7.3 Security Audits & Policy
7.3 Security Audits & Policy
Overview
Security is a top priority in the MonadAI ecosystem, ensuring that AI Agents, governance mechanisms, and financial operations remain safe from exploits, unauthorized actions, and malicious manipulation.
To achieve this, MonadAI enforces a multi-layered security policy, including:
✅ Third-party smart contract audits to eliminate vulnerabilities.
✅ Ongoing community-driven security monitoring to detect threats.
✅ Bug bounty programs to incentivize white-hat security research.
✅ AI execution policies to prevent rogue AI behaviors.
By implementing these measures, MonadAI ensures that AI Agents remain secure, trustless, and aligned with decentralized governance principles.
1. Smart Contract Audits & Vulnerability Assessments
Mandatory Third-Party Security Audits
All critical smart contracts undergo third-party security audits before deployment, focusing on: ✅ Bonding curve mechanisms – Preventing price manipulation or unintended liquidity drains. ✅ DEX liquidity migration – Ensuring AI Agent token liquidity transitions securely. ✅ Governance & staking contracts – Securing against governance attacks and unauthorized changes. ✅ AI Agent execution contracts – Preventing rogue AI behavior or unauthorized smart contract interactions.
📌 Example Use Case:
Before an AI Agent token graduates from the bonding curve to a DEX, its liquidity migration contract is audited to prevent front-running exploits.
✅ This ensures that the transition process is tamper-proof and resistant to external manipulation.
On-Chain Security & Automated Monitoring
Security contracts continuously monitor AI Agent transactions, smart contract activity, and governance interactions.
AI Agents must operate within pre-approved execution parameters, preventing unauthorized on-chain actions.
Security triggers can pause AI execution if anomalies are detected, preventing real-time exploitation.
📌 Example Use Case:
If an AI Agent unexpectedly attempts to execute a high-risk transaction, a security contract halts execution pending governance review.
✅ This ensures that AI Agents cannot be hijacked or exploited for unintended financial transactions.
2. Governance-Driven Security Policies
Decentralized Security Oversight
Governance participants have the power to: ✅ Propose and approve security updates for AI execution logic. ✅ Set risk thresholds for AI Agents, ensuring they cannot make decisions outside of governance-defined parameters. ✅ Review flagged transactions and anomalies, preventing malicious AI activity.
📌 Example Use Case:
The community identifies an AI governance assistant that incorrectly interprets voting patterns.
A governance proposal is submitted to adjust its decision-making logic, ensuring future accuracy.
✅ This ensures that AI-driven decisions remain transparent, interpretable, and aligned with governance intent.
3. Bug Bounty Programs & Community Security Incentives
Incentivized Security Research
White-hat hackers and security researchers are incentivized to find vulnerabilities in MonadAI contracts.
Bug bounty rewards are paid in $MONAI, ensuring that security researchers are motivated to report, not exploit, vulnerabilities.
Critical security flaws receive higher payouts, ensuring that major vulnerabilities are addressed quickly.
📌 Example Use Case:
A security researcher finds a potential reentrancy exploit in an AI Agent’s smart contract.
They report the issue, receive a $MONAI reward, and the issue is patched before any damage occurs.
✅ This ensures ongoing security improvements without relying solely on internal audits.
Community-Led Security Audits
MonadAI governance can approve and fund security audits through decentralized decision-making.
Security experts from the community can submit audit proposals and receive funding to evaluate smart contract integrity.
Regular audits ensure AI Agents evolve securely, without introducing new vulnerabilities.
📌 Example Use Case:
The community votes to fund an additional security audit before a high-value AI Agent launch, ensuring its contract is secure from exploits.
✅ This enables continuous security assessments while keeping security oversight decentralized.
4. AI Execution Safety Mechanisms
Restricting Unauthorized AI Actions
AI Agents cannot execute transactions beyond their governance-approved scope.
Governance-approved AI execution policies define: ✅ Which smart contracts AI Agents can interact with. ✅ What risk thresholds apply to AI-driven DeFi strategies. ✅ How AI Agents update themselves, ensuring they cannot self-modify outside of governance control.
📌 Example Use Case:
A DeFi AI Agent tries to execute a transaction with excessive leverage.
The transaction is blocked by security policies, preventing a potential high-risk liquidation event.
✅ This ensures that AI Agents operate within strict, predefined risk parameters.
Multi-Signature Execution for High-Risk Actions
Certain AI Agent operations require multi-signature approval, preventing single-actor exploits.
Governance-defined multi-sig requirements ensure AI Agents cannot execute high-impact transactions autonomously.
📌 Example Use Case:
A DAO governance AI proposes a major treasury allocation.
Multiple governance signers must approve the transaction before it can be executed.
✅ This ensures that high-value AI operations remain under strict governance control.
Key Takeaways
All critical smart contracts undergo third-party security audits, ensuring safe and transparent execution.
AI Agent execution is continuously monitored, preventing unauthorized actions.
Governance participants play an active role in defining AI security policies and risk thresholds.
Bug bounty programs incentivize white-hat security research, ensuring ongoing contract integrity.
Multi-signature approval mechanisms prevent AI Agents from executing high-risk actions autonomously.
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