Is Abracadabra Safe?
Risk Grade: D+ (62/100)
Abracadabra is rated as high risk — extreme novelty, critical interactions, unproven at scale.
High risk — three exploits in two years and a stablecoin that repeatedly breaks its peg demonstrate systemic smart contract quality issues
A lending platform where users deposit crypto to borrow the MIM stablecoin. It holds $11M in deposits, down significantly from peak levels after three separate hacks since January 2024 totaling over $21M in losses. Its D+ grade reflects the pattern of recurring smart contract vulnerabilities and a stablecoin that dropped to $0.76 during one incident.
TVL
$9M
Mechanisms
7
Interactions
6
Value Grade
D-
Key Risks for Abracadabra Users
Three separate hacks since January 2024 stole a combined $21M+ from the protocol. The same type of vulnerability in the batch transaction system (cook() function) was exploited twice.
The MIM stablecoin crashed to $0.76 during the January 2024 exploit. If another hack occurs, MIM holders could lose 20% or more overnight.
Collateral depends on external protocols like GMX and Yearn. If those protocols are exploited, Abracadabra's Cauldrons inherit the losses as bad debt.
Top Risk Factors
- •Three major exploits in under two years ($6.5M Jan 2024, $13M Mar 2025, $1.8M Oct 2025) demonstrate a pattern of recurring smart contract vulnerabilities in the Cauldron architecture.
- •MIM stablecoin depegged to $0.76 during the January 2024 exploit, showing that the peg mechanism cannot withstand protocol-level shocks.
- •The cook() multi-action batch function has been exploited twice via action-sequencing vulnerabilities that bypass solvency checks, and the general pattern may have additional undiscovered variants.
Risk Score Breakdown
Abracadabra's highest risk area is Interaction Severity (20/20). Here's how each dimension contributes to the overall 62/100 score:
Read the Full Abracadabra Risk Report
This protocol has 2 collapse scenarios. 2 critical and 2 high-severity interaction risks identified. See the full mechanism classification, interaction matrix, and deep-dive recommendations.
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