Moonwell Lending Protocol Drained via External Oracle Price Manipulation Flaw
Flawed oracle integration permitted a collateral token's price to be grossly inflated, enabling an under-collateralized asset drain.
Malicious Chrome Extension Steals Seed Phrases via Covert Sui Transactions
A high-ranking malicious wallet extension weaponized the Sui blockchain to covertly exfiltrate user mnemonics, bypassing traditional network monitoring.
Balancer V2 Stable Pools Drained Exploiting Faulty Access Control Logic
Faulty access control in the core vault's manageUserBalance function allowed unauthorized internal withdrawal, compromising over $128 million in multi-chain liquidity.
Balancer V2 Pools Drained by Faulty Smart Contract Access Control
V2 vault access control logic failed to validate message senders, enabling unauthorized internal withdrawals and a $110 million multi-chain asset drain.
Balancer V2 Exploit Triggers $128 Million Loss Exposing Systemic DeFi Risk
The multi-chain access control exploit underscores the critical need for a hardened, multi-layered security architecture beyond traditional smart contract audits to secure composable DeFi primitives.
Oracle Failures and Access Flaws Trigger $129 Million Multi-Chain DeFi Loss
The cascading $129M loss from oracle manipulation and faulty access controls re-centers the DeFi industry on infrastructure risk and security rigor.
Zero-Knowledge Proof of Training Secures Decentralized AI Consensus
A new Zero-Knowledge Proof of Training (ZKPoT) consensus mechanism leverages zk-SNARKs to cryptographically verify model performance, eliminating Proof-of-Stake centralization and preserving data privacy in decentralized machine learning.
Zero-Knowledge Proof of Training Secures Federated Learning Consensus
ZKPoT uses zk-SNARKs to verify model contributions privately, eliminating the trade-off between decentralized AI privacy and consensus efficiency.
Zero-Knowledge Proof of Training Secures Private Federated Consensus
A novel Zero-Knowledge Proof of Training (ZKPoT) mechanism leverages zk-SNARKs to validate machine learning contributions privately, enabling a scalable, decentralized AI framework.
