Skip to content
#software testing Review Open access

Malice-Scan: A Configurable Multi-Language Static Security Scanner for Pattern-Based Code Analysis

Sep 2026 · International Journal for Research in Applied Science and Engineering Technology · 0 citations

TL;DR

Malice-Scan is presented, a configurable static analysis security scanner designed to inspect source code without executing it, which provides a lightweight and extensible approach for security-oriented source-code scanning and can be integrated into local review and CI/CD workflows.

Abstract

Software projects often contain insecure coding practices, suspicious constructs, exposed secrets, and other patterns that require early identification during development and code review. This paper presents Malice-Scan, a configurable static analysis security scanner designed to inspect source code without executing it. The implemented system supports JavaScript, Python, PHP, Java, Ruby, and Shell scripting. Its architecture separates command-line orchestration, the detection engine, configuration management, utility functions, and multi-format reporting. The scanner applies forty configurable regularexpression-based detection patterns covering code injection, backdoors and network operations, runtime-error patterns, cryptographic weaknesses, and other security-relevant issues. It further incorporates heuristic checks for minified JavaScript and large Base64-like content, along with confidence estimation based on severity, dangerous keywords, entropy, and contextual indicators. Practical safeguards include file-size limits, regularexpression timeouts, Base64 decoding limits, scan-depth limits, path-traversal controls, and per-rule finding limits. Findings can be filtered by severity and exported in JSON, HTML, or CSV formats. The test structure includes vulnerable and clean samples, with a comprehensive test project containing 77 intentional issues across six supported languages. The resulting system provides a lightweight and extensible approach for security-oriented source-code scanning and can be integrated into local review and CI/CD workflows.

Read PDF

Similar papers

Preprint Sep 2026

CASHEWS: Source Preprocessor for LLM-based Malicious Package Detection

Malicious npm package detection tools now leverage LLMs'semantic understanding of source code to detect malicious intent at scale. This capability has proven invaluable in identifying packages involved in recent supply-chain attacks such as Shai-Hulud. However, threat actors exploit the limited context windows of LLMs...

Jean-Charles Noirot Ferrand, David Adei, Anders Møller et al. · 0 citations
#small language model Book Open access Oct 2026

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.

Wen-Yu Xu, Anders Møller · 0 citations
Open access Sep 2026

On-Premise CodeBERT-Driven Model for Vulnerability Detection in Source Code

An on premise Artificial Intelligence (AI)-based model for vulnerability detection in source code, designed to ensure there is efficiency in identifying potential weaknesses, and deployed locally within a Dockerized environment.

D. Sako · 0 citations
#software testing Open access Sep 2026

LLM-Assisted Porting of Security-Critical C Libraries to Idiomatic Rust: A Multi-Model Empirical Study

Differential fuzzing reveals complementary bugs in the manual and LLM porting of security-critical C libraries to idiomatic Rust and translates these findings into concrete practical guidance for teams planning a similar migration.

Marco Parrillo, Marco Grassi, Luigi Laura · 0 citations
#artificial intelligence Preprint Sep 2026

Exploring Automated Vulnerability Identification in JavaScript Code Using Large Language Models

Results indicate that LLMs provide a practical approach for automated vulnerability identification in JavaScript code, particularly when combined with task-aligned supervision, though they should complement rather than replace existing security analysis workflows due to limited recall and uneven performance across vuln...

Manit Kaushik, Ishir Bhardwaj, Pranav Gupta et al. · 0 citations
Open access Aug 2026

AI-Enhanced Static Code Analyzer for Secure Coding Support

This paper is innovative in its two-process theory, the conceptual underpinning for the translation of the technical diagnostics into the rich narrative stories for an education audience.

H. Jie, M. Mansor, Ranjit Singh · 0 citations

Related blog posts

Microsoft Research Blog Oct 30, 2026

From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence

Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence appeared first on Microsoft Research.

Microsoft Research Blog Oct 29, 2026

Microsoft Research Focus – Edition #97

Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research.

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