aegis

mcp
Security Audit
Fail
Health Pass
  • License — License: Apache-2.0
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Community trust — 10 GitHub stars
Code Fail
  • rm -rf — Recursive force deletion command in .github/workflows/release.yml
  • fs module — File system access in .github/workflows/release.yml
  • fs module — File system access in .release-it.json
  • network request — Outbound network request in benchmarks/memory-check.ts
  • Hardcoded secret — Potential hardcoded credential in benchmarks/run.ts
Permissions Pass
  • Permissions — No dangerous permissions requested
Purpose
This tool acts as a local-first proxy and credential isolation server for AI agents. It prevents agents from directly accessing your API keys by injecting secrets at the network boundary and enforcing domain restrictions.

Security Assessment
The tool handles highly sensitive data by design, managing and injecting API keys. The automated rule scan raised two critical failures: a `rm -rf` recursive force deletion command in the release workflow, and a potential hardcoded secret in the benchmark files. Additionally, the codebase makes outbound network requests to route traffic and accesses the file system. While the core proxy concept is built to enhance security, the hardcoded credential finding in the test suite is a significant code hygiene risk, and the proxy inherently controls all outbound network traffic from your agents. Overall risk: Medium.

Quality Assessment
The project is relatively new but actively maintained, with its most recent push happening today. It uses the permissive Apache-2.0 license and includes a highly detailed README with clear instructions. However, community trust is currently very low, with only 10 GitHub stars, meaning the codebase has not yet undergone widespread peer review.

Verdict
Use with caution — the concept is highly useful for AI security, but the hardcoded secrets and low community adoption mean you should thoroughly review the codebase before trusting it with production credentials.
SUMMARY

Credential isolation for AI agents. Local-first transparent proxy — your agent never sees your API keys.

README.md

Aegis

CI
npm version
Docker
License

Stop putting API keys where AI agents can read them.

Aegis is a local-first credential isolation proxy for AI agents. It sits between your agent and the APIs it calls — injecting secrets at the network boundary so the agent never sees, stores, or transmits real credentials.

Aegis demo

How It Works

How Aegis works — agent sends request through Gate, credentials injected at the network boundary

Why?

AI agents (Claude, GPT, Cursor, custom bots) increasingly call real APIs — Slack, GitHub, Stripe, databases. The current pattern is dangerous:

  1. Agents see raw API keys — one prompt injection exfiltrates them
  2. No domain guard — a compromised agent can send your Slack token to evil.com
  3. No audit trail — you can't see what an agent did with your credentials
  4. No access control — every agent can use every credential

Aegis solves all four. Your agent makes HTTP calls through a local proxy. Aegis handles authentication, enforces domain restrictions, and logs everything.

Quick Start

# Install
npm install -g @getaegis/cli

# Initialize (stores master key in OS keychain by default)
aegis init

# Add a credential
aegis vault add \
  --name slack-bot \
  --service slack \
  --secret "xoxb-your-token-here" \
  --domains slack.com

# Start the proxy
aegis gate --no-agent-auth

# Test it — Aegis injects the token, forwards to Slack, logs the request
# X-Target-Host tells Gate which upstream server to forward to (optional if credential has one domain)
curl http://localhost:3100/slack/api/auth.test \
  -H "X-Target-Host: slack.com"

Production Setup (with agent auth)

# Create an agent identity
aegis agent add --name "my-agent"
# Save the printed token — it's shown once only

# Grant it access to specific credentials
aegis agent grant --agent "my-agent" --credential "slack-bot"

# Start Gate (agent auth is on by default)
aegis gate

# Agent must include its token
curl http://localhost:3100/slack/api/auth.test \
  -H "X-Target-Host: slack.com" \
  -H "X-Aegis-Agent: aegis_a1b2c3d4..."

MCP Integration

Aegis is a first-class MCP server. Any MCP-compatible AI agent can use it natively — no HTTP calls needed.

Before (plaintext key in config):

{
  "mcpServers": {
    "slack": {
      "command": "node",
      "args": ["slack-mcp-server"],
      "env": { "SLACK_TOKEN": "xoxb-1234-real-token-here" }
    }
  }
}

After (Aegis — no key visible):

{
  "mcpServers": {
    "aegis": {
      "command": "npx",
      "args": ["-y", "@getaegis/cli", "mcp", "serve"]
    }
  }
}

Generate the config for your AI host:

aegis mcp config claude   # Claude Desktop
aegis mcp config cursor   # Cursor
aegis mcp config vscode   # VS Code
aegis mcp config cline    # Cline
aegis mcp config windsurf # Windsurf

The MCP server exposes three tools:

Tool Description
aegis_proxy_request Make an authenticated API call (provide service + path, Aegis injects credentials)
aegis_list_services List available services (names only, never secrets)
aegis_health Check Aegis status

The MCP server replicates the full Gate security pipeline: domain guard, agent auth, body inspection, rate limiting, audit logging.

Setup Guides

Features

Feature Description
Encrypted Vault AES-256-GCM encrypted credential storage with PBKDF2 key derivation
HTTP Proxy (Gate) Transparent credential injection — agent hits localhost:3100/{service}/path
Domain Guard Every outbound request checked against credential allowlists. No bypass
Audit Ledger Every request (allowed and blocked) logged with full context
Agent Identity Per-agent tokens, credential scoping, and rate limits
Policy Engine Declarative YAML policies — method, path, rate-limit, time-of-day restrictions
Body Inspector Outbound request bodies scanned for credential-like patterns
MCP Server Native Model Context Protocol for Claude, Cursor, VS Code, Windsurf, Cline
Web Dashboard Real-time monitoring UI with WebSocket live feed
Prometheus Metrics /_aegis/metrics endpoint for Grafana dashboards
Webhook Alerts HMAC-signed notifications for blocked requests, expiring credentials
RBAC Admin, operator, viewer roles with 16 granular permissions
Multi-Vault Separate vaults for dev/staging/prod with isolated encryption keys
Shamir's Secret Sharing M-of-N key splitting for team master key management
Cross-Platform Key Storage OS keychain by default (macOS, Windows, Linux) with file fallback
TLS Support Optional HTTPS on Gate with cert/key configuration
Configuration File aegis.config.yaml with env var overrides and CLI flag overrides

Example Integrations

Step-by-step guides with config files and policies included:

  • Slack Bot — Protect your Slack bot token with domain-restricted proxy access
  • GitHub Integration — Secure GitHub PAT with per-agent grants and read-only policies
  • Stripe Backend — Isolate Stripe API keys with body inspection and rate limiting
  • OpenClaw Skill — Aegis skill for OpenClaw personal AI assistant

Security

  • Published STRIDE threat model — 28 threats analysed, 0 critical/high unmitigated findings
  • Full security architecture documentation (trust boundaries, crypto pipeline, data flow)
  • AES-256-GCM + ChaCha20-Poly1305 encryption at rest
  • Domain guard enforced on every request — no bypass
  • Agent tokens stored as SHA-256 hashes — cannot be recovered, only regenerated
  • Request body inspection for credential pattern detection
  • Open source (Apache 2.0) — read the code

How Aegis Compares

.env files Vault/Doppler Infisical Aegis
Agent sees raw key Yes Yes (after fetch) Yes (after fetch) No — never
Domain restrictions No No No Yes
MCP-native No No Adding Yes
Local-first Yes No No Yes
Setup 10 sec 30+ min 15+ min ~2 min

See full comparison for detailed breakdowns against each approach.

Documentation

Document Description
Usage Guide Full reference: CLI commands, configuration, RBAC, policies, webhooks, troubleshooting
Security Architecture Trust boundaries, crypto pipeline, data flow diagrams
Threat Model STRIDE analysis — 28 threats, mitigations, residual risks
Comparison Detailed comparison with .env, Vault, Doppler, Infisical
FAQ Common questions and objections
Roadmap Feature roadmap
Contributing Code style, PR process, architecture overview

Install

# npm
npm install -g @getaegis/cli

# Homebrew
brew tap getaegis/aegis && brew install aegis

# Docker
docker run ghcr.io/getaegis/aegis --help

Requires Node.js ≥ 20 — check with node -v

Development

git clone https://github.com/getaegis/aegis.git
cd aegis
yarn install
yarn build
yarn test

See CONTRIBUTING.md for code style, PR process, and architecture overview.

License

Apache 2.0

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