Leveraging Large Language Models for Trustworthiness Assessment of Web Applications

• URL: http://arxiv.org/abs/2603.23781v1
• Date: Tue, 24 Mar 2026 23:33:54 GMT
• Title: Leveraging Large Language Models for Trustworthiness Assessment of Web Applications
• Abstract summary: This study presents an empirical methodology to automate the trustworthiness assessment of web applications by leveraging Large Language Models (LLMs)<n>We propose an extension of a hierarchical Quality Model (QM) based on the Logic Score of Preference (LSP)<n> Experimental results indicate that excessive structural context can introduce noise, whereas rule-based instructional prompting improves assessment reliability.
• Score: 13.909850314037653
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• (Towards) Scalable Reliable Automated Evaluation with Large Language Models [0.0]
  This work introduces a novel evaluation framework designed to approximate expert-level assessments of Large Language Models (LLMs) outputs.<n>An Elo rating system is used to generate stable and interpretable rankings.<n>Preliminary results show that automatically derived rankings correlate well with expert judgments.
  arXiv  Detail & Related papers  (2026-07-30T14:31:07Z)
• SecPI: Secure Code Generation with Reasoning Models via Security Reasoning Internalization [50.71047638695205]
  Reasoning language models (RLMs) are increasingly used in programming.<n>Yet, even state-of-the-art RLMs frequently introduce critical security vulnerabilities in generated code.<n>We present SecPI, a fine-tuning pipeline that teaches RLMs to internalize structured security reasoning.
  arXiv  Detail & Related papers  (2026-04-04T04:29:11Z)
• WebDevJudge: Evaluating (M)LLMs as Critiques for Web Development Quality [62.43165871914528]
  We introduce WebDevJudge, a systematic benchmark for assessing LLM-as-a-judge performance in web development.<n>WebDevJudge comprises human preference labels over paired web implementations, annotated with structured and query-grounded rubrics.<n>In-depth analysis indicates this gap stems from fundamental model limitations, including failures in recognizing functional equivalence, verifying task feasibility, and mitigating bias.
  arXiv  Detail & Related papers  (2025-10-21T12:16:04Z)
• Expert Preference-based Evaluation of Automated Related Work Generation [54.29459509574242]
  We propose GREP, a multi-turn evaluation framework that integrates classical related work evaluation criteria with expert-specific preferences.<n>For better accessibility, we design two variants of GREP: a more precise variant with proprietary LLMs as evaluators, and a cheaper alternative with open-weight LLMs.
  arXiv  Detail & Related papers  (2025-08-11T13:08:07Z)
• OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics [82.0813150432867]
  We introduce OpenUnlearning, a standardized framework for benchmarking large language models (LLMs) unlearning methods and metrics.<n>OpenUnlearning integrates 13 unlearning algorithms and 16 diverse evaluations across 3 leading benchmarks.<n>We also benchmark diverse unlearning methods and provide a comparative analysis against an extensive evaluation suite.
  arXiv  Detail & Related papers  (2025-06-14T20:16:37Z)
• SV-TrustEval-C: Evaluating Structure and Semantic Reasoning in Large Language Models for Source Code Vulnerability Analysis [39.229080120880774]
  We introduce SV-TrustEval-C, a benchmark designed to evaluate Large Language Models' abilities for vulnerability analysis of code written in the C programming language.<n>Our results show that current LLMs are far from satisfactory in understanding complex code relationships and that their vulnerability analyses rely more on pattern matching than on robust logical reasoning.
  arXiv  Detail & Related papers  (2025-05-27T02:16:27Z)
• Enhancing LLM Reliability via Explicit Knowledge Boundary Modeling [41.19330514054401]
  Large language models (LLMs) are prone to hallucination stemming from misaligned self-awareness.<n>We propose the Explicit Knowledge Boundary Modeling framework to integrate fast and slow reasoning systems to harmonize reliability and usability.
  arXiv  Detail & Related papers  (2025-03-04T03:16:02Z)
• The Vulnerability of Language Model Benchmarks: Do They Accurately Reflect True LLM Performance? [1.3810901729134184]
  Large Language Models (LLMs) excel at standardized tests while failing to demonstrate genuine language understanding and adaptability.<n>Our systematic analysis of NLP evaluation frameworks reveals pervasive vulnerabilities across the evaluation spectrum.<n>We lay the groundwork for new evaluation methods that resist manipulation, minimize data contamination, and assess domain-specific tasks.
  arXiv  Detail & Related papers  (2024-12-02T20:49:21Z)
• SelfPrompt: Autonomously Evaluating LLM Robustness via Domain-Constrained Knowledge Guidelines and Refined Adversarial Prompts [0.6291443816903801]
  This paper introduces a novel framework designed to autonomously evaluate the robustness of large language models (LLMs)<n>Our method generates descriptive sentences from domain-constrained knowledge graph triplets to formulate adversarial prompts.<n>This self-evaluation mechanism allows the LLM to evaluate its robustness without the need for external benchmarks.
  arXiv  Detail & Related papers  (2024-12-01T10:58:53Z)
• Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning [61.2224355547598]
  Open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress. Our investigation exposes a critical oversight in this belief. By deploying carefully designed demonstrations, our research demonstrates that base LLMs could effectively interpret and execute malicious instructions.
  arXiv  Detail & Related papers  (2024-04-16T13:22:54Z)
• TrustScore: Reference-Free Evaluation of LLM Response Trustworthiness [58.721012475577716]
  Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, prompting a surge in their practical applications. This paper introduces TrustScore, a framework based on the concept of Behavioral Consistency, which evaluates whether an LLMs response aligns with its intrinsic knowledge.
  arXiv  Detail & Related papers  (2024-02-19T21:12:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.