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

This work presents SCRIPTIOC-BENCH, a benchmark for measuring static IOC extraction capability on real-world malicious scripts, and evaluates a broad range of proprietary and open-weight LLMs, showing that IOC recovery without execution remains challenging across model scales.

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

The proposed CodeSIFT, a threat-model-agnostic detection method that leverages influence functions to identify batches of prompts that induce anomalous model behavior, suggests that influence-function-based detection is a promising direction for identifying malicious code-generation prompts without requiring prior know...

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.