Open Source AI Agent Gateway: Secure Credentials Management for Agent Configurations

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A security-focused open-source platform named AI Agent Gateway has been introduced to address vulnerabilities in AI agent configurations by eliminating direct exposure of credentials.

Introduction to AI Agent Gateway

The tool functions as an intermediary between AI agents and their connected services, including MCP tool servers that interface with platforms like GitHub and Jira, as well as model providers that process prompts.

How the Gateway Operates

Unlike traditional setups, the gateway operates within the user’s local environment without requiring a Tuskira account. In typical workflows involving tools such as Claude Code, Cursor, and custom ticket bots, model keys and MCP credentials are often embedded directly into agent configurations on individual devices and CI/CD runners.

Security Risks and Mitigation

This practice creates significant risks, as unauthorized access to these files could grant adversaries full control over the agents. Tuskira’s solution mitigates this by enforcing permission checks at the moment an agent initiates a service call. Additionally, it restricts the set of tools visible to each agent, ensuring that even if an agent is coerced into using an unlisted tool, the request is denied.

Key Features and Functionality

The gateway supports model traffic by redirecting SDK requests through its infrastructure. It natively integrates with Anthropic, OpenAI, and Gemini, with compatibility for Anthropic via AWS Bedrock. All interactions are logged, including token usage and estimated costs per call.

Permission Checks and Tool Restrictions

The gateway enforces permission checks at the moment an agent initiates a service call. It restricts the set of tools visible to each agent, ensuring that even if an agent is coerced into using an unlisted tool, the request is denied.

Critical Configuration Defaults

Two critical configuration defaults are highlighted. First, profiles are only active when explicitly bound to keys. Unbound keys can dynamically request any profile via a header, potentially allowing unauthorized access. Binding keys to specific profiles restricts their capabilities to the permissions defined in the associated profile.

Profile Binding and Key Restrictions

For example, a sample CI profile limits access to a single tool. The demo environment retains LLM request and response bodies, with a 1 MiB cap per file, enabling visibility into prompts and generated code. Users can disable this storage feature if needed.

Docker Compose and Network Security

The provided Docker Compose setup allows outbound traffic to the host machine and loopback addresses for testing but advises removing these exceptions before deployment. By default, the gateway blocks connections to private networks, loopback ranges, and cloud metadata endpoints.

Compatibility and Integration

The tool is compatible with macOS and Linux, with Windows support via WSL2 still under evaluation. The repository includes working examples for Claude Code, Cursor, VS Code, Codex CLI, Python-based agents, and Kubernetes integrations.

Conclusion

AI Agent Gateway is freely available on GitHub, offering a centralized approach to securing AI agent workflows by decoupling credentials from configuration files and enforcing strict access controls during service interactions.

The demo environment retains LLM request and response bodies, with a 1 MiB cap per file, enabling visibility into prompts and generated code.