🛡️ Risks & Securit
While NeuraLink emphasizes decentralization, collaboration, and transparency, the system must actively manage risks across technical, economic, governance, and ethical dimensions.
10.1 Technical Risks
🛠 Fake Simulation Results & Cheating Malicious nodes may submit falsified model outputs or automate fake contributions.
Mitigations:
Proof-of-Training (PoT) and cross-node result verification
Trusted Execution Environments (TEE) or Zero-Knowledge Proofs (ZKP)
Redundant simulation tasks and multi-party challenge systems
📦 Data Poisoning & Low-Quality Inputs Low-value or misleading data may pollute model performance.
Mitigations:
Minimum quality thresholds for data uploads
Reputation scoring for data contributors
Community-driven data validation and labeling bounties
🔁 Model Divergence & Non-Reproducibility Lack of coordination may result in fragmented model versions or unverifiable outcomes.
Mitigations:
On-chain versioning with ModelID + CommitHash
Fork/Merge support and metadata transparency
Required parameter trace logging for publication
10.2 Economic Risks
🎯 Reward Abuse & Task Farming Users or bots may exploit tasks for unearned rewards.
Mitigations:
CAPTCHA or behavioral validation
Dynamic weightings based on task rarity and contributor history
Penalties or cooldowns for low-value or repetitive behavior
📉 Token Volatility & Overdependence Heavy reliance on token incentives may affect long-term sustainability.
Mitigations:
Treasury stabilization pool funded by usage fees
Partial rewards in stable assets or value-locked Neura
Dynamic emission caps linked to activity and revenue
10.3 Governance Risks
🗳 Governance Takeover or Proposal Attacks Wealthy actors could dominate votes or submit harmful proposals.
Mitigations:
Time-weighted voting with staking delay
Hybrid voting (token + reputation)
Emergency multisig veto committee (initial phases only)
🪓 DAO Splits or Protocol Forks Disagreements could fragment community consensus.
Mitigations:
Proposal quorum + discussion periods
DAO-managed soft fork migration tools
Model migration tooling with version inheritance
10.4 Ethical & Output Risks
⚠️ Model Misuse or Harmful Content Open-source models could be trained to produce misinformation, bias, or harmful outputs.
Mitigations:
DAO-based model review system
On-chain invocation tracking and usage scoring
Governance rules for banning, modifying, or restricting unethical models
NeuraLink’s risk framework is continuously evolving. All critical system parameters and mitigations will be subject to DAO oversight and upgradable through community consensus.
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