Vulnerability Detection
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/niklasrisse/usenix_2024
공식 코드Uncovering the Limits of Machine Learning for Automatic Vulnerability Detection
https://github.com/niklasrisse/vpp
공식 코드Uncovering the Limits of Machine Learning for Automatic Vulnerability Detection
https://github.com/niklasrisse/vpp_label_accuracy
공식 코드Uncovering the Limits of Machine Learning for Automatic Vulnerability Detection
https://github.com/nyu-llm-ctf/llm_ctf_automation
공식 코드NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
https://github.com/nyu-llm-ctf/llm_ctf_database
공식 코드NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
https://github.com/nyu-llm-ctf/nyu_ctf_bench
공식 코드NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
https://github.com/nyu-llm-ctf/nyuctf_agents_craken
공식 코드CRAKEN: Cybersecurity LLM Agent with Knowledge-Based Execution
https://github.com/optimatch/optimatch
공식 코드Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities
https://github.com/pial08/semvuldet
공식 코드An Unbiased Transformer Source Code Learning with Semantic Vulnerability Graph
https://github.com/pilot-vd-2023/pilot
공식 코드When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection
https://github.com/rmusab/vul-llm-finetune
공식 코드Finetuning Large Language Models for Vulnerability Detection
https://github.com/rockmao45/llmvulndetection
공식 코드Multi-role Consensus through LLMs Discussions for Vulnerability Detection
https://github.com/rufimelo99/SAE-Java-Bug-Detection
공식 코드Are Sparse Autoencoders Useful for Java Function Bug Detection?
https://github.com/rymarinelli/wormy_ai
공식 코드https://github.com/sakirinn/llm4cvd
공식 코드Investigating Large Language Models for Code Vulnerability Detection: An Experimental Study
https://github.com/sap/project-kb
공식 코드A Survey on Machine Learning Techniques for Source Code Analysis
https://github.com/seclab-ucr/buglens
공식 코드The Hitchhiker's Guide to Program Analysis, Part II: Deep Thoughts by LLMs
https://github.com/shangqing-liu/graphsearchnet
공식 코드GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search
https://github.com/sweetaroo/vuldetectbench
공식 코드VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models
https://github.com/timothee-chauvin/eyeballvul_experiments
공식 코드eyeballvul: a future-proof benchmark for vulnerability detection in the wild
https://github.com/tjunlp-lab/aprt
공식 코드https://github.com/vannguyennd/dam2p
공식 코드Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin Principle
https://github.com/vannguyennd/livuitcl
공식 코드https://github.com/wagner-group/diversevul
공식 코드DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection
https://github.com/web3se-lab/web3-sekit
공식 코드https://github.com/williamadahl/RNN-for-Vulnerability-Detection
공식 코드Stack-based Buffer Overflow Detection using Recurrent Neural Networks
https://github.com/wolong3385/EatVul-Resources
공식 코드EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability Detection
https://github.com/xin-cheng-wen/po4vul
공식 코드https://gitlab.com/web3se/smartintent
공식 코드https://zenodo.org/record/7608802
공식 코드https://zenodo.org/record/zenodo.8206635
공식 코드Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic Datasets
https://github.com/CUMLSec/trex
Trex: Learning Execution Semantics from Micro-Traces for Binary Similarity
https://github.com/NIPC-DL/covec
SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities
https://github.com/NIPC-DL/torchplp
SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities
https://github.com/OSUSecLab/SymLM
Trex: Learning Execution Semantics from Micro-Traces for Binary Similarity
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.