Copertina

LLMSecConfig: An LLM-Based Approach for Fixing Software Container Misconfigurations

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ye, Ziyang, Le, Triet Huynh Minh, Babar, M. Ali
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Software Engineering Artificial Intelligence Cryptography and Security Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++
di: Nguyen, Anh The, et al.
Pubblicazione: (2024)

Securing the AI Supply Chain: What Can We Learn From Developer-Reported Security Issues and Solutions of AI Projects?
di: Nguyen, The Anh, et al.
Pubblicazione: (2025)

Automatic Data Labeling for Software Vulnerability Prediction Models: How Far Are We?
di: Le, Triet H. M., et al.
Pubblicazione: (2024)

Mitigating Data Imbalance for Software Vulnerability Assessment: Does Data Augmentation Help?
di: Le, Triet H. M., et al.
Pubblicazione: (2024)

MVD: A Multi-Lingual Software Vulnerability Detection Framework
di: Zhang, Boyu, et al.
Pubblicazione: (2024)

Are Latent Vulnerabilities Hidden Gems for Software Vulnerability Prediction? An Empirical Study
di: Le, Triet H. M., et al.
Pubblicazione: (2024)

Software Vulnerability Prediction in Low-Resource Languages: An Empirical Study of CodeBERT and ChatGPT
di: Le, Triet H. M., et al.
Pubblicazione: (2024)

A Qualitative Study on Using ChatGPT for Software Security: Perception vs. Practicality
di: Kholoosi, M. Mehdi, et al.
Pubblicazione: (2024)

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

AdaptiveGuard: Towards Adaptive Runtime Safety for LLM-Powered Software
di: Yang, Rui, et al.
Pubblicazione: (2025)

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

Software Vulnerability Management in the Era of Artificial Intelligence: An Industry Perspective
di: Kholoosi, M. Mehdi, et al.
Pubblicazione: (2025)

Tracking Software Security Topics
di: Vu, Phong Minh, et al.
Pubblicazione: (2024)

SCoPE: Evaluating LLMs for Software Vulnerability Detection
di: Gonçalves, José, et al.
Pubblicazione: (2024)

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

Evaluating LLaMA 3.2 for Software Vulnerability Detection
di: Gonçalves, José, et al.
Pubblicazione: (2025)

Containment Verification: AI Safety Guarantees Independent of Alignment
di: Moon, Royce, et al.
Pubblicazione: (2026)

Software Vulnerability Detection Using a Lightweight Graph Neural Network
di: Farmer, Miles, et al.
Pubblicazione: (2026)

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

An Empirically Grounded Reference Architecture for Software Supply Chain Metadata Management
di: Tran, Nguyen Khoi, et al.
Pubblicazione: (2023)

Implicit Patterns in LLM-Based Binary Analysis
di: Li, Qiang, et al.
Pubblicazione: (2026)

SecDOAR: A Software Reference Architecture for Security Data Orchestration, Analysis and Reporting
di: Chauhan, Muhammad Aufeef, et al.
Pubblicazione: (2024)

Fortifying LLM-Based Code Generation with Graph-Based Reasoning on Secure Coding Practices
di: Patir, Rupam, et al.
Pubblicazione: (2025)

SCDBench: A Benchmark for LLM-Based Smart Contract Decompilers
di: Qin, Kaihua, et al.
Pubblicazione: (2026)

Benchmark of Benchmarks: Unpacking Influence and Code Repository Quality in LLM Safety Benchmarks
di: Chu, Junjie, et al.
Pubblicazione: (2026)

Does Teaming-Up LLMs Improve Secure Code Generation? A Comprehensive Evaluation with Multi-LLMSecCodeEval
di: Sabir, Bushra, et al.
Pubblicazione: (2026)

Hackers or Hallucinators? A Comprehensive Analysis of LLM-Based Automated Penetration Testing
di: Peng, Jiaren, et al.
Pubblicazione: (2026)

Differentiated Directional Intervention A Framework for Evading LLM Safety Alignment
di: Zhang, Peng, et al.
Pubblicazione: (2025)

Centralized Defense: Logging and Mitigation of Kubernetes Misconfigurations with Open Source Tools
di: Russell, Eoghan, et al.
Pubblicazione: (2024)

Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture the Flag Challenges
di: Al-Kaswan, Ali, et al.
Pubblicazione: (2026)

Locus: Agentic Predicate Synthesis for Directed Fuzzing
di: Zhu, Jie, et al.
Pubblicazione: (2025)

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

DevOps-Gym: Benchmarking AI Agents in Software DevOps Cycle
di: Tang, Yuheng, et al.
Pubblicazione: (2026)

Automatically Generating Rules of Malicious Software Packages via Large Language Model
di: Zhang, XiangRui, et al.
Pubblicazione: (2025)

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

LinuxArena: A Control Setting for AI Agents in Live Production Software Environments
di: Tracy, Tyler, et al.
Pubblicazione: (2026)

Co-PatcheR: Collaborative Software Patching with Component(s)-specific Small Reasoning Models
di: Tang, Yuheng, et al.
Pubblicazione: (2025)

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

Investigating Detection and Obfuscation of Prompt Injection Attacks Against Software Reverse Engineering AI Agents
di: Crawford, Brian, et al.
Pubblicazione: (2026)

Safety and Performance, Why Not Both? Bi-Objective Optimized Model Compression against Heterogeneous Attacks Toward AI Software Deployment
di: Zhu, Jie, et al.
Pubblicazione: (2024)