Problem

Oracle manipulation attacks pose a significant threat to decentralized finance (DeFi) ecosystems. Oracles provide crucial external data to smart contracts, such as asset prices. If an oracle is compromised or manipulated, it can lead to incorrect data being fed to smart contracts, resulting in potential financial losses, liquidations, and exploits.

In addition to oracles, decentralized exchanges (DEXes) using liquidity pools are also susceptible to price manipulation attacks. Malicious actors can manipulate the prices within these liquidity pools by executing large trades, thereby exploiting arbitrage opportunities or triggering liquidations in connected protocols. Therefore, a robust mechanism is needed to detect and mitigate both oracle and liquidity pool price manipulation attacks promptly.

Solution

The Oracle and Liquidity Pool Attack Protection Agent (OLPAPA) is designed to continuously monitor the price points of a given asset across multiple oracles and liquidity pools. It aims to detect any discrepancies or anomalies that may indicate manipulation attacks. Upon detecting significant variations in price data from different sources, the agent will emit events to notify smart contracts and stakeholders about the potential threat.

Overview

How it Works

1. Oracle and Liquidity Pool Integration:
   • The OLPAPA integrates with various oracle services (e.g., Chainlink, Band Protocol, Tellor) and DEXes with liquidity pools (e.g., Uniswap, SushiSwap, Balancer) to fetch real-time price data for a given asset.
   • The agent continuously polls these sources at predefined intervals to collect the latest price points.
2. Data Aggregation:
   • The collected price data from different oracles and liquidity pools are aggregated and stored temporarily for analysis.
   • Each price point is timestamped to ensure accurate comparison over time.
3. Discrepancy Detection:
   • The agent applies statistical analysis and anomaly detection algorithms to compare the price data from different sources.
   • It calculates metrics such as mean, median, and standard deviation to identify outliers.
   • A threshold-based approach is used to determine acceptable price variations. If the difference between price points exceeds the defined threshold, it is flagged as a potential manipulation.
4. Event Emission:
   • Upon detecting significant discrepancies, the OLPAPA emits events containing detailed information about the detected anomaly.
   • These events can be subscribed to by smart contracts, dApps, and monitoring systems to take appropriate actions, such as halting transactions or switching to a backup oracle or liquidity pool.

How Contribution Works

1. Adding More Supported Oracles:
   • Community Proposals: Contributors can propose new oracles to be integrated into the OAPA. Proposals should include detailed information about the oracle, its reliability, and any relevant documentation.
   • Development and Testing: Approved proposals will move to the development phase, where contributors can implement the integration of the new oracle. The integration will be thoroughly tested in a staging environment before being deployed.
   • Review and Deployment: The implemented changes will undergo peer review and, upon approval, will be merged and deployed to the main network.
2. Adding More Supported Liquidity Pools:
   • Community Proposals: Similar to oracles, contributors can propose new liquidity pools to be monitored by the OAPA. Proposals should include details about the DEX, the liquidity pool, and relevant documentation.
   • Development and Testing: Contributors develop and test the integration of the new liquidity pool in a controlled environment.
   • Review and Deployment: After peer review and approval, the changes are deployed to the main network, expanding the coverage of the OAPA.
3. Improving the Oracle Manipulation Deviation Detection Algorithm:
   • Algorithm Proposals: Contributors can propose enhancements or new algorithms for better detection of oracle manipulation. Proposals should include a detailed explanation of the algorithm, its advantages, and potential improvements over the existing method.
   • Research and Development: Contributors develop and test the proposed algorithm in a sandbox environment, comparing its performance against the current algorithm.
   • Peer Review and Testing: The new algorithm undergoes peer review and extensive testing to ensure its efficacy and reliability.
   • Deployment: Upon successful testing and approval, the new algorithm is integrated into the OAPA, enhancing its detection capabilities.

Task Provability

How Results are Proven