Ironclaw

Key Features

• Rust-based runtime for compile-time memory safety
• Encrypted vault for secret storage and injection
• WebAssembly (Wasm) sandboxing for tool isolation
• Real-time outbound traffic scanning for leak detection
• Network allowlisting to restrict data destinations
• Trusted Execution Environment (TEE) cloud deployment
• Model-agnostic compatibility with major LLM providers

Use Cases

Use Case 1: Secure Credential Management

Problem: AI agents often require API keys or passwords to perform tasks, but these secrets can be exposed to the model through prompt injection attacks.
Solution: Ironclaw uses an encrypted vault that injects credentials only at the host boundary. This ensures the raw secrets are never visible to the AI model itself.
Example: An agent managing a user's GitHub and Slack accounts without the LLM ever having access to the raw OAuth tokens.

Use Case 2: Isolated Execution of Third-Party Tools

Problem: Using community-developed AI skills or plugins carries the risk of malicious code exfiltrating data or accessing unauthorized files.
Solution: Every tool in Ironclaw runs within its own WebAssembly (Wasm) sandbox, isolated from the rest of the system with strict capability-based permissions.
Example: Running a data-scraping tool from a public repository while ensuring it cannot access local files or make unauthorized network calls.

Use Case 3: Privacy-Compliant Cloud Deployment

Problem: Organizations want to deploy AI agents in the cloud but are concerned about the cloud provider or external actors accessing sensitive data in memory.
Solution: Ironclaw deploys agents within Trusted Execution Environments (TEEs) on the NEAR AI Cloud, where data remains encrypted even during processing.
Example: A healthcare assistant processing patient records in a secure enclave that prevents the cloud host from viewing the data.