AegisAI Protocol: Real-Time Cross-Chain Risk Intelligence

Hyperion HyperHack Ideathon
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Problem Statement:

DeFi protocols lose billions annually due to inadequate risk systems that can’t detect threats in real-time. Current oracles miss cross-chain interactions, smart contract vulnerabilities, and market manipulation patterns, leaving protocols and users exposed to preventable exploits, rug pulls, and cascade failures that could be stopped with intelligent early warning systems.

Solution Overview:

AegisAI Protocol is an AI-native oracle providing real-time risk intelligence across multiple blockchain networks. Leveraging Hyperion’s on-chain AI inference and parallel execution, we continuously analyze transactions, smart contract interactions, and market patterns to generate dynamic risk scores and early warning alerts. Our adaptive system learns from each exploit to prevent future attacks, making DeFi safer for mainstream adoption.

Project Description

Built specifically for Hyperion’s AI-native environment, AegisAI features a Real-Time Risk Engine processing thousands of cross-chain transactions per second, identifying suspicious patterns and attack vectors before they execute. Our Adaptive Intelligence Layer uses on-chain machine learning to continuously improve threat detection.

Core Features:

Cross-Chain Risk Aggregation : Monitors Ethereum, Polygon, BSC, and other networks simultaneously.
Smart Contract Vulnerability Scanner : Analyzes code patterns and interaction graphs in real-time.
Liquidity Health Monitor : Detects potential bank runs or manipulated pools.
Market Manipulation Detector : Identifies coordinated attacks and price manipulation schemes.

User Experience: Protocols access our Risk Dashboard with custom alerts and detailed analytics. DeFi users receive Safety Scores for protocols, Transaction Risk Warnings before interacting with suspicious contracts, and Portfolio Risk Assessment across multi-chain holdings.

Track Alignment : This is a Track 1: AI-Native project with Alith Integration for enhanced on-chain AI capabilities, targeting both the $150k main prize pool and $30k Alith bonus rewards.

What excites us most is democratizing institutional-grade risk intelligence for the entire DeFi ecosystem, building the trust infrastructure needed for Web3 mainstream adoption.

Community Engagement Features

Our gamified Risk Hunter Academy transforms security testing into engaging community experiences:

Threat Detection Challenges (100-500 points): Analyze historical attack scenarios and identify red flags, testing AI accuracy against known exploits. Higher difficulty scenarios earn bonus multipliers.

Protocol Safety Audits (200-1000 points): Evaluate new DeFi protocols using AegisAI tools, submit detailed risk assessments. Quality audits receive community recognition badges and leaderboard positions.

Real-Time Alert Verification (50-300 points): Verify AegisAI risk alerts, provide feedback helping train our AI while earning rewards for accurate validations.

Risk Prediction Tournaments (500-2000 points): Monthly competitions predicting protocol vulnerabilities, with exclusive NFT rewards and investor networking access for top performers.

Cross-Chain Risk Mapping (150-750 points): Contribute to vulnerability discovery by testing protocol interactions across different chains.

Gamification Benefits : Points unlock exclusive features, early access to new tools, governance voting power, and potential revenue sharing from commercial integrations. This creates a self-improving ecosystem where community engagement directly enhances DeFi security.

Getting Involved

Developers : Join our GitHub for AI model development, API integration, and earn bounties for quality contributions.

Security Researchers : Participate in our bug bounty program, weekly security workshops, and help improve threat detection algorithms.

DeFi Protocols : Beta-test integration partnerships with technical support, custom risk models, and co-marketing opportunities.

Community : Join Discord for risk discussions, educational webinars, and contribute to our crowdsourced threat intelligence database.

Students/Researchers : Access open datasets for academic research, university partnerships, and internship opportunities.

Ready to build the future of DeFi security? Let’s make Web3 safer together!

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nice brief explanation, i have something in mind after reading about AegisAI,

  1. How does AegisAI differ from traditional oracles like Chainlink when it comes to real-time threat detection?
  2. What specific role does Hyperion’s AI-native environment play in enhancing AegisAI’s performance?
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Hello @santhoshkumar0918

  1. How does AegisAI ensure the accuracy and reliability of its real-time risk scores across multiple chains, especially given the complexities of cross-chain interactions and evolving threat vectors?

  2. How does AegisAI’s integration with Hyperion and the Alith framework enable unique advantages over traditional off-chain threat detection systems?

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What unique AI techniques does AegisAI Protocol’s Real-Time Risk Engine use to detect cross-chain threats?

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Hi, i have few technical questions in my head to get an overview.

  1. How is the training data for these models sourced, curated, labeled, and kept up-to-date, especially for identifying novel attack vectors that haven’t been seen before? Do you employ unsupervised or semi-supervised learning techniques for anomaly detection?

  2. How does the Adaptive Intelligence Layer technically implement “on-chain machine learning”? Does this involve retraining models directly on Hyperion, or is it more about updating model parameters based on new data processed on-chain? What are the mechanisms for ensuring the integrity and security of these on-chain model updates?

  3. For the Liquidity Health Monitor, what specific on-chain and off-chain (if any) metrics are tracked to detect potential bank runs or manipulated pools (e.g., rapid liquidity withdrawal rates, abnormal token concentration changes, wash trading indicators)?

  4. How does AegisAI function as an “AI-native oracle”? Does it provide on-chain accessible endpoints for other smart contracts to query risk scores or receive alerts directly? If so, what is the mechanism for ensuring the data delivered by the oracle is tamper-proof and reflects the latest intelligence?

Thank you.

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  1. AegisAI vs Traditional Oracles

Chainlink and traditional oracles are designed to bring external data (like prices) onto the blockchain. They’re essentially “data fetchers.”

AegisAI is completely different - we’re analyzing what’s already happening on-chain to detect threats. While Chainlink tells you “ETH costs $3000,” AegisAI tells you “this transaction looks like an attack pattern we’ve seen before.”

Think of it this way: traditional oracles report news, while AegisAI prevents crimes. We’re looking at transaction patterns, smart contract behaviors, and cross-chain activities to spot threats before they happen.

  1. Why Hyperion Makes AegisAI Powerful

Most blockchains can’t run AI models directly on-chain due to cost and speed limitations. Hyperion changes this with:

Native AI processing: Our models run directly on-chain, making results transparent and verifiable

Parallel execution: We can analyze thousands of transactions simultaneously
Instant finality : Alerts happen in real-time, not after waiting for block confirmations

On Ethereum, running our AI would cost thousands of dollars per analysis. On Hyperion, it’s efficient and affordable, making real-time threat detection actually feasible.

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Unique AI Techniques for Cross-Chain Threat Detection

Multi-Chain Transaction Graph Analysis: Our AI builds real-time relationship maps between wallets, contracts, and liquidity across different chains. It spots suspicious connections that humans would miss - like when the same entity manipulates multiple protocols simultaneously.

Behavioral Pattern Recognition: We use transformer models (similar to ChatGPT) trained on exploit data to recognize pre-attack behaviors. The AI learns signatures like unusual approval patterns, rapid liquidity movements, or governance voting anomalies that typically happen 10-30 minutes before exploits.

Federated Learning Across Chains: When our AI detects a new attack pattern on Ethereum, it immediately applies that knowledge to protect Polygon, BSC, and other networks. Each exploit makes the entire system smarter.

Real-Time Anomaly Scoring: Instead of simple rule-based alerts, we use ensemble machine learning to generate dynamic risk scores (0-100) that consider multiple factors: transaction volume, timing, wallet history, and cross-protocol interactions.

Adaptive Thresholds:The AI automatically adjusts sensitivity based on market conditions - stricter during high volatility, more relaxed during stable periods.

Key Innovation: We’re the first to combine on-chain AI inference with cross-chain intelligence, creating a “security brain” that thinks across the entire DeFi ecosystem in real-time!

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  1. Training Data & Novel Attack Detection

Data Sources: Historical exploit transactions, DeFiSafety reports, and real-time transaction patterns across major chains.

Novel Attack Detection: We use semi-supervised learning with Alith’s framework - the AI learns normal transaction patterns, then flags statistical anomalies. For completely new attacks, we employ ensemble anomaly detection that combines multiple unsupervised techniques (isolation forests, autoencoders) to catch zero-day exploits.

Continuous Updates: Alith’s modular architecture allows us to retrain models weekly with new exploit data while maintaining on-chain performance.

  1. On-Chain Machine Learning Implementation

Technical Approach: Using Alith’s inference engine, we don’t retrain entire models on-chain (too expensive). Instead, we update model parameters and thresholds based on new patterns detected on-chain.

Mechanism: Alith provides secure model serving where the AI logic runs on-chain but heavy computation happens in Alith’s optimized runtime. Model updates use cryptographic proofs to ensure integrity.

  1. Liquidity Health Monitoring

Key Metrics We Track:

  • Withdrawal velocity (% of liquidity removed per block)
  • Concentration ratios (top 10 holders’ percentage)
  • Trading volume vs liquidity depth ratios
  • Flash loan usage patterns around liquidity events

Detection Logic: Alith processes these metrics in real-time, flagging when multiple indicators spike simultaneously.

  1. AI-Native Oracle Functionality

Smart Contract Integration: Yes, We provide simple on-chain functions:

solidity code :
function getRiskScore(address protocol) returns (uint256)
function getAlertLevel(address token) returns (AlertLevel)

Tamper-Proof Mechanism: Alith’s consensus layer ensures data integrity. Multiple Alith nodes must agree on risk scores before updating on-chain state.
Real-time Access: Other protocols can query our risk data directly from Hyperion’s state, no external API calls needed.

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How does your system ensure transparency or explainability of these AI-generated risk scores, especially for users or auditors who may not be familiar with how ensemble models work?

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We break down our risk scores so anyone can understand them. Instead of just showing “Risk: 85” with no explanation, users see exactly why:

“High risk detected because of unusual liquidity withdrawals (contributed 40% to score), suspicious governance voting (30%), and wallet connections to known exploiters (15%).”

For auditors, we keep detailed logs on-chain showing which specific transactions triggered alerts and why those patterns are dangerous. Think of it like showing your work on a math problem.

We also translate technical stuff into simple language. So instead of “ensemble model detected statistical anomaly,” users see “This looks similar to the pattern that happened before the Euler hack last year.”

The community helps too - when our AI flags something, security experts can verify and explain it in plain English. This creates better explanations over time.

Basically, we think if you can’t explain why something is risky in simple terms, people won’t trust your AI. So we prioritize making everything clear and understandable, even for non-technical users.

The goal is that anyone should be able to look at our risk assessment and understand exactly why we’re concerned about a particular protocol or transaction.

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Thank you so much for the clear and insightful explanation, @santhoshkumar0918 :raising_hands:

Looking forward to seeing how AegisAI continues to redefine on-chain security. Will definitely keep an eye on your updates!"

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