LLM Security Scanner

Garak
LLM Vulnerability Scanner

The industry-leading open-source vulnerability scanner specifically designed for Large Language Models. Detect prompt injection, jailbreaking, and other AI-specific vulnerabilities with comprehensive testing frameworks.

Open Source
Python-based
Enterprise Ready
garak-scanner
$ pip install garak
$ garak --model-name "gpt-3.5-turbo" \
--probes promptinject \
--detectors base
🔍 Scanning for vulnerabilities...
⚠️ Found 3 potential issues
✅ Scan complete - Report generated
50+
Vulnerability Types
200+
Test Probes
15+
Model Integrations
10K+
Active Users

Comprehensive LLM Security Testing

Garak provides extensive testing capabilities designed specifically for AI model security assessment, covering all major vulnerability categories and attack vectors.

Prompt Injection Detection
Advanced detection of prompt injection attacks including direct, indirect, and context-aware injection techniques.
Direct InjectionIndirect InjectionContext PoisoningSystem Prompt Extraction
Jailbreaking Assessment
Comprehensive testing for jailbreaking attempts and safety filter bypasses across multiple attack vectors.
Role PlayingHypothetical ScenariosEncoding AttacksMulti-turn Exploitation
Data Extraction Testing
Identifies vulnerabilities that could lead to training data extraction or sensitive information leakage.
Training Data ExtractionPII LeakageModel InversionMembership Inference
Model Integration
Seamless integration with popular LLM APIs and local model deployments for comprehensive testing.
OpenAI APIHugging FaceLocal ModelsCustom Endpoints
Reporting & Analytics
Detailed vulnerability reports with risk scoring, remediation guidance, and compliance mapping.
Risk ScoringOWASP MappingCompliance ReportsTrend Analysis
CI/CD Integration
Automated security testing integration for continuous deployment pipelines and DevSecOps workflows.
GitHub ActionsJenkinsGitLab CICustom Webhooks

Modular Architecture

Garak's modular design allows for flexible testing configurations and easy integration with existing security workflows. Each component can be customized and extended for specific use cases.

Probe Engine

Core testing engine that executes vulnerability probes against target models with configurable parameters.

Detector Framework

Intelligent detection system that analyzes model responses to identify potential vulnerabilities and security issues.

Generator System

Automated test case generation system that creates diverse attack scenarios based on vulnerability patterns.

Reporting Module

Comprehensive reporting system that generates detailed vulnerability assessments and remediation recommendations.

Architecture Flow

1
Initialize target model connection
2
Load selected probe configurations
3
Generate test cases and attack vectors
4
Execute probes against target model
5
Analyze responses with detectors
6
Generate comprehensive security report

Vulnerability Detection Capabilities

Garak can detect a wide range of LLM-specific vulnerabilities, from prompt injection to model inversion attacks.

Prompt Injection
Attacks that manipulate model behavior through crafted input prompts, potentially bypassing safety measures.
Direct prompt injection
Indirect prompt injection
System prompt extraction
Context window poisoning
Multi-turn injection chains
Data Extraction
Techniques to extract sensitive information from model training data or internal system prompts.
Training data extraction
PII information leakage
Model inversion attacks
Membership inference
Gradient-based extraction
Jailbreaking
Methods to bypass model safety filters and content policies through various manipulation techniques.
Role-playing scenarios
Hypothetical questioning
Encoding-based bypasses
Language switching
Emotional manipulation
Model Manipulation
Advanced attacks targeting model behavior, reasoning, and decision-making processes.
Adversarial examples
Backdoor activation
Model poisoning effects
Reasoning manipulation
Output steering

Implementation Guide

Get started with Garak in your security testing pipeline with these step-by-step instructions.

Quick Start

1

Installation

Install Garak using pip package manager

pip install garak
2

Basic Scan

Run a basic vulnerability scan against an OpenAI model

garak --model-name "gpt-3.5-turbo" --probes promptinject
3

Custom Configuration

Create a custom configuration file for specific testing requirements

garak --config custom_config.yaml --output-dir ./results
4

Report Analysis

Review generated reports and vulnerability findings

garak --report-from ./results/garak_20241201_*.jsonl

Integration Options

CI/CD Pipeline
Automated security testing in continuous integration workflows
GitHub ActionsJenkins IntegrationAutomated ReportingFail Conditions
Security Platforms
Integration with enterprise security and compliance platforms
SIEM IntegrationVulnerability ManagementCompliance MappingRisk Scoring
Model Platforms
Support for various LLM deployment platforms and APIs
Cloud APIsLocal ModelsCustom EndpointsBatch Processing

Success Stories

Real-world implementations and results from organizations using Garak for LLM security testing.

FinTech
Financial Services Security
Large financial institution implements Garak for LLM security testing in customer service applications.
Challenge:

Ensuring customer data protection and regulatory compliance in AI-powered chat systems.

Result:

Identified and mitigated 15 critical vulnerabilities before production deployment.

85%
improvement
Healthcare
Healthcare AI Security
Medical AI company uses Garak to secure patient-facing diagnostic assistance models.
Challenge:

Preventing medical misinformation and protecting patient privacy in AI interactions.

Result:

Achieved HIPAA compliance and reduced security incidents by 90%.

90%
improvement
E-commerce
E-commerce Platform
Online marketplace integrates Garak into their AI recommendation and search systems.
Challenge:

Protecting against prompt injection attacks that could manipulate product recommendations.

Result:

Prevented potential revenue loss and maintained customer trust through proactive security.

75%
improvement

What Security Experts Say

Testimonials from security professionals and researchers using Garak in production environments.

"Garak has become an essential part of our AI security toolkit. The comprehensive vulnerability detection capabilities have helped us identify issues we never would have found manually."

SC
Sarah Chen
Senior Security Engineer
TechCorp AI

"The integration with our CI/CD pipeline was seamless. We now catch LLM vulnerabilities before they reach production, saving us significant time and resources."

MR
Michael Rodriguez
DevSecOps Lead
SecureAI Solutions

"As a security researcher, I appreciate Garak's extensibility and the quality of its vulnerability detection. It's become my go-to tool for LLM security assessments."

DEW
Dr. Emily Watson
AI Security Researcher
University Research Lab

"The detailed reporting and risk scoring features help us communicate security findings effectively to both technical and business stakeholders."

JP
James Park
CISO
Enterprise Solutions Inc

"Garak's open-source nature allows us to customize it for our specific use cases while contributing back to the community. It's a win-win situation."

LT
Lisa Thompson
Security Architect
Open Source Security

"The comprehensive coverage of OWASP Top 10 for LLMs makes Garak invaluable for compliance and security audits. Highly recommended for any organization using AI."

RK
Robert Kim
Compliance Manager
RegTech Innovations

Start Securing Your LLMs Today

Join thousands of security professionals using Garak to identify and mitigate AI security risks in production systems.