Evaluating LLMs for Real-World Web Vulnerability Detection
Large Language Models (LLMs) have emerged as a promising tool for automated vulnerability detection, yet their effectiveness on web-specific vulnerabilities remains to be explored. This work benchmarks six frontier (Claude Opus 4.6, Codex GPT-5.4, Gemini 3.1-pro-preview) and open-weight models (Qwen 3.5, Qwen 3 Coder Next, MiniMax M2.5) on their ability to detect real-world web vulnerabilities using static analysis in WordPress plugins, including SQL injection, stored cross-site scripting, path traversal, and remote code execution. Using five prompt designs of varying structure, scope, and complexity across three experiment iterations, we aim to answer how model and prompt choice affects ...
| Authors | Sebastian Neef, Luca Jungnickel, Antonio Benjamin Buchholz et al. |
|---|---|
| Primary category | cs.CR |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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