Skip to content

Patch-Guided Vulnerability Detection: Extracting Java API Security Rules via Attack–Defense Cross-Analysis

· 2 citations · 41 references

TL;DR

V UL G ENIE is a patch-driven framework that extracts precise API security rules from confirmed security patches to then detect API misuse vulnerabilities, and scales analysis with adaptive, deviation-guided static analysis to balance precision and performance.

View source

Similar papers

Open access Aug 2026

Static Code Analysis Framework for Automated Security Vulnerability Detection

Experimental results show that AST-based structural features substantially improve recall compared with the TF-IDF baseline, while the combined TF-IDF and AST representation maintains this improved performance.

Vani Pasupula, M. N. V. Manikanth, Nagaraju Vassey · 0 citations
Aug 2026

SA2Pat: enhancing binary security patch function localization via security advisory-guided LLMs

SA2Pat (Security Advisory to Patch), a framework that accurately localizes security patches by learning from patterns in historical vulnerability fixes, is proposed, and a fine-tuned SecureBERT model is employed to predict the likely CWE ID, ensuring that the input information remains complete.

Ze-Tan Li, Xiaoya Zhu, Zhuo Li et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Beyond Static Guarantees: Measuring the Static-Pass Dynamic-Fail Gap in Security-Sensitive and LLM-Generated Python Code

The Static-Pass Dynamic-Fail (SPDF) phenomenon and a three-stage agentic pipeline combining static scanning, LLM-driven Common Weakness Enumeration (CWE) reasoning, and autonomous exploit verification in isolated Docker containers are introduced.

Jessica Pourleyli, Maitreyee Das Urmi, Glaucia Melo · 0 citations
Preprint Aug 2026

Finding Vulnerabilities via LLM-Augmented Semantics-Aware Type-Checking

SETYPE is presented, a semantics-aware type system that can be derived directly from source code based solely on the meanings of symbols and expressions in natural language that achieves 87% detection precision and 88% detection accuracy on real-world applications.

Ruizhe Wang, Meng Xu, N. Asokan · 0 citations
Open access Jul 2026

Predictive Web Application Security Using Intelligent SQL Injection Detection

This work presents an intelligent approach for improving web application security through the prediction and detection of SQL injection attacks using machine learning techniques, enabling faster, more reliable, and automated detection of SQL injection vulnerabilities.

D. A. Reddy, G.Rajini · 0 citations

Defensive Capability Analysis for JavaScript Libraries

A defensive capability analysis for JavaScript libraries that soundly reports every exercised capability for code executed under a lightweight protected runtime, complementing the static analysis with a lightweight runtime enforcement mechanism that blocks those patterns.

Unknown authors · 1 citation

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.