TEEs, MCP & Multimodal

MaigaXBT innovation with TEEs, multi-venue MCP server, and Herd Multimodal model to power next-generation crypto and DeFi AI agents

Trusted Execution Environments (TEEs)

• Secure Enclave for Secrets & Models

  • MaigaXBT’s private keys (wallet creds) and proprietary trading models live inside TEEs.

  • All market-data ingestion, inference, and order-signing happen in-enclave—so even if the host is compromised, keys and model weights remain opaque.

• Remote Attestation & Auditability

  • Upstream counter-parties (or regulators) verify via TEE attestation that MaigaXBT is running exactly the audited code, enabling trustless data sharing (e.g. private order-flow metrics).

• Enclave Lifecycle & Memory Protection

  • Secure Data Ingestion

    • Market feeds, API tokens, and model weights are provisioned via a secure channel (TLS + remote attestation handshake).

    • The enclave decrypts these secrets internally; the host sees only encrypted blobs.

  • Remote Attestation

    • MaigaXBT’s backend obtains a QUOTE from the enclave’s trusted authority.

    • Counter-parties verify the QUOTE against known measurements to ensure no code tampering.

  • Runtime Protections

    • Side-channel mitigations (e.g. page-fault blocking, cache-partitioning) are enabled to reduce leakage.

    • Any attempt by the OS or hypervisor to read/write enclave pages triggers hardware exceptions.

  • Sealed Storage & Key Rotation

    • Long-term keys (e.g. MPC wallet private keys) are sealed to disk via enclave sealing keys, tied to the CPU.

    • Periodic key rotation is driven by enclave-only code, ensuring that even if disk is compromised, data remains protected.

Area	Use-Case
Analysis	Run encrypted on-chain analytics (whale-move detection, mempool scans) without exposing raw data.
Report	Generate signed “proof of analysis” summaries that clients can verify against the enclave.
Research	Safely test novel strategy code inside TEEs before promoting to production.
Signal	Issue inference-only signals (e.g. buy/sell triggers) where the signal payload is attested and privacy-preserving.
Automation	Execute flash-loan or liquidation-protection flows end-to-end in-enclave, with on-chain attested proofs of execution.

Multi-Venue MCP Server

• Unified Market Interface

  • MCP server normalizes order-book, trade, funding-rate, and on-chain liquidity feeds across CEXs and DEXs into a single JSON “context packet.”

• Orchestrated Execution & Risk Layer

  • MaigaXBT routes all trade calls (/mcp/order) through the MCP server, which handles smart routing, cross-venue risk checks, and atomic multi-leg fills.

• Session & Memory Management

  • The MCP layer caches deltas and maintains per-agent sessions, letting MaigaXBT ask only for incremental updates and track in-flight orders globally.

Area	Use-Case
Analysis	Aggregate and compare liquidity, slippage, and funding across venues in real time.
Report	Produce multi-venue PnL reports or “health checks” (e.g. margin utilization) on demand.
Research	Backtest strategies on unified historical context streams spanning all supported markets.
Signal	Generate cross-exchange arbitrage or spread-trade signals, e.g., BTC/ETH basis or funding-rate plays.
Automation	Auto-execute portfolio rebalances, stop-loss baskets, or structured products by fan-out across venues.

Herd Multimodal Model

• Multimodal Market Understanding

  • Herd Multimodal model extends beyond text to ingest charts (order-book heatmaps), on-chain address diagrams, and other form of contents.

• Joint Embedding Pipeline

  • Visual and textual inputs are embedded into a shared latent space, letting MaigaXBT’s agent correlate, say, funding-rate spikes with on-chain whale-transfer patterns.

• Natural-Language & Visual Reasoning

  • The model can generate narrative summaries (“ETH funding rates rose 0.03% as on-chain DEX outflows spiked”) alongside numeric signals.

• Architecture & Embedding Fusion

  1. Modality Encoders

     • Text Encoder: A transformer stack (e.g. Llama 4) producing 1,024-dim embeddings for market commentary, news.

     • Image Encoder: A Vision Transformer fine-tuned on order-book heatmaps and on-chain flow charts, yielding 1,024-dim vectors.

  2. Fusion Layer

     • Cross-Attention Blocks: Interleave text and image embeddings via cross-modal attention, yielding a joint context vector.

     • Prompt Conditioning: The joint vector is concatenated with the numeric MCP context (after a linear projection) to form the final prompt for generation or classification heads.

Area	Use-Case
Analysis	Overlay order-book depth visualizations with price action text to spot hidden liquidity holes.
Report	Auto-compose illustrated market briefs—combining charts, annotated diagrams, and prose—for client distribution.
Research	Visualize and query historical multimodal datasets (e.g., token flows + social-media sentiment videos).
Signal	Trigger signals based on pattern-recognition in chart images (e.g., support/resistance breaks) fused with text indicators.
Automation	Drive GUI-based trading bots that read DEX UIs (screenshots) and execute via MCP, enabling Web-only venues without APIs.