# TEEs, MCP & Multimodal

## 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.

<table><thead><tr><th width="138.6015625">Area</th><th>Use-Case</th></tr></thead><tbody><tr><td><strong>Analysis</strong></td><td>Run encrypted on-chain analytics (whale-move detection, mempool scans) without exposing raw data.</td></tr><tr><td><strong>Report</strong></td><td>Generate signed “proof of analysis” summaries that clients can verify against the enclave.</td></tr><tr><td><strong>Research</strong></td><td>Safely test novel strategy code inside TEEs before promoting to production.</td></tr><tr><td><strong>Signal</strong></td><td>Issue inference-only signals (e.g. buy/sell triggers) where the signal payload is attested and privacy-preserving.</td></tr><tr><td><strong>Automation</strong></td><td>Execute flash-loan or liquidation-protection flows end-to-end in-enclave, with on-chain attested proofs of execution.</td></tr></tbody></table>

## 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.

<table><thead><tr><th width="138.5625">Area</th><th>Use-Case</th></tr></thead><tbody><tr><td><strong>Analysis</strong></td><td>Aggregate and compare liquidity, slippage, and funding across venues in real time.</td></tr><tr><td><strong>Report</strong></td><td>Produce multi-venue PnL reports or “health checks” (e.g. margin utilization) on demand.</td></tr><tr><td><strong>Research</strong></td><td>Backtest strategies on unified historical context streams spanning all supported markets.</td></tr><tr><td><strong>Signal</strong></td><td>Generate cross-exchange arbitrage or spread-trade signals, e.g., BTC/ETH basis or funding-rate plays.</td></tr><tr><td><strong>Automation</strong></td><td>Auto-execute portfolio rebalances, stop-loss baskets, or structured products by fan-out across venues.</td></tr></tbody></table>

## 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.

<table><thead><tr><th width="139.08984375">Area</th><th>Use-Case</th></tr></thead><tbody><tr><td><strong>Analysis</strong></td><td>Overlay order-book depth visualizations with price action text to spot hidden liquidity holes.</td></tr><tr><td><strong>Report</strong></td><td>Auto-compose illustrated market briefs—combining charts, annotated diagrams, and prose—for client distribution.</td></tr><tr><td><strong>Research</strong></td><td>Visualize and query historical multimodal datasets (e.g., token flows + social-media sentiment videos).</td></tr><tr><td><strong>Signal</strong></td><td>Trigger signals based on pattern-recognition in chart images (e.g., support/resistance breaks) fused with text indicators.</td></tr><tr><td><strong>Automation</strong></td><td>Drive GUI-based trading bots that read DEX UIs (screenshots) and execute via MCP, enabling Web-only venues without APIs.</td></tr></tbody></table>