The DOI matches its Crossref record.
Manticore: A User-Friendly Symbolic Execution Framework for Binaries and Smart Contracts
Open accessAbstract
An effective way to maximize code coverage in software tests is through dynamic symbolic execution-a technique that uses constraint solving to systematically explore a program's state space. We introduce an open-source dynamic symbolic execution framework called Manticore for analyzing binaries and Ethereum smart contracts. Manticore's flexible architecture allows it to support both traditional and exotic execution environments, and its API allows users to customize their analysis. Here, we discuss Manticore's architecture and demonstrate the capabilities we have used to find bugs and verify the correctness of code for our commercial clients.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Empirical review of automated analysis tools on 47,587 Ethereum smart contracts
Durieux, T., Ferreira, J. F., Abreu, R., & Cruz, P. (2020) · Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering · doi:10.1145/3377811.3380364 · Cited by 409
Smart Contract: Attacks and Protections
Sayeed, S., Marco-Gisbert, H., & Caira, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970495 · Cited by 322
DepFuzz: Efficient Smart Contract Fuzzing with Function Dependence Guidance
Ma, C., Song, W., & Huang, J. (2025) · Proceedings of the ACM on Programming Languages · doi:10.1145/3763185 · Cited by 4
Peng, C., Jiang, M., Zhou, Y., & Wu, L. (2026) · Proceedings of the ACM on Software Engineering · doi:10.1145/3808141
Metadata
- Authors
- Mossberg, M., Manzano, F., Hennenfent, E., Groce, A., Grieco, G., Feist, J., Brunson, T., & Dinaburg, A.
- Venue
- 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)
- Publisher
- IEEE
- Published
- 2019-11
- Pages
- 1186-1189
- OpenAlex
- W2962497757
- Cited by
- 391
- References
- 0
- FWCI
- 23.44
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Manticore: A User-Friendly Symbolic Execution Framework for Binaries and Smart Contracts. https://app.cochat.ai/papers/10.1109/ase.2019.00133
- The paper
- Mossberg, M., Manzano, F., Hennenfent, E., Groce, A., Grieco, G., Feist, J., Brunson, T., & Dinaburg, A. (2019). Manticore: A User-Friendly Symbolic Execution Framework for Binaries and Smart Contracts. 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE). https://doi.org/10.1109/ase.2019.00133
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
