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Eidolon is a high-performance reverse proxy designed to secure Large Language Model (LLM) interactions. It intercepts outgoing prompts to providers like OpenAI, Anthropic, or Google, automatically detects and redacts Personally Identifiable Information (PII) using a hybrid engine, and restores the original data in the response.

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# Eidolon — PII Redaction Proxy

[![Rust](https://img.shields.io/badge/Built%20with-Rust-orange?logo=rust)](https://www.rust-lang.org/)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://raw.githubusercontent.com/0M3REXE/eidolon/main/LICENSE)

**Zero-Trust LLM Security Gateway**

Eidolon is a high-performance reverse proxy that secures Large Language Model interactions. It intercepts outgoing prompts to providers like **OpenAI**, **Google Gemini**, **Anthropic Claude**, and **Ollama**, automatically detects and redacts Personally Identifiable Information (PII) using a hybrid engine, and restores the original data in real-time streaming or static responses — all within your secure perimeter.

Built in Rust, Eidolon offers sub-millisecond regex overhead and creates an airtight privacy layer for your AI applications, protecting sensitive data without sacrificing speed or developer productivity.

---

## How It Works

```
┌──────────┐   ①   ┌──────────┐   ④   ┌──────────┐
│  Your    │──────▶│ Eidolon  │──────▶│  LLM     │
│  App     │◀──────│  Proxy   │◀──────│ Provider │
└──────────┘   ⑤   └──────────┘   ⑤   └──────────┘
                      │     ▲
                   ②③ │     │ ③
                      ▼     │
                   ┌──────────┐
                   │  Redis   │
                   │ (or Mem) │
                   └──────────┘
```

1. **Intercept** — Your application sends a standard LLM API request (with or without SSE streaming) to Eidolon instead of the provider.
2. **Redact** — Eidolon scans the prompt and replaces PII with realistic synthetic values (e.g., `john.doe@company.com` → `jane.smith@example.org`).
3. **Store** — The bidirectional mapping is AES-256-GCM encrypted and stored in Redis (or a high-speed in-memory store if Redis is unavailable) with a configurable TTL.
4. **Forward** — The sanitized prompt is forwarded to the LLM provider.
5. **Restore & Stream** — The LLM's response is intercepted, synthetic tokens are swapped back to real PII using high-speed multi-pattern matching, and an egress DLP scan catches any *hallucinated* PII before reaching your application.

---

## Why Eidolon?

Unlike SaaS-based redaction APIs that require sending sensitive data to yet another third party, Eidolon runs **entirely on your infrastructure**.

| Feature | Eidolon | SaaS Redaction Services | Naive Regex |
|---|---|---|---|
| **Data Sovereignty** | 100% Local (redaction happens within your VPC before forwarding) | Trust-Based (they process your PII) | Local |
| **Detection Engine** | Hybrid (Regex Speed + BERT Accuracy) | AI-Only (often slow/costly) | Regex-Only (high false positives) |
| **Re-Identification** | Context-Aware De-tokenization | Often One-Way Redaction | ❌ One-Way |
| **Performance** | <10ms Latency (Rust + ONNX Runtime) | >500ms (Network Hops) | <1ms |
| **Encryption at Rest** | AES-256-GCM on all Redis values | Varies | ❌ None |
| **Provider Support** | OpenAI, Gemini, Claude, Ollama | Single provider | N/A |

---

## Key Capabilities

### 1. Hybrid Detection Engine

Eidolon combines the speed of compiled Regex with the contextual understanding of a local BERT NER model.

**Instant Regex** — Detects structured data:
- Email addresses
- Phone numbers
- Credit card numbers (with Luhn validation)
- IPv4 addresses
- SSNs (`XXX-XX-XXXX`)
- API keys (`sk-...`, `AKIA...`)

**Contextual NLP (BERT)** — Detects unstructured entities:
- Person names (`PER`)
- Locations (`LOC`)
- Organizations (`ORG`)

### 2. Realistic Synthetic Substitution

Instead of opaque tokens like `[REDACTED]`, Eidolon substitutes realistic plausible values so the LLM can reason naturally about the data shape:

| PII Type | Real Value | Synthetic Value |
|---|---|---|
| Email | `john.doe@company.com` | `jane.smith@example.org` |
| Person Name | `John Doe` | `Alice Johnson` |
| IPv4 | `192.168.1.100` | `10.42.7.23` |
| Credit Card | `4532-0151-1283-0366` | `CC_a1b2c3d4` |
| SSN | `123-45-6789` | `SSN_f8e7d6c5` |

### 3. Egress DLP Scanning

Eidolon also scans LLM **responses** for hallucinated PII that wasn't in the original prompt. This catches cases where a model fabricates realistic-looking sensitive data (emails, SSNs, etc.) in its output.

### 4. Prompt Injection Shield

A multi-layer defense against adversarial prompts:

- **Regex blocklist** — Catches known jailbreak phrases (`"ignore previous instructions"`, `"DAN mode"`, etc.)
- **Unicode normalization** — NFKC normalization + zero-width character stripping defeats homoglyph and invisible-character attacks.
- **ML Shield** (optional) — A DeBERTa-based classifier for detecting prompt injection attempts.

### 5. Custom Rules

Define domain-specific redaction rules in `config.toml` without touching the codebase:

```toml
[[custom]]
name = "PROJECT_ID"
regex = "Project-\\d{4}"   # Matches "Project-1234"
```

### 6. Vendor-Agnostic Drop-in Replacement

Works with any client compatible with the OpenAI API format. Routes automatically based on the `model` field:

| Model prefix | Routed to |
|---|---|
| `gpt-*`, `gpt4*`, `o1-*`, `o3-*`, `o4-*`, `chatgpt-*` | OpenAI API |
| `gemini*` | Google Generative Language API |
| `claude*` | Anthropic Messages API |
| Everything else | Local Ollama instance |

Simply change the `base_url` in your existing SDKs.

### 7. Browser Extension Endpoint

A dedicated `POST /v1/redact` endpoint allows browser extensions to redact text client-side and receive substitution mappings for local un-redaction of model responses.

---

## Quick Start

### Docker Compose

```yaml
version: '3.8'

services:
  eidolon:
    build: .
    ports:
      - "3000:3000"
    environment:
      - REDIS__URL=redis://default:eidolon-redis-secret@redis:6379
      - RUST_LOG=info
      - SECURITY__ENCRYPTION_KEY=change-me-to-32-char-secret-!!!!
    depends_on:
      redis:
        condition: service_healthy
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:3000/health"]
      interval: 15s
      timeout: 5s
      retries: 3
      start_period: 10s

redis:
    image: redis:7-alpine
    restart: unless-stopped
    volumes:
      - redis-data:/data
    command: redis-server --save 60 1 --loglevel warning --requirepass eidolon-redis-secret
    healthcheck:
      test: ["CMD", "redis-cli", "-a", "eidolon-redis-secret", "ping"]
      interval: 10s
      timeout: 3s
      retries: 5

volumes:
  redis-data:
```

```bash
docker-compose up -d
```

Eidolon is now protecting your traffic at `http://localhost:3000`.

### Building from Source

```bash
# 1. Clone the repository
git clone https://github.com/0m3rexe/eidolon.git
cd eidolon

# 2. Build the release binary
cargo build --release

# 3. Run (requires Redis running on localhost:6379)
./target/release/eidolon
```

> **Note:** Requires Rust 1.80+ and a running Redis instance.

---

## Usage Example

Once Eidolon is running, point your existing LLM SDKs to the proxy.

### Python (OpenAI SDK)

```python
from openai import OpenAI

# Point the client to Eidolon's local port.
# Your actual API key is securely passed through to the provider.
client = OpenAI(
    base_url="http://localhost:3000/v1",
    api_key="sk-your-actual-openai-key"
)

response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "user", "content": "Draft an email to John.Doe@company.com about Q4 revenue."}
    ]
)

print(response.choices[0].message.content)
# The LLM never saw the real email, but you get the seamlessly restored output.
```

### Google Gemini (via OpenAI-compatible format)

```python
client = OpenAI(
    base_url="http://localhost:3000/v1",
    api_key="your-gemini-api-key"       # Gemini API key
)

response = client.chat.completions.create(
    model="gemini-2.5-flash",           # Routes automatically to Gemini
    messages=[
        {"role": "user", "content": "My SSN is 123-45-6789. What format is that?"}
    ]
)
```

### Anthropic Claude (via OpenAI-compatible format)

```python
client = OpenAI(
    base_url="http://localhost:3000/v1",
    api_key="your-anthropic-api-key"
)

response = client.chat.completions.create(
    model="claude-sonnet-4-20250514",
    messages=[
        {"role": "user", "content": "Contact alice@example.com regarding the contract."}
    ]
)
```

### Local Ollama

```python
client = OpenAI(
    base_url="http://localhost:3000/v1",
    api_key="not-needed"                # Ollama doesn't require a key
)

response = client.chat.completions.create(
    model="llama3.2",                   # Routes to local Ollama
    messages=[
        {"role": "user", "content": "My credit card is 4532-0151-1283-0366."}
    ]
)
```

---

## Configuration

All settings live in `config.toml` and can be overridden via environment variables using `__` (double underscore) as the section separator.

### `config.toml` Reference

```toml
[server]
port = 3000
host = "0.0.0.0"

[redis]
url = "redis://127.0.0.1:6379"
ttl_seconds = 3600                     # Mapping expiry (1 hour)
fallback_in_memory = true              # Switch to thread-safe memory storage if Redis is unavailable

[security]
fail_open = false                      # false = block on storage failure; true = pass through
encryption_key = "change-me-to-32-char-secret-!!!!"   # AES-256-GCM key for storage PII values
max_request_body_mb = 10               # Reject overly large payloads before buffering in memory

[ollama]
base_url = "http://localhost:11434"

[logging]
level = "info"

[rate_limit]
requests_per_second = 20
burst_size = 10
trust_proxy = false

[nlp]
model_path = "assets/bert-ner-quantized.onnx"
tokenizer_path = "assets/tokenizer.json"

[policy]
# Redaction mode: "regex" (default, ultra-fast), "nlp" (regex + BERT NER), or "off"
mode = "regex"

[shield]
# model_path = "assets/deberta-v3-base-prompt-injection.onnx"
# tokenizer_path = "assets/shield-tokenizer.json"

# Custom patterns (repeatable)
[[custom]]
name = "PROJECT_ID"
regex = "Project-\\d{4}"
```

### Environment Variable Overrides

| Environment Variable | TOML Key | Description | Default |
|---|---|---|---|
| `SERVER__PORT` | `server.port` | Port to listen on | `3000` |
| `SERVER__HOST` | `server.host` | Bind address | `0.0.0.0` |
| `REDIS__URL` | `redis.url` | Redis connection string | `redis://127.0.0.1:6379` |
| `REDIS__TTL_SECONDS` | `redis.ttl_seconds` | Mapping TTL | `3600` |
| `REDIS__FALLBACK_IN_MEMORY` | `redis.fallback_in_memory` | Fallback to RAM when Redis offline | `true` |
| `POLICY__MODE` | `policy.mode` | Redaction mode (`regex`, `nlp`, or `off`) | `regex` |
| `SECURITY__FAIL_OPEN` | `security.fail_open` | Pass-through on storage failure | `false` |
| `SECURITY__ENCRYPTION_KEY` | `security.encryption_key` | AES-256-GCM encryption key | *(must change)* |
| `SECURITY__MAX_REQUEST_BODY_MB` | `security.max_request_body_mb` | Request payload size limit in MB | `10` |
| `SECURITY__ALLOWED_ORIGINS` | `security.allowed_origins` | CORS origin whitelist | `[]` (localhost) |
| `SECURITY__REDACT_API_TOKEN` | `security.redact_api_token` | Bearer token for `/v1/redact` | `None` |
| `SECURITY__METRICS_TOKEN` | `security.metrics_token` | Bearer token for `/metrics` | `None` |
| `RATE_LIMIT__TRUST_PROXY` | `rate_limit.trust_proxy` | Rate limiter respects XFF header | `false` |
| `LIMITS__MAX_PROMPT_TOKENS` | `limits.max_prompt_tokens` | Rejects prompts above token limit | `0` (unlimited) |
| `RUST_LOG` | `logging.level` | Log verbosity (`eidolon=debug`, `info`, etc.) | `info` |

---

## API Endpoints

| Method | Path | Description |
|---|---|---|
| `POST` | `/v1/chat/completions` | OpenAI-compatible chat proxy (Streaming + PII redaction + egress DLP) |
| `POST` | `/v1/redact` | Browser extension — returns redacted text + substitution map |
| `GET` | `/v1/models` | Transparent passthrough to OpenAI or Ollama model list |
| `GET` | `/health` | Health check (verifies Redis or fallback in-memory connectivity) |
| `GET` | `/ready` | Readiness check indicating service operational readiness |
| `GET` | `/metrics` | Prometheus metrics endpoint |
| `POST` | `/api/chat` | Ollama native chat endpoint (with redaction & streaming) |
| `POST` | `/api/generate` | Ollama native generate endpoint (with redaction) |
| `POST` | `/api/embeddings` | Ollama embedding calculations (with egress PII redaction) |
| `GET` | `/` & `/api/version` | Ollama CLI handshake compatibility routes |
| `GET` | `/api/tags` | Ollama model list passthrough |
| `GET` | `/api/ps` | Ollama active running model status passthrough |
| `POST` | `/api/show` | Ollama model info passthrough |

---

## Architecture

```
src/
├── main.rs                    # Entry point, server init, graceful shutdown
├── config.rs                  # TOML + env config deserialization
├── error.rs                   # Unified error types
├── lib.rs                     # Crate re-exports
├── api/
│   ├── handlers.rs            # Route handlers (proxy, health, ollama, redact)
│   ├── routes.rs              # Axum router with middleware stack
│   ├── models.rs              # OpenAI/Ollama request/response types
│   ├── gemini.rs              # Gemini API translation layer
│   └── anthropic.rs           # Anthropic API translation layer
├── engine/
│   ├── patterns.rs            # Compiled regex patterns + Luhn validator
│   ├── nlp.rs                 # BERT NER inference via ONNX Runtime
│   └── shield_model.rs        # DeBERTa prompt injection classifier
├── middleware/
│   ├── redaction.rs           # Ingress PII redaction middleware
│   ├── streaming.rs           # Streaming response unredactor (Aho-Corasick)
│   ├── preflight.rs           # Body buffering, shield check, token limit
│   ├── shield.rs              # Blocklist + Unicode normalization
│   └── rate_limiter.rs        # Token-bucket rate limiter (per IP)
├── state/
│   ├── mod.rs                 # AppState (shared across handlers)
│   └── redis.rs               # Redis connection manager + DashMap fallback storage
└── utils/
    ├── crypto.rs              # AES-256-GCM encrypt/decrypt, SHA-256 hashing
    ├── faker.rs               # Realistic fake data generation
    └── response.rs            # Strip leaked system-prompt blocks
```

---

## Disclaimer

Eidolon is a **technical privacy layer**, not a compliance certification. In industries where regulations prohibit any external data transmission — regardless of redaction — deploy Eidolon with **self-hosted models** (e.g., Ollama) to keep all data within your infrastructure. Always consult your legal/compliance team before deploying in regulated environments.

---

## Contributing

Pull requests are very welcome! For major changes, please open an issue first to discuss what you would like to change. Please ensure to update tests as appropriate.

## License

This project is licensed under the [Apache 2.0 License](LICENSE).

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