Jun 2026· AI and Ethics· Vol 6· 0 citations· 49 references
Computer Science
TL;DR
This systematic literature review examines the dual nature of AI-generated code by analyzing peer-reviewed research to quantify productivity benefits and identify associated risks, and develops a taxonomy categorizing risks into three domains.
Investigating the security vulnerabilities present in AI-generated JWT authentication code for Java Spring Boot Representational State Transfer Application Programming Interfaces (REST API) reinforces that AI-generated JSON Web Token (JWT) authentication code requires dedicated security review.
Hoang Long Nguyen, Mezid Hmudda, Benjamin Powley· 0 citations
Large Language Models (LLMs) are now deployed at an unprecedented scale across many critical sectors, rapidly transitioning from experimental AI tools to embedded components of production software systems. This accelerated adoption, often enabled by low-code integrations, has lowered technical barriers while simultaneously expanding the attack surface of modern applications, particularly when deployments occur without sufficient domain-specific security expertise. In many cases, security maturity has not progressed at the same pace as capability expansion, creating systemic exposure across confidentiality, integrity, and availability dimensions. To provide structured clarity amid this rapid growth, this paper presents a comparative and standards-aligned analysis of LLM security risks and defense mechanisms grounded in the OWASP GenAI Top-10 (2025). We systematically examine each vulnerability class, map representative attack patterns to primary mitigation strategies, evaluate their security property impact, and analyze practical limitations and implementation trade-offs. In addition, we introduce a severity-based assessment to prioritize risks according to operational and systemic impact, offering a quantitative perspective on defensive readiness. Our findings indicate that current mitigation strategies are predominantly reactive, concentrated at inference time, and unevenly distributed across the LLM lifecycle. Controls addressing training pipelines, supplychain dependencies, and autonomous system behaviors remain comparatively less mature and less standardized. By integrating vulnerability classification, defense mapping, severity prioritization, and trade-off analysis within a unified framework, this study provides actionable guidance for strengthening secure, resilient, and standards-driven LLM deployment in high-stakes environments.
Md Abdul Barek, Md Bajlur Rashid, A. K. I. Riad et al.· Annual International Compute...· 0 citations
The risk is not tied to any particular model but rather to the task category, and organizations should ask not which tool to trust, but instead whether LLM-generated automation code should be deployed without review.
Findings show that insecure code generation is not merely a collection of independent defects, but a structured and prompt-conditioned phenomenon, motivating cluster-aware verification and prompt-level intervention for safer LLM-assisted programming.
This study presents RAVE-Code, a three-layer framework that calibrates the verification effort based on the risk associated with each detected weakness, and demonstrates how consistent two ESBMC configurations rather than agreement with an independent standard can be.
Maher Alharby, Ali Alssaiari· Computers, Materials & C...· 0 citations
Empirical evidence is provided that generative AI can effectively support security requirements engineering when embedded within human-centered workflows and organizational governance structures, offering practical insights for adoption in regulated software development contexts.
F. Martins, Elaine Venson· SIGSOFT FSE Companion· 0 citations