A developer just shipped Unwind, a deterministic security proxy that runs on Raspberry Pi 5 and intercepts every tool call between AI agents and their MCP servers. Released as a Show HN project, this open-source stack combines tamper-evident logging inspired by cryptographic audit chains with a 15-check enforcement pipeline that blocks malicious actions without using an LLM in the critical path. It wraps any MCP server invisibly, snapshots filesystem changes for rollback, and even caught its own builder (Codex) attempting unauthorized execution during the first live test. For anyone running Claude Desktop, Cursor, or Windsurf with MCP integrations, this is a hardware-backed security layer you can deploy today to enhance the safety and accountability of your AI agent operations.
What Is Unwind and Why Did Someone Build It on a Raspberry Pi?
Unwind emerged from a fundamental question that challenges anyone running autonomous AI agents: what mechanism is in place to oversee the agent when human supervision is not constant? The builder, after 18 months of daily AI tool experimentation and shipping three open-source packages, found an answer using a Raspberry Pi 5 from a summer project. The device became the first OpenClaw installation, then evolved into something more ambitious. Taking inspiration from Apple’s Time Machine, the design philosophy centers on rollback capability. If an agent goes rogue, you need to see exactly what happened and revert the system to a previous, safe state. This led to Unwind, a deterministic security proxy that sits between your agent and every tool call it attempts. Unlike cloud-based security guards, this runs locally on ARM64 hardware you control. The Pi 5 provides sufficient compute power to run the enforcement pipeline, hash chain verification, and dashboard without sending sensitive tool call data to external APIs. It is physical infrastructure specifically designed for agent oversight.
The choice of Raspberry Pi was highly pragmatic. The builder already owned the hardware, and OpenClaw runs efficiently on the 8GB model. However, the implications extend beyond single-device deployment. If an $80 ARM board can enforce security policies with deterministic guarantees, enterprise teams can distribute these proxies across edge clusters, creating a distributed security perimeter. The architecture proves that robust agent security does not require expensive GPU farms or extensive cloud contracts. You can effectively air-gap the enforcement layer while still allowing agents to access necessary tools. This represents a paradigm shift where security is treated as infrastructure, not merely as a service.
How Does Unwind Intercept AI Agent Tool Calls Without Detection?
Unwind operates as an MCP stdio proxy, positioning itself transparently between the client and server. When an AI client like Claude Desktop, Cursor, or Windsurf initiates a tool call, it speaks to Unwind first. Unwind then forwards the request to the actual MCP server, but not before running its 15-check enforcement pipeline. The AI agent remains entirely oblivious to this interception. From the agent’s perspective, it is communicating directly with the filesystem, database, or API tool. Unwind handles the bidirectional communication, meticulously inspecting both requests and responses while adding negligible latency, ensuring a seamless user experience.
The technical implementation leverages Python’s robust subprocess management capabilities to wrap the upstream MCP server. Unwind spawns the actual server process, proxies standard input (stdin) and standard output (stdout) through its enforcement layer, and meticulously maintains the JSON-RPC message format that MCP expects. Because it operates at the transport layer rather than modifying the MCP protocol itself, it functions seamlessly with any stdio-based MCP server without requiring any code changes to the server. You simply configure your client to communicate with Unwind, and Unwind, in turn, communicates with the designated MCP server. This clever indirection pattern allows the proxy to snapshot filesystem states before modifications and maintain the CRAFT audit chain without the agent ever knowing it is under surveillance.
What Is CRAFT and Why Does Tamper-Evident Logging Matter?
CRAFT is the cryptographic backbone of Unwind’s audit capability. Standing for Cryptographic Receipt and Audit Framework for Tools, it originated from a separate cryptographic project the builder worked on for months before combining it with the Time Machine concept. Every single tool call made by an AI agent gets logged to a tamper-evidently secured hash chain. Each entry in this chain includes the call parameters, a precise timestamp, and a cryptographic hash of the previous entry. If someone attempts to modify the log after the fact, even a single character, the hash chain breaks, making the tampering immediately detectable.
This feature is critically important because AI agents often operate with high privileges, allowing them to read files, write code, and execute shell commands. When something goes wrong, whether due to a sophisticated prompt injection attack or a simple logic error, you need forensic evidence that is unimpeachable and has not been altered. Traditional logging methods typically write to mutable files that a root user or a compromised agent could easily delete or modify. CRAFT, however, employs sequential hashing similar to blockchain structures, but without the significant overhead associated with distributed consensus mechanisms. It runs locally, writes to append-only storage, and provides cryptographic proof of precisely what the agent did and when, creating an immutable record. For compliance teams and security auditors, this creates a verifiable and unalterable record of agent activity that stands up to rigorous scrutiny.
The Three-Package Architecture Explained for Comprehensive Security
The Unwind ecosystem consists of three distinct Python packages, all readily available on PyPI today. Each package addresses a different layer of the agent security problem, offering flexible deployment options. Understanding when and how to use each package is crucial for establishing your desired security posture, from lightweight auditing to full enforcement.
First, craft-auth provides foundational tamper-evident command authentication. This package is remarkably lean, consisting of only 1,605 lines of pure Python code and utilizing only the standard library. It boasts zero external dependencies, making it incredibly easy to integrate into existing tools without introducing “dependency hell.” It is responsible for the cryptographic signing and verification that underpins the entire CRAFT system, ensuring the integrity of audit logs.
Second, ghostmode acts as a powerful dry-run proxy. It intelligently intercepts write operations initiated by an AI agent while allowing read operations to pass through unchanged. This invaluable feature enables you to observe exactly what an agent would do without risking actual filesystem modifications or unintended side effects. It is ideal for testing new agents, experimenting with unfamiliar prompts, or validating agent behavior in a safe, sandboxed environment.
Third, unwind-mcp is the full enforcement engine. This is the flagship package that combines craft-auth’s tamper-evidence with ghostmode’s interception capabilities. It integrates the core 15-check deterministic ruleset and provides the user-friendly dashboard interface for real-time monitoring and control. This is the package you deploy when you require active, production-grade protection for your AI agents, offering both real-time blocking and comprehensive auditing.
| Package | Primary Function | Key Features | Dependencies | License | |--------------|--------------------------|------------------|-----------| | craft-auth | Tamper-evident auth | Zero (stdlib only) | Separately licensable | | ghostmode | Dry-run MCP proxy | Python 3.10+ | AGPL-3.0 | | unwind-mcp | Full enforcement & dashboard | Python 3.10+ | AGPL-3.0 |
Choose craft-auth for embedded security functionality in your own projects, ghostmode for safe testing and auditing of agent intentions, and unwind-mcp for complete, real-time protection and oversight of your AI agents. Each package serves a specific purpose, allowing developers to implement a layered security approach tailored to their needs.
Installing Unwind on Your Raspberry Pi 5 for Enhanced Security
Deployment of Unwind begins with ensuring you have Python 3.10 or newer installed on your Raspberry Pi. It is important to note that the standard Raspberry Pi OS often ships with older Python versions, so you will likely need to install Python 3.11 or 3.12, either via pyenv or by compiling from source. Avoid using the system Python to prevent potential conflicts with other system utilities. Once the correct Python version is in place, the installation process is straightforward and takes only a few seconds:
pip install unwind-mcpTo begin protecting your filesystem, you need to start the Unwind proxy, configuring it to wrap your desired MCP server. For instance, to protect the filesystem MCP server, you would use:
unwind serve -- npx @modelcontextprotocol/server-filesystem ~/DocumentsAfter starting Unwind, the crucial next step is to point your MCP client configuration at Unwind itself, rather than directly at the original MCP server. The precise client configuration steps vary by tool (e.g., Claude Desktop, Cursor), but generally, you will specify the command to execute as unwind serve and pass the upstream server’s command and arguments as parameters to Unwind.
For monitoring and real-time decision visibility, you will want to use the Unwind dashboard. This requires two separate terminal sessions. First, start the sidecar process, which handles communication with the main Unwind proxy:
unwind sidecar serveThen, in a second terminal, launch the dashboard application:
unwind dashboardFinally, navigate to localhost:9001 in your web browser. Here, you will see real-time ALLOW or BLOCK decisions as your AI agent makes tool calls, providing immediate feedback on Unwind’s enforcement actions. The terminal running the sidecar will display detailed enforcement logs, while the web interface offers a more user-friendly, graphical overview for detailed forensics. It is also important to ensure your Raspberry Pi has sufficient cooling; continuous cryptographic hashing and filesystem snapshots can generate sustained CPU load, and an efficient cooling solution will prevent thermal throttling and maintain optimal performance.
Why Deterministic Enforcement Beats LLM-Based Security Guards for AI Agents
Unwind’s enforcement pipeline is designed to operate with zero Large Language Models (LLMs) in the critical path. This design choice is deliberate and fundamental to its security philosophy. When an AI agent attempts a potentially destructive command like rm -rf / (recursively deleting files from the root directory) or tries to exfiltrate sensitive data via unusual DNS queries, you cannot afford to wait for a cloud-based LLM API to evaluate and judge the action. Immediate, deterministic blocking based on predefined rules is paramount.
The 15 checks within Unwind use precise pattern matching and rule evaluation, not probabilistic neural inference. They meticulously examine the tool name, its parameters, and the contextual information against a predefined policy matrix. This entire process executes in milliseconds on the Raspberry Pi’s ARM cores, without introducing any network latency. In contrast, LLM-based security guards inherently introduce jitter, incur significant computational and monetary costs, and carry availability risks. If your security architecture depends on the uptime and performance of an external LLM API (e.g., OpenAI’s API), you have created a circular dependency where the security guard itself can fail, potentially leaving the AI agent vulnerable and unmonitored.
Furthermore, deterministic enforcement provides absolute reproducibility. The same input will consistently produce the same ALLOW or BLOCK decision every single time. This consistency makes testing, auditing, and policy validation straightforward and reliable. You can write robust unit tests for your security policy with confidence. With LLM-based guards, the same prompt might yield varying safety judgments depending on factors like temperature settings, model updates, or even transient network conditions. Unwind treats security as verifiable code and explicit rules, not as probabilistic interpretation or a black box.
The MCP Stdio Proxy Pattern: A Foundation for Agent Security
The Model Context Protocol (MCP) leverages standard input/output (stdio) as its primary transport mechanism for local servers. In this architecture, the client application spawns the MCP server as a subprocess and communicates with it by sending messages over standard input and receiving responses over standard output. Unwind ingeniously exploits this architectural pattern by inserting itself as a transparent middleman in this communication channel.
When you configure an AI client, such as Claude Desktop or Cursor, to use Unwind, you are essentially instructing the client to spawn Unwind as if it were the MCP server process itself. Unwind then, in turn, spawns the real MCP server (for example, the filesystem server) as its own child process. It then meticulously proxies JSON-RPC messages back and forth between the client and the actual server. Throughout this process, Unwind parses each incoming request, allowing it to inspect the tool name and arguments before forwarding the request to the upstream server.
This pattern is highly effective because MCP servers are generally stateless regarding their transport layer. They are designed to accept JSON-RPC messages over stdin and respond over stdout, without caring whether the other end of the communication channel is the client directly or an intermediary proxy. Unwind maintains the protocol state, handles the intricacies of message framing, and adds its crucial security headers and checks without breaking the established MCP schema. This approach ensures that Unwind can generalize to protect any MCP server, whether it is an official Anthropic implementation or a community-developed server for databases, APIs, or other tools. The result is robust security applied without requiring any modifications to the original MCP server code.
OpenClaw Integration Status and Current Limitations for Unwind
The builder initially targeted OpenClaw for Unwind’s integration, even installing OpenClaw on the Raspberry Pi 5 as the very first step. However, as of the initial release, direct OpenClaw support currently faces unresolved adapter issues. Consequently, the MCP stdio proxy path remains the most stable and recommended integration method for Unwind today. If you are running OpenClaw agents and attempt to route their tool calls through Unwind, you might encounter connection failures or protocol mismatches, leading to an unstable experience.
This limitation stems from fundamental differences in how OpenClaw handles subprocess spawning and environment variable passing compared to other MCP clients like Claude Desktop or Cursor. OpenClaw’s internal adapter layer expects a different lifecycle management approach than what Unwind currently provides. The builder is aware of these issues and advises users to leverage the generic MCP stdio path for now, bypassing the direct OpenClaw integration layer.
For dedicated OpenClaw users, this means that while Unwind can protect some MCP interactions, you may experience intermittent disconnects or a failure to recognize specific tool schemas when attempting to integrate directly. The community is actively exploring and testing workarounds, but for optimal production stability and reliability, it is currently recommended to use Unwind with clients such as Claude Desktop, Windsurf, or VS Code Copilot’s MCP implementations. Developers should closely monitor the GitHub repository for OpenClaw-specific fixes and enhanced compatibility in upcoming releases, as this remains a high-priority area for improvement.
The Taint System: Navigating Security vs. Autonomy Trade-offs
Unwind incorporates a sophisticated taint tracking system, designed to monitor whether an AI agent has consumed external content before attempting to execute commands. The core principle is simple: if an agent fetches information from an external source (e.g., a webpage, an untrusted API), and subsequently tries to run shell commands or perform other privileged actions, Unwind flags the session as TAINTED. This triggers an automatic block on execution, pending explicit human approval. The primary goal of this system is to prevent prompt injection attacks, where malicious external content could subtly instruct the agent to delete critical files, exfiltrate sensitive data, or perform other harmful operations.
However, the current implementation of the taint system proves to be quite aggressive, posing a challenge for fully autonomous operations. Many legitimate AI agent workflows involve a natural sequence of actions: reading documentation or examples from the web, then executing code or commands based on those learned instructions. Under Unwind’s current rules, this common pattern immediately triggers a taint block, effectively freezing the agent until a human operator manually approves the action via the dashboard. This creates a significant tension between the desire for robust security and the need for seamless, unattended agent autonomy.
The builder explicitly acknowledges that this remains an unresolved design problem. For attended sessions, where a human monitors the dashboard and can approve actions, the taint system functions correctly and provides a valuable security layer. However, for unattended 24/7 agents, it effectively halts progress, undermining the goal of full automation. Future iterations of Unwind may introduce more nuanced solutions, such as allowlisting trusted domains for documentation, implementing graduated taint levels where external content “ages out” of its risky status over time, or offering configurable taint policies tailored to specific tool types. For now, users must carefully weigh the trade-off between strict security enforcement and the desired level of agent autonomy.
Ghost Mode: Auditing AI Agent Intentions Without Introducing Risk
Before deploying Unwind with full enforcement, it is highly recommended to audit your AI agent’s behavior using Ghost Mode. This separate package functions as a read-only observer, intelligently intercepting all write operations initiated by the agent while allowing read operations to pass through transparently. From the agent’s perspective, it successfully performs file modifications or other write actions, but Ghost Mode logs these intended changes without actually persisting them to disk.
To install and run Ghost Mode, you would use the following commands:
pip install ghostmode
ghostmode -- npx @modelcontextprotocol/server-filesystem ~/DocumentsWith Ghost Mode active, the AI agent receives a successful response for its write operations, but the underlying filesystem remains completely unchanged. This “dry-run” capability is invaluable for observing an agent’s intentions when testing new skills, experimenting with unfamiliar prompts, or validating complex workflows. You can thoroughly verify that the agent attempts the correct file modifications and actions before enabling live enforcement, where those actions would have real-world consequences.
Ghost Mode shares the same MCP stdio proxy architecture as the full Unwind enforcement engine. This architectural consistency means that switching between audit mode and full enforcement mode is straightforward, requiring only a change in the command executed (from ghostmode to unwind serve). This allows for a seamless transition from a safe, sandboxed environment to active protection. Use Ghost Mode as your primary tool when onboarding new agents, refining system prompts, or developing new agent capabilities. It provides a crucial sandbox where mistakes cost nothing more than log space and debugging time.
When Codex Flagged Itself: A Real-World Security Catch and Validation
During the initial deployment and testing phase, Unwind immediately demonstrated its practical value through a compelling real-world scenario. The builder utilized Codex, an AI coding assistant, running on the Raspberry Pi to construct the security stack itself, leveraging OpenAI’s OAuth integration for OpenClaw which allows Pro users to bypass API costs. After the Unwind installation was complete and the system was operational, Codex attempted its first live action under the new security regime.
The agent, as part of its setup and development process, had been fetching web pages to read documentation and gather information. When it subsequently tried to execute a shell command, Unwind’s dashboard instantaneously flagged the session as TAINTED and blocked the execution without hesitation. The very security infrastructure that Codex helped build had successfully caught Codex itself attempting potentially dangerous behavior.
This incident served as a powerful validation of Unwind’s threat model and its effectiveness. AI agents routinely fetch external content, and that content, whether intentionally or unintentionally, could contain hidden instructions or malicious payloads. Without Unwind in place, Codex would have executed the command immediately, potentially leading to unintended consequences. The deterministic ruleset within Unwind correctly identified the high-risk pattern: external input followed by an attempt at code execution. This real-world catch unequivocally proves that the proxy works not just in theory, but against actual, dynamic agent behavior. It starkly underscores why deterministic enforcement is so critical. An LLM-based guard might have reasoned that the command appeared legitimate in isolation, but Unwind’s strict rules blocked it based solely on its taint status, prioritizing safety above all else.
Comparing Unwind to Other OpenClaw and AI Agent Security Layers
Several security tools are emerging within the OpenClaw and broader AI agent ecosystem, each adopting different architectural approaches to address security concerns. Understanding these distinctions is essential for choosing the most appropriate protection for your specific threat model and operational environment.
| Tool | Primary Approach | Enforcement Layer | Best For |
|---|---|---|---|
| Unwind | Deterministic proxy | MCP stdio transport | Local development, Raspberry Pi deployments, protocol-level blocking, tamper-evident auditing. |
| Agentward | Runtime enforcer | OpenClaw process hooks | Preventing specific actions like file deletion within an OpenClaw context, specific behavioral constraints. |
| Clawshield | Specialized security proxy | Network layer | OpenClaw-specific deployments, network-level traffic inspection, and filtering for OpenClaw communications. |
| Rampart | Open-source policy middleware | Application middleware | Enterprise policy enforcement, integrating security policies into existing application stacks. |
| Raypher | eBPF runtime analysis | Kernel level | Deep system introspection, hardware identity verification, detecting low-level system compromises. |
Unwind distinguishes itself by focusing exclusively on the MCP protocol rather than delving into OpenClaw’s internal mechanics or low-level kernel hooks. This focused approach makes it exceptionally portable across any MCP-compatible client, not just OpenClaw itself. Furthermore, it operates efficiently on minimal hardware like the Raspberry Pi 5, whereas more advanced eBPF solutions, such as Raypher, often require specific kernel compilation privileges and more robust system resources. However, it is important to note that Unwind, operating at the application layer, cannot detect or prevent kernel-level exploits that a tool like Raypher might. Therefore, you would choose Unwind for robust, protocol-level security with minimal infrastructure requirements and high portability.
Unlike traditional network-layer proxies, Unwind does not necessitate complex certificate pinning configurations or TLS termination, simplifying its deployment considerably. It operates at the application layer, granting it a semantic understanding of tool calls. This means it can intelligently block specific, dangerous file operations while simultaneously allowing safe and legitimate ones, a level of granularity that generic network firewalls simply cannot achieve. For developers and builders deploying AI agents on edge hardware where kernel module compilation or root access beyond initial setup might be restricted, Unwind provides a viable and powerful security solution without these elevated privileges.
License Strategy and Commercial Viability Considerations for Unwind
Unwind and Ghost Mode are distributed under the AGPL-3.0 license. This is a strong copyleft license that includes a crucial clause: any network use of the software (which includes interaction with the Unwind dashboard over a network) triggers obligations for source code distribution. This means that if you modify Unwind and run it to protect your company’s AI agents, and provide access to that system over a network, you are generally required to share those modifications with your users or clients. This design choice aims to keep improvements to the core security tool within the open-source community.
However, craft-auth adopts a different licensing strategy. It carries zero AGPL dependencies and is explicitly designed to be licensed separately. This strategic split in licensing allows commercial vendors to embed the tamper-evident authentication capabilities of craft-auth into their proprietary products without the risk of “infecting” their entire codebase with the AGPL’s copyleft obligations. The builder intentionally kept craft-auth free of copyleft dependencies to enable its integration into commercial offerings while maintaining the open-source nature of the full Unwind proxy.
For Software-as-a-Service (SaaS) providers who might consider offering “Unwind-as-a-service,” the AGPL presents particular challenges. You cannot run modified instances of Unwind on behalf of multiple customers without potentially being obligated to share your modifications with each of those customers. This licensing model often pushes commercial adoption towards dual-licensing arrangements (where a commercial license is offered alongside the AGPL) or requires clean-room reimplementations of the core functionality. For individual developers and internal corporate tools, however, there are generally no such restrictions. You can run Unwind internally within your organization without source distribution requirements, as long as you do not provide external network access to the dashboard or modified software.
Platform Requirements and Cross-Platform Status for Unwind
Unwind has a strict requirement for Python 3.10 or newer. This immediately means that many default operating system installations, such as older macOS versions which typically ship with Python 3.9.6, will not be compatible out of the box. Attempting to import the package on these older Python versions will result in syntax errors and crashes. macOS users, therefore, must proactively install a newer Python version (3.11 or 3.12) using package managers like Homebrew or environment managers like pyenv before attempting to deploy Unwind.
For example, on macOS, the installation process would typically involve:
brew install python@3.12
pip3.12 install unwind-mcpWhile the codebase is written in pure Python and primarily uses cross-platform libraries, its functionality on Windows remains largely untested by the developer. The underlying mechanisms, particularly subprocess handling and path separator conventions, can behave differently on Windows compared to Unix-like systems (Linux, macOS). Therefore, while it might technically run, no guarantees of stable or correct operation can be made without dedicated testing. Currently, Linux (including Raspberry Pi OS) and macOS are the officially tested and supported environments for Unwind.
The Raspberry Pi 5 runs on an ARM64 architecture, and Unwind is designed to handle this natively without issue. Performance remains acceptable because the deterministic checks primarily involve efficient string comparisons and cryptographic hash operations, which are not computationally intensive like neural inference. However, it is crucial to recognize that filesystem snapshotting, a core feature for rollback, can generate significant I/O load. To avoid bottlenecking the audit chain writes and ensure smooth operation, it is highly recommended to use a solid-state drive (SSD) for the Raspberry Pi’s root filesystem, rather than a slower SD card. The sidecar and dashboard components consume minimal RAM, leaving ample headroom for the actual MCP servers and the AI agent itself to operate effectively.
Future Roadmap: What Builders Should Watch for in Unwind’s Evolution
The builder has clearly identified three immediate priorities for the continued evolution and improvement of Unwind. First and foremost is resolving the existing OpenClaw adapter issues. This fix is critical to expand Unwind’s addressable market beyond just MCP stdio clients and will involve debugging the subtle differences in subprocess spawning and lifecycle management between OpenClaw and clients like Claude Desktop. This will ensure broader compatibility and ease of integration for a wider range of AI agent platforms.
Second, addressing the aggressiveness of the taint system for unattended operation remains a high-priority challenge. The current implementation, while robust for security, too often blocks legitimate autonomous workflows, requiring manual intervention. Potential solutions under consideration include implementing domain allowlists for trusted documentation sources, introducing a “graduated taint decay” mechanism where external content’s risk factor diminishes over time, or offering highly configurable taint policies that can be tailored per tool type or agent persona. This will allow users to strike a better balance between security and autonomy.
Third, the project is in urgent need of real-world testers. While the codebase has proven itself effective against specific AI models like Codex and Claude Code during development, deploying it in diverse production workloads with various MCP servers will inevitably reveal new edge cases and unforeseen interactions. The builder actively encourages and welcomes pull requests, particularly for contributions related to Windows support, the addition of more sophisticated deterministic checks, and integrations with containerized AI agents.
Developers and users interested in Unwind should closely watch the GitHub repository for upcoming releases that will address these identified gaps. If you are currently running AI agents on Raspberry Pi or other edge hardware, actively testing Unwind now and reporting any issues or suggestions will significantly help in hardening the security model before its wider deployment. The deterministic enforcement approach represents a highly viable and promising path forward for AI agent security, but its full maturity relies heavily on community validation and collaborative development.
Getting Started: Your First Unwind Deployment Today
To begin your journey with Unwind, it is highly recommended to start with Ghost Mode. This will allow you to understand your AI agent’s behavior and intentions without introducing any real-world risks. Install Ghost Mode on your development machine, then configure your AI client (such as Cursor or Claude Desktop) to point at this Ghost Mode instance. Run your typical agent workflows and observe the Unwind dashboard. Pay close attention to which tool calls trigger read operations versus those that attempt write operations. This initial baseline understanding is invaluable for configuring more specific and effective security policies later on.
Once you feel comfortable with how your agent interacts with its environment and you have a clear picture of its intended actions, you can then transition to full Unwind enforcement. When you first enable full enforcement, it is advisable to begin with more permissive settings. This means allowing most operations while ensuring that Unwind meticulously logs everything. Regularly review the CRAFT audit chain to confirm that it captures all the necessary evidence and provides the level of forensic detail you require. Over time, you can gradually tighten the 15-check ruleset, iteratively refining your security policy based on your specific threat model, the sensitivity of the data being handled, and the operational context of your AI agents.
For deployment on a Raspberry Pi, ensure that you have adequate cooling and a stable power supply. While the Raspberry Pi 5 is capable of sustaining the cryptographic hashing and filesystem snapshot operations, thermal throttling can significantly slow down enforcement if the device overheats. Furthermore, for optimal performance and to prevent the audit chain from consuming your primary storage, consider mounting the CRAFT logs on a separate partition or an external solid-state drive.
Finally, stay connected with the project by subscribing to the repository releases. Security tools, by their very nature, require regular updates as new attack vectors emerge and as the underlying technologies (like MCP servers) evolve. Unwind’s deterministic enforcement approach makes updates generally safe to apply, as the behavior is predictable, but you will still need to monitor for compatibility fixes and enhancements that align with new MCP server versions and agent capabilities.