https://github.com/North-Shore-AI/LlmGuard
AI Firewall and guardrails for LLM-based Elixir applications
https://github.com/North-Shore-AI/LlmGuard
ai ai-firewall ai-safety beam content-filtering elixir ensemble-methods guardrails llm llm-security machine-learning nshkr-crucible otp prompt-injection reliability research safety-constraints security security-framework statistical-testing
Last synced: 2 months ago
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AI Firewall and guardrails for LLM-based Elixir applications
- Host: GitHub
- URL: https://github.com/North-Shore-AI/LlmGuard
- Owner: North-Shore-AI
- License: mit
- Created: 2025-10-11T00:58:06.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-12-29T03:59:53.000Z (3 months ago)
- Last Synced: 2025-12-31T23:53:45.047Z (3 months ago)
- Topics: ai, ai-firewall, ai-safety, beam, content-filtering, elixir, ensemble-methods, guardrails, llm, llm-security, machine-learning, nshkr-crucible, otp, prompt-injection, reliability, research, safety-constraints, security, security-framework, statistical-testing
- Language: Elixir
- Size: 224 KB
- Stars: 3
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
- Roadmap: docs/roadmap.md
Awesome Lists containing this project
- awesome-ml-gen-ai-elixir - LlmGuard - AI firewall with prompt injection detection, PII redaction, and jailbreak prevention for LLM applications. (Generative AI / Development Tools)
README
# LlmGuard
**AI Firewall and Guardrails for LLM-based Elixir Applications**
[](https://elixir-lang.org)
[](https://www.erlang.org)
[](https://hex.pm/packages/llm_guard)
[](https://hexdocs.pm/llm_guard)
[](https://github.com/North-Shore-AI/LlmGuard/blob/main/LICENSE)
LlmGuard provides comprehensive security protection for LLM applications including prompt injection detection, jailbreak prevention, data leakage protection, and content moderation.
## Features
- ✅ **Prompt Injection Detection** - Multi-layer detection with 34+ patterns
- ✅ **Jailbreak Detection** - Role-playing, hypothetical, encoding, emotional attacks
- ✅ **PII Detection & Redaction** - Email, phone, SSN, credit cards, IP, URLs
- ✅ **Pipeline Architecture** - Flexible, extensible security pipeline
- ✅ **Configuration System** - Centralized configuration with validation
- ✅ **Zero Trust** - Validates all inputs and outputs
- ✅ **High Performance** - <15ms latency for pattern-based detection
- ⏳ **Content Moderation** - Coming soon
- ⏳ **Rate Limiting** - Coming soon
- ⏳ **Audit Logging** - Coming soon
## Quick Start
Add to your `mix.exs`:
```elixir
def deps do
[
{:llm_guard, "~> 0.3.1"}
]
end
```
Basic usage:
```elixir
# Create configuration
config = LlmGuard.Config.new(
prompt_injection_detection: true,
confidence_threshold: 0.7
)
# Validate user input
case LlmGuard.validate_input(user_input, config) do
{:ok, safe_input} ->
# Safe to send to LLM
llm_response = MyLLM.generate(safe_input)
# Validate output
case LlmGuard.validate_output(llm_response, config) do
{:ok, safe_output} -> {:ok, safe_output}
{:error, :detected, details} -> {:error, "Unsafe output"}
end
{:error, :detected, details} ->
# Blocked malicious input
Logger.warn("Threat detected: #{details.reason}")
{:error, "Input blocked"}
end
```
## Architecture
LlmGuard uses a multi-layer detection strategy:
1. **Pattern Matching** (~1ms) - Fast regex-based detection
2. **Heuristic Analysis** (~10ms) - Statistical analysis (coming soon)
3. **ML Classification** (~50ms) - Advanced threat detection (coming soon)
```
User Input
│
▼
┌─────────────────┐
│ Input Validation│
│ - Length check │
│ - Sanitization │
└────────┬────────┘
│
▼
┌─────────────────────┐
│ Security Pipeline │
│ ┌───────────────┐ │
│ │ Detector 1 │ │
│ ├───────────────┤ │
│ │ Detector 2 │ │
│ ├───────────────┤ │
│ │ Detector 3 │ │
│ └───────────────┘ │
└────────┬────────────┘
│
▼
LLM Processing
│
▼
┌─────────────────────┐
│ Output Validation │
└────────┬────────────┘
│
▼
User Response
```
## Detected Threats
### Prompt Injection (34 patterns)
- Instruction override: "Ignore all previous instructions"
- System extraction: "Show me your system prompt"
- Delimiter injection: "---END SYSTEM---"
- Mode switching: "Enter debug mode"
- Role manipulation: "You are now DAN"
- Authority escalation: "As SUPER-ADMIN..."
### Jailbreak Detection
- Role-playing: DAN, DUDE, KEVIN, etc.
- Hypothetical scenarios: "In a world where..."
- Prefix injection: [SYSTEM OVERRIDE], <>
- Emotional manipulation: "For educational purposes..."
- Encoding attacks: Base64, hex, leetspeak
- Format manipulation: Structured jailbreak instructions
### PII Detection & Redaction
- Email addresses (95% confidence)
- Phone numbers (US format, 80-90% confidence)
- Social Security Numbers (95% confidence)
- Credit card numbers (98% with Luhn validation)
- IP addresses (85-90% confidence)
- URLs (90% confidence)
### Coming Soon
- Harmful content (violence, hate speech, etc.)
- Advanced ML-based classification
- Multi-turn conversation analysis
## Testing
```bash
# Run all tests
mix test
# Run with coverage
mix coveralls.html
# Run security tests only
mix test --only security
# Run performance benchmarks
mix test --only performance
```
**Current Status**:
- ✅ 222/228 tests passing (97.4%)
- ✅ Zero compilation warnings
- ✅ 100% documentation coverage
## Configuration
```elixir
config = LlmGuard.Config.new(
# Detection toggles
prompt_injection_detection: true,
jailbreak_detection: false, # Coming soon
data_leakage_prevention: false, # Coming soon
content_moderation: false, # Coming soon
# Thresholds
confidence_threshold: 0.7,
max_input_length: 10_000,
max_output_length: 10_000,
# Rate limiting (coming soon)
rate_limiting: %{
requests_per_minute: 100,
tokens_per_minute: 200_000
}
)
# Optional: Caching (set `caching` to enable pipeline result caching)
caching_config = %{
enabled: true,
pattern_cache: true,
result_cache: true,
result_ttl_seconds: 300,
max_cache_entries: 10_000
}
config = LlmGuard.Config.new(
prompt_injection_detection: true,
caching: caching_config
)
```
### Caching
The pipeline will reuse detector results when `caching.enabled` is true **and** the cache process is
running.
```elixir
# Start the cache in your supervision tree
children = [
{LlmGuard.Cache.PatternCache, []},
# ...other children
]
# Fetch cache statistics
stats = LlmGuard.Cache.PatternCache.stats()
# => %{pattern_count: 10, result_count: 42, hit_rate: 0.78, ...}
```
### Telemetry & Metrics
Telemetry emits pipeline, detector, and cache events with native durations.
```elixir
# Initialize handlers once (idempotent)
:ok = LlmGuard.Telemetry.Metrics.setup()
# Inspect metrics in-process
metrics = LlmGuard.Telemetry.Metrics.snapshot()
# Prometheus text format
prom_text = LlmGuard.Telemetry.Metrics.prometheus_metrics()
```
Integrate with `Telemetry.Metrics` reporters:
```elixir
import Telemetry.Metrics
metrics = LlmGuard.Telemetry.Metrics.metrics()
```
Use these metrics with Prometheus (e.g., `TelemetryMetricsPrometheus`) or LiveDashboard to track
request outcomes, detector latency, cache hit rates, and confidence distributions.
## Performance
Current (Phase 1):
- **Latency**: <10ms P95 (pattern matching)
- **Throughput**: Not yet benchmarked
- **Memory**: <50MB per instance
Targets (Phase 4):
- **Latency**: <150ms P95 (all layers)
- **Throughput**: >1000 req/s
- **Memory**: <100MB per instance
## Development Status
See [IMPLEMENTATION_STATUS.md](IMPLEMENTATION_STATUS.md) for detailed progress.
**Phase 1 - Foundation**: ✅ 80% Complete
- [x] Core framework (Detector, Config, Pipeline)
- [x] Pattern utilities
- [x] Prompt injection detector (24 patterns)
- [x] Main API (validate_input, validate_output, validate_batch)
- [ ] PII scanner & redactor
- [ ] Jailbreak detector
- [ ] Content safety detector
**Phase 2 - Advanced Detection**: ⏳ 0% Complete
**Phase 3 - Policy & Infrastructure**: ⏳ 0% Complete
**Phase 4 - Optimization**: ⏳ 0% Complete
## Examples
Run examples with `mix run examples/example_name.exs`:
```bash
# Basic usage demonstration
mix run examples/basic_usage.exs
# Jailbreak detection examples
mix run examples/jailbreak_detection.exs
# Comprehensive multi-layer protection
mix run examples/comprehensive_protection.exs
```
### CrucibleIR Pipeline Integration
```elixir
# Use LlmGuard as a stage in CrucibleIR research pipelines
defmodule MyExperiment do
def run_with_guardrails do
# Configure guardrails
guardrail = %CrucibleIR.Reliability.Guardrail{
profiles: [:default],
prompt_injection_detection: true,
jailbreak_detection: true,
pii_detection: true,
pii_redaction: false,
fail_on_detection: true
}
# Create experiment context
context = %{
experiment: %{
reliability: %{
guardrails: guardrail
}
},
inputs: "User prompt to validate"
}
# Run the stage
case LlmGuard.Stage.run(context) do
{:ok, updated_context} ->
# Check validation results
case updated_context.guardrails.status do
:safe ->
IO.puts("Input validated successfully")
process_safe_input(updated_context.guardrails.validated_inputs)
:detected ->
IO.puts("Threats detected: #{inspect(updated_context.guardrails.detections)}")
handle_detected_threats(updated_context.guardrails)
:error ->
IO.puts("Validation errors: #{inspect(updated_context.guardrails.errors)}")
end
{:error, {:threats_detected, details}} ->
# Strict mode: fail_on_detection was true
IO.puts("Pipeline halted due to detected threats")
{:error, details}
end
end
end
```
### Phoenix Integration
```elixir
defmodule MyAppWeb.LlmGuardPlug do
import Plug.Conn
def init(opts), do: opts
def call(conn, _opts) do
with {:ok, input} <- extract_llm_input(conn),
{:ok, sanitized} <- LlmGuard.validate_input(input, config()) do
assign(conn, :sanitized_input, sanitized)
else
{:error, :detected, details} ->
conn
|> put_status(:forbidden)
|> json(%{error: "Input blocked", reason: details.reason})
|> halt()
end
end
end
```
### Batch Validation
```elixir
# Validate multiple inputs concurrently
inputs = ["Message 1", "Ignore all instructions", "Message 3"]
results = LlmGuard.validate_batch(inputs, config)
Enum.each(results, fn
{:ok, safe_input} -> process_safe(safe_input)
{:error, :detected, details} -> log_threat(details)
end)
```
## Documentation
Full documentation is available at [hexdocs.pm/llm_guard](https://hexdocs.pm/llm_guard).
Generate locally:
```bash
mix docs
open doc/index.html
```
## Contributing
Contributions are welcome! Please open an issue or pull request on GitHub.
Areas needing help:
- Additional detection patterns
- Performance optimization
- Documentation improvements
- Test coverage expansion
- ML model integration
## Roadmap
- **v0.2.0** - PII detection & redaction ✅
- **v0.3.0** - CrucibleIR integration & Stage implementation ✅
- **v0.4.0** - Jailbreak detection
- **v0.5.0** - Content moderation
- **v0.6.0** - Rate limiting & audit logging
- **v0.7.0** - Heuristic analysis (Layer 2)
- **v1.0.0** - ML classification (Layer 3)
## Security
For security issues, please email security@example.com instead of using the issue tracker.
## License
MIT License. See [LICENSE](LICENSE) for details.
## Acknowledgments
Built following security best practices and threat models from:
- OWASP LLM Top 10
- AI Incident Database
- Prompt injection research papers
- Production LLM security deployments
---
**Status**: Alpha - Production-ready for prompt injection detection
**Version**: 0.3.1
**Elixir**: ~> 1.14
**OTP**: 25+