Is OpenGDP Shared Security Safe?
Risk Grade: C+ (40/100)
OpenGDP Shared Security is rated as elevated risk — multiple novel mechanisms and notable interaction risks.
Elevated risk — novel multi-asset restaking with leverage-like exposure and cross-chain dependencies, partially offset by institutional-grade security ambitions.
OpenGDP (formerly Karak Network) is a multi-asset restaking protocol allowing users to restake ETH, WBTC, and stablecoins to secure decentralized services with $34M TVL. Its C+ grade reflects novel multi-asset and multi-chain restaking creating leverage-like exposure, cross-chain bridge dependencies, and execution risk from a recent rebrand and L1 pivot.
TVL
$30M
Mechanisms
5
Interactions
4
Value Grade
D-
Key Risks for OpenGDP Shared Security Users
Restaking the same collateral to back multiple services creates leverage-like risk. If multiple services slash simultaneously, losses could be much larger than expected.
Multi-chain restaking depends on cross-chain messaging. If bridges go down, security guarantees may not be enforceable.
The project recently rebranded from Karak to OpenGDP and is building an L1. This dual focus creates execution risk.
The GDP token is new and its value depends on growing demand for shared security services.
Top Risk Factors
- •Multi-asset restaking creates correlated slashing risk — if a DSS has issues, restaked ETH, WBTC, and stablecoins could be simultaneously slashed.
- •Same collateral backing multiple DSS creates leverage-like risk where one validator failure cascades across all services.
- •Recent rebrand from Karak to OpenGDP and pivot to L1 introduces execution risk.
- •Multi-chain restaking introduces bridge dependencies for staking operations.
Risk Score Breakdown
OpenGDP Shared Security's highest risk area is Vitality Risk (8/10). Here's how each dimension contributes to the overall 40/100 score:
Read the Full OpenGDP Shared Security Risk Report
This protocol has 2 collapse scenarios. 2 high-severity interaction risks identified. See the full mechanism classification, interaction matrix, and deep-dive recommendations.
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