Thousands of smart contracts are deployed daily, each a potential target for exploits. Manual audits can't keep up with the flow. The damage from flash loan attacks exceeded $1 billion in the last two years. We built an AI system that scans code, identifies reentrancy, overflow, oracle manipulation, and other vulnerabilities in seconds. Average savings per project: $20,000 to $100,000. Typical damage from reentrancy attacks runs into millions. Our solution reduces audit costs by up to 80%, saving $20,000 per project on average. Leave your request — we'll conduct a free trial scan of your contract. For instance, in a recent DeFi project, our AI detected a critical reentrancy vulnerability that manual review missed, preventing a potential loss of $5 million.
What Vulnerabilities We Detect
| Class | Attack Example | Detection Method |
|---|---|---|
| Reentrancy | The DAO ($60M), Cream Finance ($130M) | Call graph analysis — external calls before state changes. Static taint analysis from balance to call |
| Integer Overflow/Underflow | DoS pre-Solidity 0.8, unchecked blocks | Detect unchecked{} with arithmetic; symbolic execution (Manticore, Echidna) |
| Price Oracle Manipulation | Flash loan + single oracle | Analyze block.timestamp, tx.origin, single sources. Recommend TWAP |
| Access Control | Public mint/withdraw, missing modifiers | Check all privileged functions for modifiers |
| Flash Loan Attack Surface | Single-transaction manipulation | Analyze business logic for disproportionate advantage |
Based on OpenZeppelin smart contract security report
Why Combining Methods Delivers the Best Results
Symbolic execution (Manticore, Mythril) explores all possible code paths. Its drawback: state explosion on complex contracts. Fuzzing (Echidna, Foundry) generates random inputs and is faster, but coverage is incomplete. Our pipeline combines both: first fuzzing for quick reconnaissance, then symbolic execution for suspicious areas. The result — 40% higher coverage compared to each method alone.
For ML detection, we use CodeBERT — code embeddings plus a classifier per vulnerability type. We train on a dataset of 5,000+ verified vulnerable contracts. Processing speed: seconds per contract. Our AI system is 100 times faster than manual audits, reducing costs by 80%.
Detection Method Comparison
| Method | Speed | Coverage | False Positives |
|---|---|---|---|
| Symbolic execution | Minutes per contract | All paths (theoretically) | Low |
| Fuzzing | Seconds per contract | Random paths | Medium |
| ML (CodeBERT) | <5 seconds per contract | Trainable patterns | High, but filtered |
How AI Detection Scales to Thousands of Contracts
Manual audit of one contract takes 2–5 days. Our AI system checks 1,000 contracts in an hour. The ML model processes each contract in under 5 seconds. For complex cases (e.g., contracts with delegate calls), the system switches to hybrid mode with symbolic execution — this takes up to 15 minutes but provides verification.
How the AI System Integrates into CI/CD
We provide ready-made GitHub Actions and GitLab CI templates. Setup takes one hour. On each commit, scanning runs, results appear in the Pull Request. If a critical vulnerability is found, the pipeline blocks. This catches issues before deployment.
What Continuous Monitoring Delivers
A one-time audit protects only at the moment of review. Continuous monitoring tracks on-chain transactions and alerts on anomalies: unusual calls, flash loan attacks, oracle manipulation. Alerts arrive in Slack/Telegram within minutes. You can react before damage becomes critical.
What Is Included in the Work
- Source code and ABI analysis (support Solidity 0.4–0.8+, Vyper, Yul)
- Detailed report on each vulnerability (CWE classification, PoC, recommendations)
- CI/CD integration (GitHub Actions, GitLab CI) for automatic scanning on every commit
- Continuous monitoring (on-chain alerts, Slack/Telegram notifications)
- Team training on interpreting results and best practices
- Documentation of the pipeline and access to a dashboard with real-time metrics
- Post-deployment support for 30 days with priority response
Process Overview
- Analytics — gather requirements and upload contracts into a protected environment.
- Design — configure the pipeline (model weights, alert thresholds).
- Implementation — run scanning, parallel execution on GPU cluster. Typical SLA — 24 hours per contract up to 1,000 lines.
- Testing — verify false positives via symbolic execution.
- Deployment — set up monitoring and CI/CD pipeline.
Indicative Timelines
| Scope | Timeline |
|---|---|
| Single contract (up to 500 lines) | from 1 day |
| Portfolio up to 50 contracts | 5 to 10 business days |
| Continuous monitoring | Integration in 2 days |
The cost is calculated individually — contact us for a project assessment. Typical pricing starts at $5,000 per contract (under 500 lines) and scales with complexity. Our experience: over 20 projects in DeFi, certification in Solidity and EVM.
Typical Mistakes When Adopting AI Auditing
- Expecting 100% accuracy — false positives are inevitable; manual review of top 10 alerts is mandatory.
- Neglecting continuous monitoring — a one-time audit doesn't protect against new attacks after deployment.
- Using a single method (only fuzzing or only ML) — combining methods provides the best coverage.
Get a consultation: we'll evaluate your stack and select the optimal pipeline. Confidentiality guaranteed — we sign NDAs.