llama-stack-provider-trustyai-garak 0.1.2

Out-Of-Tree Llama Stack provider for Garak Red-teaming

TrustyAI Garak (trustyai_garak): Out-of-Tree Llama Stack Eval Provider for Garak Red Teaming

About

This repository implements Garak as a Llama Stack out-of-tree provider for security testing and red teaming of Large Language Models with optional Shield Integration for enhanced security testing.

What It Does

• Automated Security Testing: Detects prompt injection, jailbreaks, toxicity, and bias vulnerabilities
• Compliance Scanning: OWASP LLM Top 10, AVID taxonomy benchmarks
• Shield Testing: Compare LLM security with/without guardrails
• Scalable Deployment: Local or Kubernetes/Kubeflow execution
• Comprehensive Reporting: JSON, HTML, and detailed logs with vulnerability scores (0.0-1.0)

Installation

git clone https://github.com/trustyai-explainability/llama-stack-provider-trustyai-garak.git
cd llama-stack-provider-trustyai-garak
python3 -m venv .venv && source .venv/bin/activate
pip install -e .
# For remote execution: pip install -e ".[remote]"

Quick Start

1. Configure Environment

export VLLM_URL="http://your-model-endpoint/v1"
export INFERENCE_MODEL="your-model-name"

2. Start Server

# Basic mode (standard scanning)
llama stack run run.yaml --image-type venv

# Enhanced mode (with shield testing)
llama stack run run-with-safety.yaml --image-type venv

# Remote mode (Kubernetes/KFP)
llama stack run run-remote.yaml --image-type venv

Server runs at http://localhost:8321

3. Run Security Scan

from llama_stack_client import LlamaStackClient

client = LlamaStackClient(base_url="http://localhost:8321")

# Quick 5-minute scan
job = client.eval.run_eval(
    benchmark_id="trustyai_garak::quick",
    benchmark_config={
        "eval_candidate": {
            "type": "model",
            "model": "your-model-name",
            "sampling_params": {"max_tokens": 100}
        }
    }
)

# Check status
status = client.eval.jobs.status(job_id=job.job_id, benchmark_id="trustyai_garak::quick")
print(f"Status: {status.status}")

# Get results when complete
if status.status == "completed":
    results = client.eval.get_eval_job_result(job_id=job.job_id, benchmark_id="trustyai_garak::quick")

Available Benchmarks

Compliance Frameworks

Benchmark ID	Framework	Duration
trustyai_garak::owasp_llm_top10	OWASP LLM Top 10	~8 hours
trustyai_garak::avid_security	AVID Security	~8 hours
trustyai_garak::avid_ethics	AVID Ethics	~30 minutes
trustyai_garak::avid_performance	AVID Performance	~40 minutes

Test Profiles

Benchmark ID	Description	Duration
trustyai_garak::quick	Essential security checks (3 probes)	~5 minutes
trustyai_garak::standard	Standard attack vectors (5 categories)	~1 hour

Duration estimates based on Qwen2.5 7B via vLLM

Advanced Usage

Other Garak Probes

client.benchmarks.register(
    benchmark_id="custom",
    dataset_id="garak",
    scoring_functions=["garak_scoring"],
    provider_benchmark_id="custom",
    provider_id="trustyai_garak",
    metadata={
        "probes": ["latentinjection.LatentJailbreak", "snowball.GraphConnectivity"],
        "timeout": 900
    }
)

Shield Testing

# Test with input shield
client.benchmarks.register(
    benchmark_id="with_shield",
    dataset_id="garak",
    scoring_functions=["garak_scoring"],
    provider_benchmark_id="with_shield",
    provider_id="trustyai_garak",
    metadata={
        "probes": ["promptinject.HijackHateHumans"],
        "shield_ids": ["Prompt-Guard-86M"]  # Input shield only
    }
)

# Test with input/output shields
metadata={
    "probes": ["promptinject.HijackHateHumans"],
    "shield_config": {
        "input": ["Prompt-Guard-86M"],
        "output": ["Llama-Guard-3-8B"]
    }
}

Accessing Reports

# Get report file IDs from job status
scan_report_id = status.metadata["scan.report.jsonl"]
scan_html_id = status.metadata["scan.report.html"]

# Download via Files API
content = client.files.content(scan_report_id)

# Or via HTTP
import requests
report = requests.get(f"http://localhost:8321/v1/openai/v1/files/{scan_html_id}/content")

Remote Execution (Kubernetes/KFP)

Setup

# KFP Configuration
export KUBEFLOW_PIPELINES_ENDPOINT="https://your-kfp-endpoint"
export KUBEFLOW_NAMESPACE="your-namespace"
export KUBEFLOW_EXPERIMENT_NAME="trustyai-garak-scans"
export KUBEFLOW_BASE_IMAGE="quay.io/spandraj/trustyai-garak-provider:cpu" # for gpu - "quay.io/spandraj/trustyai-garak-provider:gpu"

# S3 Configuration (for artifacts)
export AWS_ACCESS_KEY_ID="your-key"
export AWS_SECRET_ACCESS_KEY="your-secret"
export AWS_S3_ENDPOINT="https://your-s3-endpoint"
export AWS_S3_BUCKET="pipeline-artifacts"
export AWS_DEFAULT_REGION="us-east-1"

# Start server
llama stack run run-remote.yaml --image-type venv

Note: If you're running Llama Stack server locally, make sure BASE_URL in run-remote*.yaml is accessible from KFP pods (you can use ngrok to create an accessible endpoint for your local Llama stack service).

Usage

# Same API, runs as KFP pipeline
job = client.eval.run_eval(benchmark_id="trustyai_garak::owasp_llm_top10", ...)

# Monitor pipeline
status = client.eval.jobs.status(job_id=job.job_id, benchmark_id="trustyai_garak::owasp_llm_top10")
print(f"KFP Run ID: {status.metadata['kfp_run_id']}")

Configuration Reference

Provider Config (run.yaml)

providers:
  eval:
    - provider_id: trustyai_garak
      config:
        base_url: ${env.BASE_URL:=http://localhost:8321/v1}
        timeout: ${env.GARAK_TIMEOUT:=10800}
        max_concurrent_jobs: ${env.GARAK_MAX_CONCURRENT_JOBS:=5}
        max_workers: ${env.GARAK_MAX_WORKERS:=5}

Environment Variables

Variable	Default	Description
BASE_URL	http://localhost:8321/v1	Llama Stack service URL
GARAK_TIMEOUT	10800	Max scan timeout (seconds)
GARAK_MAX_CONCURRENT_JOBS	5	Max concurrent scans
GARAK_MAX_WORKERS	5	Shield scanning parallelism

Deployment Modes

Mode	Config File	Features
Basic	run.yaml	Standard scanning
Enhanced	run-with-safety.yaml	+ Shield integration
Remote	run-remote.yaml	KFP execution
Remote+Safety	run-remote-safety.yaml	+ Shield integration

Result Interpretation

• Score Range: 0.0 (secure) to 1.0 (vulnerable)
• Threshold: Scores ≥ 0.5 indicate security concerns
• Reports: Available in JSON, HTML, and log formats via Files API

Examples & Demos

Notebook	Description
01-getting_started	Basic usage and custom probes
02-scan_with_shields	Shield integration testing
03-remote_garak	KFP remote execution