3. Deep Contract Analysis
3.1 Automated Code Scanning
We use advanced AI to parse Solana’s Rust-based smart contracts, analyzing code semantics for potential vulnerabilities. Our code-scanning engine looks for:
Re-entrancy risks.
Overflow and underflow issues.
Race conditions in concurrent transactions.
3.2 Smart Contract Security Framework
Security is enforced by a multi-layered approach:
Static Analysis: Detecting code smells in the contract repository.
Dynamic Analysis: Testnet deployments under simulated stress conditions.
Formal Verification: Using mathematical proofs to verify that the contract’s logic adheres to specified invariants (e.g., ensuring token balances never go negative).
3.3 Mathematical Model for Vulnerability Detection
We model potential vulnerability surfaces by assigning a risk coefficient RRR to each contract function:
R=∑i=1n(αi⋅vi)R = \sum_{i=1}^{n} \bigl(\alpha_i \cdot v_i \bigr)R=i=1∑n(αi⋅vi)
Where:
viv_ivi is a specific vulnerability vector (e.g., re-entrancy, unchecked external call).
αi\alpha_iαi is the weighted impact factor, derived from historical exploit data.
This yields a final risk score RRR that triggers additional security checks if a threshold is exceeded.
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