Vulnerability Detection
51 papers with code • 2 benchmarks • 6 datasets
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.
Benchmarks
These leaderboards are used to track progress in Vulnerability Detection
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Libraries
Use these libraries to find Vulnerability Detection models and implementationsDatasets
CVEfixes
RealVul
Most implemented papers
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection.
SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities
Our experiments with 4 software products demonstrate the usefulness of the framework: we detect 15 vulnerabilities that are not reported in the National Vulnerability Database.
SAFE: Self-Attentive Function Embeddings for Binary Similarity
gadiluna/SAFE
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We report the results from a quantitative and qualitative analysis that show how SAFE provides a noticeable performance improvement with respect to previous solutions.
Automated Vulnerability Detection in Source Code Using Deep Representation Learning
The labeled dataset is available at: https://osf. io/d45bw/.
Introducing the Robot Vulnerability Database (RVD)
Cybersecurity in robotics is an emerging topic that has gained significant traction.
Trex: Learning Execution Semantics from Micro-Traces for Binary Similarity
CUMLSec/trex
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We thus train the model to learn execution semantics from the functions' micro-traces, without any manual labeling effort.
NYU CTF Dataset: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
Large Language Models (LLMs) are being deployed across various domains today.
AndroShield: Automated Android Applications Vulnerability Detection, a Hybrid Static and Dynamic Analysis Approach
The security of mobile applications has become a major research field which is associated with a lot of challenges.
Stack-based Buffer Overflow Detection using Recurrent Neural Networks
Moreover, we subscribe to the hypothesis that code may be treated as natural language, and thus we process assembly code using standard architectures commonly employed in natural language processing.
Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts
Therefore, Eth2Vec can detect vulnerabilities in smart contracts by comparing the code similarity between target EVM bytecodes and the EVM bytecodes it already learned.
