Secure coding for web applications: Frameworks, challenges, and the role of LLMs
July 29, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Kiana Kiashemshaki, Mohammad Jalili Torkamani, Negin Mahmoudi
arXiv ID
2507.22223
Category
cs.SE: Software Engineering
Citations
5
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Secure coding is a critical yet often overlooked practice in software development. Despite extensive awareness efforts, real-world adoption remains inconsistent due to organizational, educational, and technical barriers. This paper provides a comprehensive review of secure coding practices across major frameworks and domains, including web development, DevSecOps, and cloud security. It introduces a structured framework comparison and categorizes threats aligned with the OWASP Top 10. Additionally, we explore the rising role of Large Language Models (LLMs) in evaluating and recommending secure code, presenting a reproducible case study across four major vulnerability types. This paper offers practical insights for researchers, developers, and educators on integrating secure coding into real-world development processes.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted