A comprehensive analysis of security risks and productivity impacts of AI-generated code

Security Vulnerabilities in AI-Generated JWT Authentication Code for Spring Boot

A Comparative Analysis of Security Vulnerabilities and Defense Mechanisms in Large Language Models

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.

Security Vulnerability Patterns in AI-Generated Code: A Cross-Model Comparative Study

LLM-based coding tools enable non-expert users to generate routine automation scripts that may enter enterprise workflows without meaningful security review. This study examines that risk directly. Code was collected from ChatGPT, Microsoft Copilot, and Google Gemini using identical prompts across three automation domains. Claude Code performed a standardized vulnerability review. Each identified vulnerability was scored using CVSS v3.1 and mapped to the OWASP Top 10:2021 and the MITRE ATT&CK frameworks. Every script contained exploitable vulnerabilities. Nine of the 17 identified vulnerability classes appeared in code from all three models, while 14 of the 17 vulnerability classes appeared in at least two models. The weighted CVSS scores across platforms differed by less than 10%. The risk is not tied to any particular model but rather to the task category. Organizations should therefore ask not which tool to trust, but instead whether LLM-generated automation code should be deployed without review.

Cross-Cutting Security Analysis of LLM-Generated Code via Metamorphic Testing and Association Rule Mining

Large language models (LLMs) frequently generate code with security vulnerabilities, yet these weaknesses are rarely isolated: they often span multiple concern areas simultaneously, reflecting the cross-cutting nature of security in software. We present a framework that combines security-oriented Metamorphic Relations (MRs) with Association Rule (AR) mining to detect vulnerabilities in LLM-generated code, uncover their co-violation structure, and trace that structure back to prompt-level risk factors. We define nine MRs covering major CWE categories, including SQL injection, XSS, command injection, path traversal, hard-coded credentials, weak cryptography, and memory-safety errors, and apply them using an LLM-based judge to 3,700 code snippets generated by five open models from the LLMSecEval benchmark. The results show that 68.8% of snippets violate at least one MR, with hard-coded credentials (79.1%) and command injection (74.4%) among the most prevalent applicable failures. AR mining reveals strong cross-cutting co-violation patterns, notably that XSS and weak cryptography co-violations predict hard-coded credentials with 82.5% confidence (lift = 3.23), along with tightly coupled clusters linking authentication, credential handling, and cryptographic weakness, as well as input-handling and memory-safety failures. We then perform prompt-level risk analysis and find that database- and authentication-related prompts are strong predictors of broad cross-cutting insecurity, while 65.5% of prompts yield consistent violation outcomes across all five models. These 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.

RAVE-Code: A Risk-Aware Verification Engine for AI-Generated Code Security Using Composite Risk Scoring and CWE-Conditioned Model Checking

: Large Language Models (LLMs) are increasingly being used to generate source code. However, a substantial proportion of their output contains security vulnerabilities. Existing defenses typically apply uniform analysis to all code fragments, irrespective of their risk profiles. This study presents RAVE-Code, a three-layer framework that calibrates the verification effort based on the risk associated with each detected weakness. The Detection layer employs Bandit for pattern-based static analysis, annotating findings with their respective Common Weakness Enumeration (CWE) classes. The Risk Scoring layer calculates a composite risk score for each weakness instance by integrating the Common Vulnerability Scoring System (CVSS) severity, the prevalence of CWEs in AI-generated code, and actual exploitability data. The Verification layer directs each code fragment to an appropriate depth of Bounded Model Checking (BMC) using the Efficient SMT-Based Bounded Model Checker (ESBMC), with solver parameters customized for each CWE class. The framework is evaluated on three public benchmarks. On SecurityEval, Bandit identifies 28.5% of vulnerabilities with zero false positives, establishing a baseline for static analysis coverage. On FormAI, RAVE-Code achieves a detection rate of 86.0% ( F 1 = 0.909), compared to 73.8% ( F 1 = 0.834) for uniform verification, and uniquely detects 41 vulnerabilities that uniform analysis misses. Because FormAI’s labels are based on ESBMC, these results show how consistent two ESBMC configurations rather than agreement with an independent standard. On the Juliet Test Suite, which provides structurally independent ground truth, RAVE-Code achieves a detection rate of 57.3% ( F 1 = 0.614) compared to 40.8% ( F 1 = 0.479), detecting a total of 104 vulnerabilities. Applying all flags uniformly results in a detection rate 18.8 percentage points lower than the proposed per-CWE tailoring. This indicates that detection gains arise from routing rather than flag selection.

An Empirical Evaluation of Generative AI in Security Requirements Engineering and Threat Modeling

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.