AlithGuard: Real-Time AI-Powered Transaction & dApp Sentinel

Hyperion HyperHack Ideathon
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Project Name

AlithGuard: Real-Time AI-Powered Transaction & dApp Sentinel

Problem Statement

Web3 users, especially newcomers, are highly vulnerable to sophisticated scams, malicious smart contracts, and opaque transaction outcomes, leading to significant financial losses and eroding trust in the ecosystem. Existing security tools are often reactive or too technical, failing to provide accessible, real-time, preventative protection for everyday users interacting with dApps on emerging platforms like Hyperion.

Solution Overview

AlithGuard is an AI-native dApp built on Hyperion, designed to deeply integrate with and extend the capabilities of Alith, Hyperion’s built-in AI co-agent. It acts as a proactive, real-time security analysis layer, leveraging Hyperion’s on-chain AI (LLM inference) and parallel execution. Before users commit to a transaction or interact with a new dApp, AlithGuard analyzes potential impacts, scrutinizes smart contracts for risks, and provides an immediate, easy-to-understand risk assessment and plain-language explanation. This approach is innovative due to its on-chain, AI-driven, preventative nature, seamlessly woven into the user experience via Alith.

Project Description

AlithGuard aims to be the essential on-chain security companion for users on the Hyperion network, enhancing safety and confidence in Web3 interactions.

Its core functionality includes Proactive Transaction Analysis, where users (or integrated wallets/dApps) submit transaction details pre-signature. AlithGuard’s on-chain AI decodes these, predicts outcomes, and checks for attack vectors like malicious approvals or address poisoning. A second key feature is Real-Time Smart Contract Scrutiny, allowing users to input a contract address for a rapid AI-driven audit, flagging suspicious patterns and centralization risks. All findings are presented with a User-Friendly Risk Score & Explanation (e.g., color-coded warnings and simple language), making complex interactions transparent.

We envision using Hyperion’s On-chain AI (LLM inference) for threat pattern recognition, its Parallel Execution for handling concurrent analysis requests swiftly, and the Alith SDK for seamless integration. Users would interact via a dApp interface, integrated wallets, or directly through Alith (e.g., “Alith, is this transaction safe?”). They benefit by avoiding scams and understanding their on-chain actions.

What excites us is building a tangible safety net using Hyperion’s unique AI capabilities to address a critical Web3 challenge, making the ecosystem safer and fostering wider adoption by empowering users with understandable, real-time security insights.

Community Engagement Features

This will be themed “Secure Hyperion with AlithGuard” during the “Corrupted Alith Takeover” stress-testing campaign.

  • Testable Features/Tasks & Points System:

    1. Analyze Safe Transaction: Submit a benign transaction; AlithGuard rates “Safe.” (10 points)
    2. Detect Simulated Malicious Transaction: Submit a pre-defined risky transaction; AlithGuard flags “Caution/High-Risk.” (25 points)
    3. Identify AlithGuard Analysis Flaw (Honeypot Test): User interacts with a tricky test contract; correctly reports if AlithGuard misses a simulated threat. (50 points)
    4. Report Simulated Phishing Address: Identify “poisoned” addresses from a test list. (20 points/correct submission)
    5. Feedback on Analysis Clarity: Rate clarity of AlithGuard’s explanations (min. 3). (5 points, max 15)
    6. Successful Referral: Refer a new user who completes one task. (30 points)

  • How this gamifies the experience:
    A leaderboard will display top point earners. Digital badges/NFTs (e.g., “Alith’s Sentinel,” “Threat Hunter”) will be awarded for milestones. The narrative of “training” AlithGuard makes participation engaging.

  • How this encourages user onboarding:
    Tasks guide users through AlithGuard’s features. Points and rewards incentivize participation and exploration. The referral system drives new user acquisition, while collective effort improves the tool and fosters community ownership.

Getting Involved

  1. Join the Discussion: We’ll establish a project thread on the HyperHack Official Forum (link to be provided post-submission) for Q&A, updates, and community feedback.
  2. Telegram Community: Join our dedicated project channel within the Metis Developer Community Telegram (specific link like t.me/AlithGuard_HyperHack to be created and shared on the forum).
  3. Contribute (Post-Ideathon): A public GitHub repository will be shared when development begins. We’ll welcome testers for the campaign, feedback providers (UI/UX, features), security researchers, and potentially developers for defined modules.
  4. Follow our Progress: Stay updated via our HyperHack Forum thread for all announcements and links. Your initial questions and support on the forum are highly encouraged!
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Thank you for sharing this detailed and innovative project proposal!

Here are five questions about AlithGuard:

1-How does AlithGuard balance thorough security analysis with maintaining fast, real-time transaction approval workflows?

2- What kinds of malicious patterns or attack vectors is AlithGuard specifically trained to detect?

3-Will AlithGuard support integration with popular wallets and dApps beyond Hyperion to broaden user protection?

4-How do you plan to handle false positives or user confusion arising from risk warnings?

5-Are there plans for community involvement in improving AlithGuard’s AI models or reporting new threats?

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How is AlithGuard’s AI updated or retrained over time? Is it entirely on-chain or is there off-chain inference/data involved for improvement?

What role does the “Corrupted Alith Takeover” narrative play in educating users about threats while still stress-testing the AI’s capabilities?

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AlithGuard sounds like a very practical and much-needed security layer for everyday Web3 users, especially leveraging Hyperion’s unique AI capabilities. I have some questions.

  1. When using Hyperion’s on-chain LLM inference for threat pattern recognition, what kind of patterns is the LLM specifically trained to identify in transactions and smart contracts, and how does it differentiate from more traditional, non-LLM static or heuristic analysis?

  2. Regarding “Real-Time Smart Contract Scrutiny,” what is the typical turnaround time for a “rapid AI-driven audit”? What are the limitations of this rapid audit compared to a more in-depth, traditional smart contract audit?

  3. How does AlithGuard plan to stay ahead of new, evolving scam techniques or smart contract vulnerabilities that the AI may not have been explicitly trained on? What mechanisms are in place for updating its knowledge base or detection algorithms rapidly?

  4. If AlithGuard flags a dApp or transaction as risky, what are the recommended next steps or recourse for the user, facilitated through the AlithGuard interface or Alith itself?

Thank you.

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  1. Balancing Speed and Thoroughness:
    AlithGuard will use Hyperion’s parallel execution to run multiple lightweight AI checks simultaneously. It will prioritize common, high-impact threat vectors for real-time analysis and may defer deeper, more complex analysis for opt-in or less time-sensitive scans. The goal is a quick initial “go/no-go” followed by more detail if needed.
  1. Detected Malicious Patterns:
    AlithGuard will be trained to detect:
    • Address poisoning (similar-looking addresses).
    • Malicious approvals (e.g., unlimited token approvals to suspicious contracts).
    • Known scam contract signatures and code patterns.
    • Basic reentrancy and honeypot indicators.
    • Calls to blacklisted addresses or dApps.
  1. Wallet and dApp Integration:
    Initially, AlithGuard will focus on the Hyperion ecosystem. However, the long-term vision includes providing APIs and SDKs that could allow popular multi-chain wallets and dApps to integrate AlithGuard’s analysis capabilities for transactions on Hyperion, extending protection.
  1. Handling False Positives/Confusion:
    Warnings will be clearly explained in simple language. There will be a system for users to report suspected false positives. Feedback will be used to refine AI models. We’ll also provide educational materials to help users understand the types of risks AlithGuard flags.
  1. Community Involvement:
    Yes, absolutely. We plan for community members to:
    • Report new threats and scam patterns.
    • Participate in validating AI model outputs (e.g., confirming if a flagged item is indeed malicious).
    • Potentially contribute to an open-source threat database that AlithGuard uses.
      This is crucial for keeping the AI models effective and up-to-date.
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  1. AI Updates/Retraining (On-chain vs. Off-chain):
    It’s a hybrid approach. Major AI training and updates happen off-chain (for power and complex data). The refined, smaller AI rules or models are then deployed on-chain for AlithGuard to use in real-time. User feedback from on-chain interactions helps improve the next off-chain training.

  2. Role of “Corrupted Alith Takeover” Narrative:
    It makes stress-testing engaging and educational. Users learn to spot threats by trying to find “mistakes” in a simulated “corrupted” AlithGuard. This helps them understand real risks while their actions simultaneously test the AI’s limits and reveal areas for improvement.

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Thank you, this is a clear and comprehensive explanation. AlithGuard’s approach to balancing fast scanning with deeper analysis, and its emphasis on community involvement, is particularly impressive.

Quick question: Does AlithGuard aim to provide any analysis or warnings related to off-chain threats, such as approvals obtained through social engineering or malicious links?

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Thank you for the kind words! AlithGuard’s primary focus is on-chain analysis, like transactions and smart contract code, using Hyperion’s AI capabilities. For off-chain threats, such as approvals obtained through social engineering or malicious links, AlithGuard would have limited direct capabilities since these occur outside the blockchain (e.g., phishing websites or Discord scams). However, it could indirectly help by:

  • Flagging suspicious on-chain approvals that might result from social engineering (e.g., a transaction granting excessive permissions to an unknown address).
  • Integrating with a community-sourced database of known malicious off-chain sources (e.g., phishing URLs reported by users), cross-referencing them with on-chain interactions if linked.
  • Providing educational warnings through Alith’s interface, alerting users to common off-chain scam tactics when relevant (e.g., “This transaction resembles approvals often tricked via phishing links; verify the source.”).

Long-term, we aim to collaborate with off-chain security tools to enhance this coverage, but the core strength remains on-chain threat detection.

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Thanks for the clear explanation! Makes sense that AlithGuard’s strength is firmly rooted in on-chain intelligence. Still, it’s great to hear there’s indirect support for flagging approvals that might stem from social engineering, and that community-sourced data and educational prompts are part of the strategy.

Looking forward to seeing how those off-chain collaborations evolve sounds like a solid foundation to build from.

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LLM Pattern Identification vs. Traditional Analysis:

  • Patterns Identified: AlithGuard’s LLM detects semantic anomalies (e.g., odd transaction sequences), code similarities to known scams, phishing-like patterns (e.g., address poisoning), and potential economic exploits (e.g., rug pull logic).
  • Differentiation: Unlike static/heuristic checks that use fixed rules for known bugs, LLMs understand context and intent, spotting complex or novel threats by recognizing “unusual” patterns, though they may need tuning to reduce false positives.

Real-Time Smart Contract Scrutiny – Turnaround & Limitations:

  • Turnaround Time: Seconds to under a minute, leveraging Hyperion’s parallel execution.
  • Limitations: Less deep than manual audits; may miss complex logic flaws or novel exploits; focuses on common vulnerabilities and known patterns, not full economic or off-chain analysis.

Staying Ahead of New Threats:

  • Mechanisms: Off-chain retraining with new threat data (community reports, security feeds); rapid on-chain heuristic updates for urgent threats; anomaly detection for unknown patterns; partnerships with security firms. Updates are governed securely (e.g., DAO votes).

Recourse for Risky Flags:

  • Actions: Clear risk explanation (Low/Medium/High) with specific warnings (e.g., “This contract could drain funds”).
  • Recommendations: Low: Proceed cautiously; Medium: Seek more info; High: Avoid interaction. Includes “Learn More” links, community feedback options, and a cautious override for advanced users.
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"Thank you for providing these detailed clarifications – this is very insightful and clearly outlines AlithGuard’s approach to leveraging LLMs for enhanced Web3 security on Hyperion.I have a few more specific questions to further understand the technical depth and operational nuances.

  1. When inferring “intent” or “rug pull logic” from smart contract code, what specific code structures, function calls, or tokenomic patterns does the LLM prioritize or weigh heavily in its analysis, especially given that direct intent isn’t explicitly coded?

  2. For the “rapid on-chain heuristic updates for urgent threats,” can you describe the technical pathway for these updates? How are these heuristics defined and deployed securely (e.g., via the DAO) while still being “rapid” enough for urgent threats, potentially faster than a full off-chain model retraining cycle?

Thank you.

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1. How the LLM Detects “Rug Pull” Intent

Our LLM is trained to spot a combination of red flags that, together, suggest malicious intent. It prioritizes things like:

  • Excessive Owner Privileges: Functions that let the owner mint unlimited tokens, pause all trading, or blacklist any user at will.
  • Unlocked Liquidity: Code that shows the initial liquidity isn’t locked, meaning the developer can pull it out at any time.
  • Hidden Fees or “Honeypot” Code: Unusual functions that might prevent selling or introduce a massive sell tax unexpectedly.
  • Token Allocation: An abnormally large percentage of tokens held by the deployer’s wallet.

It’s not about one single line of code, but how these risky pieces fit together to form a pattern of potential abuse.

2. Pathway for Rapid, Urgent Updates

We use a two-tiered security system for speed and safety.

  1. Identify Threat: Our security team or community members spot a new, urgent threat.
  2. Propose Quick Fix: They create a simple rule (a “heuristic”), like “block this new scam contract address.”
  3. Expedited Approval: This simple rule is sent to a small, trusted Security Council (elected by the main DAO) for an immediate vote. This is much faster than a full DAO vote.
  4. Deploy Instantly: Once the council approves, this new rule is added to an on-chain “rules list” that AlithGuard checks in real-time.

This lets us patch urgent threats in minutes or hours, while bigger AI model updates can be approved by the full DAO on a less urgent schedule.

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