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Is AI-Generated Web Code Vulnerability-Free?

Jul 2026 · IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies · pp. 1-6 · 0 citations · 17 references

Abstract

With the increasing usage of AI-generated code in software development workflows, new security challenges and concerns arise. This paper analyzes five LLMs: ChatGPT, Claude, Gemini, DeepSeek, and Grok in three phases of security assessments against web vulnerabilities listed by the OWASP Top 10. Phase 1 (December 2025) evaluated 13 prompts in XSS, Authentication, API Security, Hardcoded Secrets, and React Server Component CVE. Phase 2 (March 2026) evaluated 11 prompts using newly disclosed CVEs, complex authentication, and API security, with updated models. Phase 3 (May 2026) revisited phase one prompts against current models. Across all 185 samples, 49 out of the 120 Phase 1 and 2 samples (40.8%) were identified as vulnerable, 65 (54.2%) as secure and 6 (5.0%) partially vulnerable. The CVE-based prompts recorded the highest percentage of vulnerability (76%). No failures were observed in the authentication prompts with simple patterns, while complex access control resulted in 45% failures. Phase 3 (65 samples) re-ran Phase 1 prompts on current models; no progress was observed; the partial result rate went up from 7.7% to 27.7%. The results revealed hedged responses from the updated models. All results were cross-validated using Semgrep static analysis.

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