Labels Predicted by AI
Prompt leaking Evaluation Method
Please note that these labels were automatically added by AI. Therefore, they may not be entirely accurate.
For more details, please see the About the Literature Database page.
Abstract
The widespread adoption of web applications has made their security a critical concern and has increased the need for systematic ways to assess whether they can be considered trustworthy. However, “trust” assessment remains an open problem as existing techniques primarily focus on detecting known vulnerabilities or depend on manual evaluation, which limits their scalability; therefore, evaluating adherence to secure coding practices offers a complementary, pragmatic perspective by focusing on observable development behaviors. In practice, the identification and verification of secure coding practices are predominantly performed manually, relying on expert knowledge and code reviews, which is time-consuming, subjective, and difficult to scale. This study presents an empirical methodology to automate the trustworthiness assessment of web applications by leveraging Large Language Models (LLMs) to verify adherence to secure coding practices. We conduct a comparative analysis of prompt engineering techniques across five state-of-the-art LLMs, ranging from baseline zero-shot classification to prompts enriched with semantic definitions, structural context derived from call graphs, and explicit instructional guidance. Furthermore, we propose an extension of a hierarchical Quality Model (QM) based on the Logic Score of Preference (LSP), in which LLM outputs are used to populate the model’s quality attributes and compute a holistic trustworthiness score. Experimental results indicate that excessive structural context can introduce noise, whereas rule-based instructional prompting improves assessment reliability. The resulting trustworthiness score allows discriminating between secure and vulnerable implementations, supporting the feasibility of using LLMs for scalable and context-aware trust assessment.