Abstract interpretation of Michelson smart-contracts
October 11, 2022 Β· Declared Dead Β· π SOAP@PLDI
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Guillaume Bau, Antoine MinΓ©, Vincent Botbol, Mehdi Bouaziz
arXiv ID
2210.05217
Category
cs.CR: Cryptography & Security
Cross-listed
cs.PL,
cs.SE
Citations
11
Venue
SOAP@PLDI
Last Checked
3 months ago
Abstract
Static analysis of smart-contracts is becoming more widespread on blockchain platforms. Analyzers rely on techniques like symbolic execution or model checking, but few of them can provide strong soundness properties and guarantee the analysis termination at the same time. As smart-contracts often manipulate economic assets, proving numerical properties beyond the absence of runtime errors is also desirable. Smart-contract execution models differ considerably from mainstream programming languages and vary from one blockchain to another, making state-of-the-art analyses hard to adapt. For instance, smart-contract calls may modify a persistent storage impacting subsequent calls. This makes it difficult for tools to infer invariants required to formally ensure the absence of exploitable vulnerabilities. The Michelson smart-contract language, used in the Tezos blockchain, is strongly typed, stack-based, and has a strict execution model leaving few opportunities for implicit runtime errors. We present a work in progress static analyzer for Michelson based on Abstract Interpretation and implemented within MOPSA, a modular static analyzer. Our tool supports the Michelson semantic features, including inner calls to external contracts. It can prove the absence of runtime errors and infer invariants on the persistent storage over an unbounded number of calls. It is also being extended to prove high-level numerical and security properties. CCS Concepts: $\bullet$ Security and privacy $\rightarrow$ Logic and verification; $\bullet$ Software and its engineering $\rightarrow$ Automated static analysis.
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
Membership Inference Attacks against Machine Learning Models
R.I.P.
π»
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
π»
Ghosted
Practical Black-Box Attacks against Machine Learning
R.I.P.
π»
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
π»
Ghosted
Extracting Training Data from Large Language Models
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Language Models are Few-Shot Learners
R.I.P.
π»
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
π»
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
π»
Ghosted