • ...
• Example(s):
  • a Hard-Coded Credentials Recognition Task: Recognizes hard-coded software system credentials (e.g. stored in the code).
  • Unvalidated Input Recognition: Recognizes malicious content input.
  • Insecure Deserialization Recognition: Recognizes insecure deserialization (that converts data from a serialized format into its original form).
  • Insecure Direct Object References Recognition: Recognizes insecure direct object references (references to objects that are not properly protected).
  • Weak Authentication Recognition: Recognizes weak authentication (which allows attackers to gain unauthorized access to the system without having to provide any credentials).
  • Insecure Cryptographic Libraries Recognition: Recognizes insecure cryptographic libraries (which implement weak encryption algorithms).
  • ...
• Counter-Example(s):
  • Physical Machine Vulnerability Recognition.
• See: DARPA AIxCC Contest, Vulnerable Code Pattern, Software Vulnerability Repair.

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References

2023

• Google Bard
  • QUOTE: Software vulnerability recognition is the process of identifying security flaws in software code. This can be done through a variety of methods, including:
    • Static Vulnerability Recognition Analysis: This involves inspecting the software code without executing it. This can be done using tools that scan the code for known vulnerabilities or patterns that are indicative of vulnerabilities.
    • Dynamic Vulnerability Recognition Analysis: This involves executing the software code and observing its behavior. This can be done using tools that inject test inputs into the software and monitor its responses.
    • Manual Vulnerability Recognition Analysis: This involves a human analyst reviewing the software code line by line to look for vulnerabilities. This is the most thorough method, but it is also the most time-consuming and labor-intensive.
  • NOTE: It discusses the different methods for identifying Security Flaws in Software Code, including both automated and manual techniques.

2020

• (Lin, Wen et al., 2020) ⇒ G. Lin, S. Wen, Q.L. Han, J. Zhang, and others. (2020). "Software Vulnerability Detection Using Deep Neural Networks: A Survey." In: IEEE Proceedings.
  • ABSTRACT: The constantly increasing number of disclosed security vulnerabilities have become an important concern in the software industry and in the field of cybersecurity, suggesting that the current approaches for vulnerability detection demand further improvement. The booming of the open-source software community has made vast amounts of software code available, which allows machine learning and data mining techniques to exploit abundant patterns within software code. Particularly, the recent breakthrough application of deep learning to speech recognition and machine translation has demonstrated the great potential of neural models’ capability of understanding natural languages. This has motivated researchers in the software engineering and cybersecurity communities to apply deep learning for learning and understanding vulnerable code patterns and semantics indicative of the characteristics of vulnerable code. In this survey, we review the current literature adopting deep-learning-/neural-network-based approaches for detecting software vulnerabilities, aiming at investigating how the state-of-the-art research leverages neural techniques for learning and understanding code semantics to facilitate vulnerability discovery. We also identify the challenges in this new field and share our views of potential research directions.
  • NOTE: It offers a comprehensive review of how deep learning and neural network technologies are being utilized in the field of software vulnerability detection. It discusses the emerging trend of leveraging machine learning methods to understand and identify vulnerable code patterns, and points out current challenges and future research directions in this area.
  • NOTE: It surveys the application of deep neural networks in the field of software vulnerability detection, highlighting their potential advantages.

2019

• (Huang et al., 2019) ⇒ G Huang, Y Li, Q Wang, J Ren, Y Cheng, and X Zhao. (2019). "Automatic Classification Method for Software Vulnerability Based on Deep Neural Network." In: IEEE Access.
  • QUOTE: "In order to verify the effectiveness, we use the internationally recognized National Vulnerability Database (NVD) as experimental data..."
  • NOTE: It presents a methodology for automated classification of software vulnerabilities using deep neural networks.

2017

• (Ghaffarian & Shahriari, 2017) ⇒ SM Ghaffarian, and HR Shahriari. (2017). "Software Vulnerability Analysis and Discovery Using Machine-learning and Data-mining Techniques: A Survey." In: ACM Computing Surveys (CSUR).
  • QUOTE: "Vulnerable Code Pattern Recognition and Anomaly Detection Approaches..."
  • NOTE: It provides an extensive survey of machine-learning and data-mining techniques used for software vulnerability analysis.

2009

• (Sedaghat et al., 2009) ⇒ S Sedaghat, F Adibniya, and others. (2009). "The Investigation of Vulnerability Test in Application Software." In: IEEE Conference on the Application of Software.
  • QUOTE: "A static analyzer can assess many of vulnerability recognition patterns without running codes..."
  • NOTE: It discusses the role of static analyzers in identifying software vulnerabilities.

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