Detection and Explanation of PowerShell Malware with Large Language Models

SCRIPTIOC-BENCH: A Benchmark for Recognizing Actionable Threat Intelligence from Script-Based Malware using LLMs

Script-based malware remains a prevalent attack technique. These scripts often contain indicators of compromise (IOCs) that provide actionable threat intelligence. However, statically recovering such indicators is challenging, as relevant values may be dispersed or transformed within code. Although large language model...

To Compare Various Frameworks that Integrate XAI, GANs and LLMs for Dynamic Malware Behavior Analysis

A unified approach of malware analysis incorpo-rating XAI, GAN and LLM is proposed to enable the development of more effective malware detection tools with more transparency, deeper analytical insight and advanced forensic decision-making.

Bolstering Binary Datasets for Malware Detection Through Programmatic Data Augmentation

LLM-Based Detection and Test Generation for Command Injection Vulnerabilities in Python

Command injection vulnerabilities remain a significant security threat in dynamic languages such as Python, particularly in widely used open-source projects. Recent advances in large language models (LLMs) have shown strong potential in code-related tasks, motivating their application to vulnerability detection.In this...

Detecting Contaminated Code-Generation Prompt Batches via Influence Functions

Large language models (LLMs) are increasingly used for code generation, yet they remain vulnerable to prompts that elicit insecure implementations. Existing defenses typically rely on predefined threat models or known vulnerability patterns, limiting their effectiveness against novel attacks. We propose CodeSIFT, a thr...

MalTotal: Cost-Effective and Language-Agnostic Malicious Code Poisoning Detection for Millions of Repositories

MalTotal leverages LLM-assisted semantic reasoning to identify sensitive APIs, perform hybrid semantic slicing, and reconstruct malicious behavior contexts while reducing analysis overhead, demonstrating the effectiveness, scalability, and cost-efficiency of MalTotal in mitigating large-scale code poisoning attacks.