Vyper: A Security Comparison with Solidity Based on Common Vulnerabilities
March 16, 2020 Β· Declared Dead Β· π Conference on Blockchain Research & Applications for Innovative Networks and Services
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Mudabbir Kaleem, Anastasia Mavridou, Aron Laszka
arXiv ID
2003.07435
Category
cs.CR: Cryptography & Security
Citations
41
Venue
Conference on Blockchain Research & Applications for Innovative Networks and Services
Last Checked
6 months ago
Abstract
Vyper has been proposed as a new high-level language for Ethereum smart contract development due to numerous security vulnerabilities and attacks witnessed on contracts written in Solidity since the system's inception. Vyper aims to address these vulnerabilities by providing a language that focuses on simplicity, auditability and security. We present a survey where we study how well-known and commonly-encountered vulnerabilities in Solidity feature in Vyper's development environment. We analyze all such vulnerabilities individually and classify them into five groups based on their status in Vyper. To the best of our knowledge, our survey is the first attempt to study security vulnerabilities in Vyper.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Cryptography & Security
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
π»
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
π»
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
π»
Ghosted
How To Backdoor Federated Learning
R.I.P.
π»
Ghosted
Evasion Attacks against Machine Learning at Test Time
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted