Preventive security for world's most widely used crypto-wallet
About The Project
Metaguard is a preventive-security system (also made available as a MetaMask Snap) that helps users detect scams and risky transactions before signing. Instead of relying solely on contract reputation scores, Metaguard performs a full transaction simulation on a local EVM fork, analysing execution traces and state changes to surface hidden risks.
The system highlights security insights such as ownership transfers, balance drains, unverified contracts, and suspicious code paths in a clear, user-friendly format. By combining simulation, static analysis, and external security data, Metaguard enables users to make informed decisions and avoid fraudulent NFTs, fake tokens, and malicious approvals.
System Architecture
System Architecture diagram
Key Challenges
SCAM DETECTION LAG
New scams often go undetected for days, causing widespread losses before awareness spreads
BYPASSABLE REPUTATION CHECKS
Contract-based scoring systems are easy for attackers to evade
COMPLEX TRANSACTION BEHAVIOUR
Many malicious actions only emerge during execution, not static inspection
USER COMPREHENSION
Security findings must be explained clearly to non-technical users
ECOSYSTEM SCALE
The solution must work seamlessly inside wallet UX constraints
Our Solution
TRANSACTION SIMULATION ENGINE
Simulates transactions on a local EVM fork to capture execution traces and state changes.
TRACE & STATE ANALYSIS
Analyses full transaction traces to detect balance drains, ownership transfers, and hidden side effects.
SECURITY INTELLIGENCE INTEGRATIONS
Aggregates insights from Etherscan, Alchemy, public blacklists, and audit data.
STATIC ANALYSIS LAYER
Incorporates industry-standard tools to identify vulnerabilities in smart contract code.
CROWDSOURCED THREAT SIGNALS
Enables rapid scam detection through community feedback and shared intelligence.
WALLET-NATIVE UX
Surfaces security warnings directly inside MetaMask using Snap-compatible UI patterns.
The Result
EARLY SCAM PREVENTION
Reduced scam exposure by identifying malicious behaviour before transaction execution.
INFORMED USER DECISIONS
Translated complex security signals into clear, actionable insights for end users.
FASTER THREAT AWARENESS
Shortened detection window for new scams from days to hours through simulation and data aggregation.
ECOSYSTEM IMPACT
Encouraged better developer practices and discouraged interaction with opaque or unsafe contracts.
Similar Projects
- Pre-sign transaction risk engine for an on-chain trading DAO
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