Copertina

Zer0n: An AI-Assisted Vulnerability Discovery and Blockchain-Backed Integrity Framework

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Parmar, Harshil, Vyas, Pushti, Khristi, Prayers, Panchal, Priyank
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Cryptography and Security Artificial Intelligence Software Engineering
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Needles at Scale: LLM-Assisted Target Selection for Windows Vulnerability Research
di: Bommarito II, Michael J.
Pubblicazione: (2026)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Dynamic Feature Fusion: Combining Global Graph Structures and Local Semantics for Blockchain Fraud Detection
di: Sheng, Zhang, et al.
Pubblicazione: (2025)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detection
di: Yu, Lei, et al.
Pubblicazione: (2025)