https://github.com/Avmb/semantic_neq_game
공식 코드Program Semantic Inequivalence Game with Large Language Models
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Vulnerability detection plays a crucial role in safeguarding against these threats by identifying weaknesses and potential entry points that malicious actors could exploit. Through advanced scanning techniques and penetration testing, vulnerability detection tools meticulously analyze web applications and websites for vulnerabilities such as SQL injection, cross-site scripting (XSS), and insecure authentication mechanisms.
By proactively identifying and addressing vulnerabilities, organizations can strengthen their online security posture and mitigate the risk of data breaches, financial loss, and reputational damage. Additionally, vulnerability detection empowers businesses to stay compliant with industry regulations and standards, demonstrating their commitment to safeguarding sensitive information and maintaining the trust of their customers. With the evolving threat landscape and increasingly sophisticated attack vectors, investing in robust vulnerability detection measures is paramount for staying one step ahead of cyber threats and ensuring the resilience of web-based platforms and services.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Program Semantic Inequivalence Game with Large Language Models
CASTLE: Benchmarking Dataset for Static Code Analyzers and LLMs towards CWE Detection
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning
How to Select Pre-Trained Code Models for Reuse? A Learning Perspective
Is Function Similarity Over-Engineered? Building a Benchmark
Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection
MTVHunter: Smart Contracts Vulnerability Detection Based on Multi-Teacher Knowledge Translation
Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection
Vulnerability Detection via Topological Analysis of Attention Maps
SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities
Machine Learning Techniques for Python Source Code Vulnerability Detection
VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMs
Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation
ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection
Vulnerability Detection with Code Language Models: How Far Are We?
Old but Gold: Reconsidering the value of feedforward learners for software analytics
Large Language Model-Powered Smart Contract Vulnerability Detection: New Perspectives
Large Language Models for Cyber Security: A Systematic Literature Review
How Far Have We Gone in Vulnerability Detection Using Large Language Models
D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis
VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection
Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity Analysis
Learning Program Semantics with Code Representations: An Empirical Study
Enhancing Large Language Models with Faster Code Preprocessing for Vulnerability Detection
LIVABLE: Exploring Long-Tailed Classification of Software Vulnerability Types
GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.