Imagen de Portada

Needles at Scale: LLM-Assisted Target Selection for Windows Vulnerability Research

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autor principal:	Bommarito II, Michael J.
Formato:	Preprint
Publicado:	2026
Materias:	Cryptography and Security Artificial Intelligence Software Engineering
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection
por: Yan, Shenao, et al.
Publicado: (2024)

Zer0n: An AI-Assisted Vulnerability Discovery and Blockchain-Backed Integrity Framework
por: Parmar, Harshil, et al.
Publicado: (2026)

VULPO: Context-Aware Vulnerability Detection via On-Policy LLM Optimization
por: Li, Youpeng, et al.
Publicado: (2025)

From SFT to RL: Demystifying the Post-Training Pipeline for LLM-based Vulnerability Detection
por: Li, Youpeng, et al.
Publicado: (2026)

QLPro: Automated Code Vulnerability Discovery via LLM and Static Code Analysis Integration
por: Hu, Junze, et al.
Publicado: (2025)

Taint-Style Vulnerability Detection and Confirmation for Node.js Packages Using LLM Agent Reasoning
por: Ni, Ronghao, et al.
Publicado: (2026)

SecureFixAgent: A Hybrid LLM Agent for Automated Python Static Vulnerability Repair
por: Gajjar, Jugal, et al.
Publicado: (2025)

LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning
por: Sun, Yuqiang, et al.
Publicado: (2024)

Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
por: Mitropoulos, Dimitris, et al.
Publicado: (2026)

SOK: Exploring Hallucinations and Security Risks in AI-Assisted Software Development with Insights for LLM Deployment
por: Haque, Ariful, et al.
Publicado: (2025)

An Empirical Study of Vulnerabilities in Python Packages and Their Detection
por: Quan, Haowei, et al.
Publicado: (2025)

Favia: Forensic Agent for Vulnerability-fix Identification and Analysis
por: Storhaug, André, et al.
Publicado: (2026)

LLbezpeky: Leveraging Large Language Models for Vulnerability Detection
por: Mathews, Noble Saji, et al.
Publicado: (2024)

An Empirical Study of Vulnerability Detection using Federated Learning
por: Zhou, Peiheng, et al.
Publicado: (2024)

How Effective Are Neural Networks for Fixing Security Vulnerabilities
por: Wu, Yi, et al.
Publicado: (2023)

Harnessing the Power of LLMs in Source Code Vulnerability Detection
por: Mahyari, Andrew A
Publicado: (2024)

Detecting Vulnerabilities from Issue Reports for Internet-of-Things
por: Masoumzadeh, Sogol
Publicado: (2025)

MOSAIC-Bench: Measuring Compositional Vulnerability Induction in Coding Agents
por: Steinberg, Jonathan, et al.
Publicado: (2026)

Evaluating LLMs for One-Shot Patching of Real and Artificial Vulnerabilities
por: Garg, Aayush, et al.
Publicado: (2025)

Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation
por: Chu, Zhaoyang, et al.
Publicado: (2024)

From Detection to Prevention: Explaining Security-Critical Code to Avoid Vulnerabilities
por: Krishnamurthy, Ranjith, et al.
Publicado: (2026)

Knowdit: Agentic Smart Contract Vulnerability Detection with Auditing Knowledge Summarization
por: Kong, Ziqiao, et al.
Publicado: (2026)

Focus on What Matters: Fisher-Guided Adaptive Multimodal Fusion for Vulnerability Detection
por: Bian, Yun, et al.
Publicado: (2026)

A Comprehensive Study of Exploitable Patterns in Smart Contracts: From Vulnerability to Defense
por: Ding, Yuchen, et al.
Publicado: (2025)

Bridging Semantics & Structure for Software Vulnerability Detection using Hybrid Network Models
por: Gajjar, Jugal, et al.
Publicado: (2025)

Data and Context Matter: Towards Generalizing AI-based Software Vulnerability Detection
por: Safdar, Rijha, et al.
Publicado: (2025)

MAVUL: Multi-Agent Vulnerability Detection via Contextual Reasoning and Interactive Refinement
por: Li, Youpeng, et al.
Publicado: (2025)

Code Security Vulnerability Repair Using Reinforcement Learning with Large Language Models
por: Islam, Nafis Tanveer, et al.
Publicado: (2024)

GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis
por: Sun, Yuqiang, et al.
Publicado: (2023)

Broken by Default: A Formal Verification Study of Security Vulnerabilities in AI-Generated Code
por: Blain, Dominik, et al.
Publicado: (2026)

AutoEG: Exploiting Known Third-Party Vulnerabilities in Black-Box Web Applications
por: Yang, Ruozhao, et al.
Publicado: (2026)

CodeHacker: Automated Test Case Generation for Detecting Vulnerabilities in Competitive Programming Solutions
por: Shi, Jingwei, et al.
Publicado: (2026)

Harnessing Large Language Models for Software Vulnerability Detection: A Comprehensive Benchmarking Study
por: Tamberg, Karl, et al.
Publicado: (2024)

Explainable Vulnerability Detection in C/C++ Using Edge-Aware Graph Attention Networks
por: Haque, Radowanul, et al.
Publicado: (2025)

VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models
por: Liu, Yu, et al.
Publicado: (2024)

EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability Detection
por: Liu, Shigang, et al.
Publicado: (2024)

Detecting Data Poisoning in Code Generation LLMs via Black-Box, Vulnerability-Oriented Scanning
por: Yan, Shenao, et al.
Publicado: (2026)

MalCodeAI: Autonomous Vulnerability Detection and Remediation via Language Agnostic Code Reasoning
por: Gajjar, Jugal, et al.
Publicado: (2025)

SAEL: Leveraging Large Language Models with Adaptive Mixture-of-Experts for Smart Contract Vulnerability Detection
por: Yu, Lei, et al.
Publicado: (2025)

Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale
por: Liu, Yi, et al.
Publicado: (2026)