Saved in:
| Main Authors: | Qin, Siliang, Yang, Fengrui, Wang, Hao, Zhang, Bolun, Gao, Zeyu, Zhang, Chao, Chen, Kai |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.13323 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Mono: Is Your "Clean" Vulnerability Dataset Really Solvable? Exposing and Trapping Undecidable Patches and Beyond
by: Gao, Zeyu, et al.
Published: (2025)
by: Gao, Zeyu, et al.
Published: (2025)
SecCodeBench-V2 Technical Report
by: Chen, Longfei, et al.
Published: (2026)
by: Chen, Longfei, et al.
Published: (2026)
VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models
by: Liu, Yu, et al.
Published: (2024)
by: Liu, Yu, et al.
Published: (2024)
LLAMAFUZZ: Large Language Model Enhanced Greybox Fuzzing
by: Zhang, Hongxiang, et al.
Published: (2024)
by: Zhang, Hongxiang, et al.
Published: (2024)
False Friends in the Shell: Unveiling the Emoticon Semantic Confusion in Large Language Models
by: Jiang, Weipeng, et al.
Published: (2026)
by: Jiang, Weipeng, et al.
Published: (2026)
Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation
by: Chu, Zhaoyang, et al.
Published: (2024)
by: Chu, Zhaoyang, et al.
Published: (2024)
PatUntrack: Automated Generating Patch Examples for Issue Reports without Tracked Insecure Code
by: Jiang, Ziyou, et al.
Published: (2024)
by: Jiang, Ziyou, et al.
Published: (2024)
Automatically Generating Rules of Malicious Software Packages via Large Language Model
by: Zhang, XiangRui, et al.
Published: (2025)
by: Zhang, XiangRui, et al.
Published: (2025)
Real Money, Fake Models: Deceptive Model Claims in Shadow APIs
by: Zhang, Yage, et al.
Published: (2026)
by: Zhang, Yage, et al.
Published: (2026)
The Invisible Hand: Unveiling Provider Bias in Large Language Models for Code Generation
by: Zhang, Xiaoyu, et al.
Published: (2025)
by: Zhang, Xiaoyu, et al.
Published: (2025)
Dynamic Feature Fusion: Combining Global Graph Structures and Local Semantics for Blockchain Fraud Detection
by: Sheng, Zhang, et al.
Published: (2025)
by: Sheng, Zhang, et al.
Published: (2025)
Zero-Permission Manipulation: Can We Trust Large Multimodal Model Powered GUI Agents?
by: Qian, Yi, et al.
Published: (2026)
by: Qian, Yi, et al.
Published: (2026)
KernelGPT: Enhanced Kernel Fuzzing via Large Language Models
by: Yang, Chenyuan, et al.
Published: (2023)
by: Yang, Chenyuan, et al.
Published: (2023)
Rethinking and Exploring String-Based Malware Family Classification in the Era of LLMs and RAG
by: Chen, Yufan, et al.
Published: (2025)
by: Chen, Yufan, et al.
Published: (2025)
Towards Secure and Explainable Smart Contract Generation with Security-Aware Group Relative Policy Optimization
by: Yu, Lei, et al.
Published: (2025)
by: Yu, Lei, et al.
Published: (2025)
Smart-LLaMA: Two-Stage Post-Training of Large Language Models for Smart Contract Vulnerability Detection and Explanation
by: Yu, Lei, et al.
Published: (2024)
by: Yu, Lei, et al.
Published: (2024)
DecompileBench: A Comprehensive Benchmark for Evaluating Decompilers in Real-World Scenarios
by: Gao, Zeyu, et al.
Published: (2025)
by: Gao, Zeyu, et al.
Published: (2025)
Repository-Level Graph Representation Learning for Enhanced Security Patch Detection
by: Wen, Xin-Cheng, et al.
Published: (2024)
by: Wen, Xin-Cheng, et al.
Published: (2024)
SCDBench: A Benchmark for LLM-Based Smart Contract Decompilers
by: Qin, Kaihua, et al.
Published: (2026)
by: Qin, Kaihua, et al.
Published: (2026)
SAEL: Leveraging Large Language Models with Adaptive Mixture-of-Experts for Smart Contract Vulnerability Detection
by: Yu, Lei, et al.
Published: (2025)
by: Yu, Lei, et al.
Published: (2025)
LLM-enabled Applications Require System-Level Threat Monitoring
by: Zhang, Yedi, et al.
Published: (2026)
by: Zhang, Yedi, et al.
Published: (2026)
Decaf: Improving Neural Decompilation with Automatic Feedback and Search
by: Shypula, Alexander, et al.
Published: (2026)
by: Shypula, Alexander, et al.
Published: (2026)
How Effective Are Neural Networks for Fixing Security Vulnerabilities
by: Wu, Yi, et al.
Published: (2023)
by: Wu, Yi, et al.
Published: (2023)
Hackers or Hallucinators? A Comprehensive Analysis of LLM-Based Automated Penetration Testing
by: Peng, Jiaren, et al.
Published: (2026)
by: Peng, Jiaren, et al.
Published: (2026)
Focus on What Matters: Fisher-Guided Adaptive Multimodal Fusion for Vulnerability Detection
by: Bian, Yun, et al.
Published: (2026)
by: Bian, Yun, et al.
Published: (2026)
Empirical Study of Code Large Language Models for Binary Security Patch Detection
by: Li, Qingyuan, et al.
Published: (2025)
by: Li, Qingyuan, et al.
Published: (2025)
Scrub It Out! Erasing Sensitive Memorization in Code Language Models via Machine Unlearning
by: Chu, Zhaoyang, et al.
Published: (2025)
by: Chu, Zhaoyang, et al.
Published: (2025)
PentestEval: Benchmarking LLM-based Penetration Testing with Modular and Stage-Level Design
by: Yang, Ruozhao, et al.
Published: (2025)
by: Yang, Ruozhao, et al.
Published: (2025)
Benchmark of Benchmarks: Unpacking Influence and Code Repository Quality in LLM Safety Benchmarks
by: Chu, Junjie, et al.
Published: (2026)
by: Chu, Junjie, et al.
Published: (2026)
AutoEG: Exploiting Known Third-Party Vulnerabilities in Black-Box Web Applications
by: Yang, Ruozhao, et al.
Published: (2026)
by: Yang, Ruozhao, et al.
Published: (2026)
Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detection
by: Yu, Lei, et al.
Published: (2025)
by: Yu, Lei, et al.
Published: (2025)
MASKDROID: Robust Android Malware Detection with Masked Graph Representations
by: Zheng, Jingnan, et al.
Published: (2024)
by: Zheng, Jingnan, et al.
Published: (2024)
SKILLS: Structured Knowledge Injection for LLM-Driven Telecommunications Operations
by: Brett, Ivo
Published: (2026)
by: Brett, Ivo
Published: (2026)
CoDe-R: Refining Decompiler Output with LLMs via Rationale Guidance and Adaptive Inference
by: Zhang, Qiang, et al.
Published: (2026)
by: Zhang, Qiang, et al.
Published: (2026)
LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning
by: Sun, Yuqiang, et al.
Published: (2024)
by: Sun, Yuqiang, et al.
Published: (2024)
EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability Detection
by: Liu, Shigang, et al.
Published: (2024)
by: Liu, Shigang, et al.
Published: (2024)
RefleXGen:The unexamined code is not worth using
by: Wang, Bin, et al.
Published: (2025)
by: Wang, Bin, et al.
Published: (2025)
Bridging Semantics & Structure for Software Vulnerability Detection using Hybrid Network Models
by: Gajjar, Jugal, et al.
Published: (2025)
by: Gajjar, Jugal, et al.
Published: (2025)
Implicit Patterns in LLM-Based Binary Analysis
by: Li, Qiang, et al.
Published: (2026)
by: Li, Qiang, et al.
Published: (2026)
Measuring the Permission Gate: A Stress-Test Evaluation of Claude Code's Auto Mode
by: Ji, Zimo, et al.
Published: (2026)
by: Ji, Zimo, et al.
Published: (2026)
Similar Items
-
Mono: Is Your "Clean" Vulnerability Dataset Really Solvable? Exposing and Trapping Undecidable Patches and Beyond
by: Gao, Zeyu, et al.
Published: (2025) -
SecCodeBench-V2 Technical Report
by: Chen, Longfei, et al.
Published: (2026) -
VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models
by: Liu, Yu, et al.
Published: (2024) -
LLAMAFUZZ: Large Language Model Enhanced Greybox Fuzzing
by: Zhang, Hongxiang, et al.
Published: (2024) -
False Friends in the Shell: Unveiling the Emoticon Semantic Confusion in Large Language Models
by: Jiang, Weipeng, et al.
Published: (2026)