Moonwell’s lending pools racked up about $1.78M in bad debt after a cbETH oracle mispriced the token at nearly $1 instead of around $2.2k, enabling bots and liquidators to drain collateral within hours of a misconfigured Chainlink-based update reportedly using AI-generated logic.

Decentralized finance lending protocol Moonwell suffered a $1.78 million exploit due to a pricing oracle bug that misvalued Coinbase-wrapped ETH (cbETH), according to reports from the platform.

The vulnerability originated in oracle calculation logic reportedly generated by the AI model Claude Opus 4.6, which introduced an incorrect scaling factor in the asset price feed, according to the protocol’s disclosure. Attackers borrowed against severely underpriced collateral, extracting funds before the error was detected and corrected.

The cbETH mispricing effectively collapsed the collateral requirement for borrowing within affected pools. Because lending systems rely on accurate collateral ratios, the incorrect price allowed attackers to extract assets with minimal backing value, according to the protocol’s technical analysis.

Price oracles represent critical security components in DeFi lending systems. Incorrect asset valuation can enable under-collateralized borrowing or liquidation failures. Many major DeFi exploits have historically involved oracle manipulation or pricing errors rather than core protocol flaws, according to industry security reports.

The Moonwell incident differs from traditional oracle exploits in that the faulty logic appears linked to automated AI code generation rather than malicious oracle data feeds, according to the protocol’s preliminary investigation.

The exploit highlights risks associated with AI-assisted smart-contract development in financial applications. Language models can accelerate coding workflows, but financial protocols require precise numerical correctness, unit handling and edge-case validation, according to blockchain security experts.

In DeFi systems, small arithmetic or scaling mistakes can translate into systemic vulnerabilities affecting collateral valuation and solvency. The incident raises questions about whether AI-generated contract components may require stricter auditing standards than manually written code, according to security researchers.

AI-assisted development is increasingly used across Web3 engineering workflows, from contract templates to integration logic. Security models and audit frameworks have not yet fully adapted to AI-generated contract code, according to industry observers.

The broader implications center on how automated code generation errors in financial logic represent a new category of DeFi risk. Oracle math, scaling factors and unit conversions remain high-precision domains where automation failures can propagate into protocol-level vulnerabilities, according to technical analysis of the incident.

As AI-assisted smart-contract development expands, audit methodologies will likely need to evolve toward verifying not only code correctness but generation provenance and numerical invariants, according to blockchain security firms.

Three researchers at the US Federal Reserve argue that prediction market Kalshi can better measure macroeconomic expectations in real time than existing solutions and thus should be incorporated into the Fed’s decision-making process.

The “Kalshi and the Rise of Macro Markets” paper was released on Feb. 12 by Federal Reserve Board principal economist Anthony Diercks, Federal Reserve research assistant Jared Dean Katz and Johns Hopkins research associate Jonathan Wright.

Kalshi data was compared with traditional surveys and market-implied forecasts to examine how beliefs about future economic outcomes change in response to macroeconomic news and statements from policymakers.

“Managing expectations is central to modern macroeconomic policy. Yet the tools that are often relied upon—surveys and financial derivatives—have many drawbacks,” the researchers said, adding that Kalshi can capture the market’s “beliefs directly and in real time.”

“Kalshi markets provide a high-frequency, continuously updated, distributionally rich benchmark that is valuable to both researchers and policymakers.”

Kalshi traders can bet on a range of markets tied to the Federal Reserve’s decision-making, including consumer price index inflation and payroll, in addition to other macroeconomic outcomes such as gross domestic product growth and gas prices.

The Fed researchers said Kalshi data should be used to provide a risk-neutral probability density function, which shows all possible outcomes of Fed interest rate decisions and how likely each one is. 

“Overall, we argue that Kalshi should be used to provide risk-neutral [probability density functions] concerning FOMC decisions at specific meetings” arguing that the current benchmark is “too far removed from the monetary policy interest rate decision.”

However, Fed research papers are only “preliminary materials circulated to stimulate discussion” and do not impact the central bank’s decision-making.