AI security analysis for smart contracts
Substrate-Agnostic
Vulnerability Detection
WizAud models what makes code exploitable independently of the language it is written in. Understanding gained in one ecosystem carries to every other.
Cross-Chain Knowledge Transfer
Security experience does not stay on the chain it was gained on. WizAud carries what it learns in one ecosystem into every other.
In a held-out test, a version trained with no exposure to the target language still located high-severity issues in it.
Capabilities
Coverage Without Audit History
New languages have little audit history to learn from. Through cross-language learning, WizAud brings experience from mature ecosystems to code that has never had it.
Targeted Analysis
Reading an entire codebase with a language model is slow and expensive. WizAud first identifies the regions most likely to be exploitable, then directs deep analysis only there, so compute goes where the risk is.
Confidence-Ranked Leads
Every lead is ranked by confidence and labelled with where the knowledge behind it came from. Your team sees the strongest signals first and decides how deep to go, without wading through noise.
Supported Languages
Cross-language learning means each language WizAud covers strengthens the others.
- Solidity
- Rust
- Go
- Move
- JavaScript
- Noir
- Cairo
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