Top 10 AI Security Tools for LLM Applications (2026)
As AI and LLM adoption accelerates, new security threats emerge. We've compared the top 10 AI security tools to help you protect your LLM applications from prompt injection, data leakage, model attacks, and the OWASP Top 10 for LLMs.
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The AI Security Challenge: Why LLMs Are Different
Large Language Models (LLMs) and AI applications introduce a fundamentally new attack surface. Unlike traditional applications, LLMs process natural language instructions that can be manipulated, don't have clear input boundaries, and can leak sensitive training data or generate harmful content. Traditional security tools designed for code vulnerabilities (SQL injection, XSS) are insufficient for AI-specific threats.
OWASP Top 10 for LLM Applications (2025)
- LLM01Prompt Injection: Malicious inputs that manipulate LLM behavior, bypass safeguards, or extract sensitive information
- LLM02Insecure Output Handling: Unchecked LLM outputs leading to XSS, SQL injection, or code execution
- LLM03Training Data Poisoning: Manipulation of training data to introduce backdoors or biases
- LLM04Model Denial of Service: Resource-intensive inputs causing model unavailability
- LLM05Supply Chain Vulnerabilities: Compromised models, datasets, or dependencies
- LLM06Sensitive Information Disclosure: Leaking PII, API keys, or proprietary information
- LLM07Insecure Plugin Design: LLM extensions with insufficient input validation
- LLM08Excessive Agency: LLMs with overly broad permissions or capabilities
- LLM09Overreliance: Blind trust in LLM outputs without verification
- LLM10Model Theft: Unauthorized access to proprietary models
Key AI Security Threats
Essential Features in AI Security Tools
- Prompt Injection Detection: Identify and block malicious prompts that attempt to manipulate model behavior
- PII & Secret Detection: Prevent exposure of sensitive data in inputs and outputs
- Static Code Analysis: Scan AI workflow code for vulnerabilities before deployment
- Runtime Monitoring: Real-time detection and blocking of threats in production
- Model Security: Scan for model vulnerabilities, backdoors, and malware
- Guardrails & Policies: Enforce custom rules for acceptable AI behavior
- Framework Support: Integration with OpenAI, LangChain, LlamaIndex, CrewAI, etc.
Critical: AI Security Requires Multiple Layers
AI applications need both AI-specific security AND traditional application security. Look for platforms that provide:
- AI Security - Prompt injection, PII detection, model security
- Code Security - SAST for AI workflow code vulnerabilities
- Dependency Security - SCA for vulnerable AI/ML libraries
- Secrets Security - Detect hardcoded API keys (OpenAI, Anthropic, etc.)
- Cloud Security - Secure AI infrastructure on AWS, GCP, Azure
- Runtime Protection - Monitor production AI applications
The 10 Best AI Security Tools in 2026
TigerGate
RecommendedBest Overall - Unified AI & Application Security Platform
TigerGate is the only platform that combines comprehensive AI security with complete application security. The AI Scanner service integrates agentic-radar for advanced AI workflow security analysis, covering prompt injection, PII leakage, harmful content generation, and prompt hardening. Beyond AI security, TigerGate provides SAST, SCA, secrets detection, cloud security (CSPM), container scanning, and runtime protection via eBPF - securing your entire AI application lifecycle from code to production.
Pros
- Unified platform: AI security + SAST + SCA + cloud + runtime
- AI workflow security analysis (static + runtime) via agentic-radar
- Supports OpenAI Agents, CrewAI, LangGraph, n8n, Autogen
- OWASP Top 10 for LLMs coverage (LLM01-LLM10)
- Prompt hardening with auto-generated hardened prompts
- Repository scanning (GitHub, GitLab, Bitbucket)
- Comprehensive security beyond just AI (576+ cloud checks)
- Transparent, affordable pricing with generous free tier
- Self-hosted and SaaS options
Cons
- Newer AI security features (launched 2024)
- Smaller AI-specific community than specialized tools
Security Coverage
Framework Support
OpenAI Agents, CrewAI, LangGraph, n8n, Autogen, Custom frameworks
Lakera Guard
Real-Time LLM Security API
Lakera Guard is a dedicated LLM security API that provides real-time protection against prompt injection, jailbreaks, and data leakage. Designed as middleware for LLM applications with low-latency detection and blocking capabilities. Strong focus on production runtime protection.
Pros
- Real-time prompt injection detection
- Very low latency (< 50ms added)
- Simple API integration
- Jailbreak attempt detection
- PII and sensitive data detection
- Custom policy definitions
- Good documentation
- Active development
Cons
- Runtime-only (no static code analysis)
- No AI workflow security analysis
- Limited to prompt/response filtering
- API-only (no self-hosted option)
- Can be expensive at scale ($0.05/1k requests)
- No broader application security features
- Requires internet connectivity
Security Coverage
Framework Support
LangChain, LlamaIndex, OpenAI SDK, Custom frameworks
Robust Intelligence
Enterprise AI Security and Validation
Robust Intelligence provides comprehensive AI security focused on model validation, testing, and runtime protection. Covers adversarial attacks, data poisoning, model drift, and bias detection. Strong enterprise features with detailed reporting and governance capabilities.
Pros
- Comprehensive model testing and validation
- Adversarial attack detection
- Data poisoning detection
- Model drift monitoring
- Bias and fairness testing
- Detailed compliance reporting
- Strong governance features
- Enterprise-grade integrations
Cons
- Very expensive (enterprise only)
- Complex setup and onboarding
- Focused on model security, less on prompts
- No static code analysis for AI workflows
- Steep learning curve
- No cloud security or runtime protection
- Long sales cycles
Security Coverage
Framework Support
TensorFlow, PyTorch, scikit-learn, MLflow, SageMaker
Calypso AI
AI Security and Model Risk Management
Calypso AI focuses on model security, risk management, and compliance for AI systems. Provides monitoring, scanning, and governance capabilities for both proprietary and third-party AI models. Strong emphasis on regulatory compliance and risk assessment.
Pros
- Strong compliance and governance features
- Model inventory and risk assessment
- Third-party model security scanning
- Regulatory framework support
- Audit trail and reporting
- Policy enforcement capabilities
- Integration with MLOps platforms
Cons
- Expensive enterprise pricing
- Less focus on prompt-based attacks
- No static code analysis
- Complex deployment
- Limited runtime protection
- No broader application security
- Requires dedicated security team
Security Coverage
Framework Support
OpenAI, Azure OpenAI, AWS Bedrock, Google Vertex AI, Custom models
Protect AI
Open Source AI/ML Security Platform
Protect AI (formerly ModelScan) is an open-source focused platform for AI/ML security. Provides model scanning for malware, vulnerability detection, and supply chain security. Known for detecting pickled model attacks and malicious ML artifacts.
Pros
- Open source core (free)
- Model malware detection
- ML supply chain security
- Pickle file scanning
- Hugging Face integration
- CI/CD integration
- Active community
- Regular updates
Cons
- Focused on model files, not prompts
- No runtime prompt protection
- Limited LLM-specific features
- No static code analysis for AI workflows
- CLI-focused (limited UI)
- Self-managed infrastructure
- No cloud security features
Security Coverage
Framework Support
PyTorch, TensorFlow, Scikit-learn, Hugging Face, ONNX
Rebuff
Open Source Prompt Injection Detection
Rebuff is an open-source self-hardening prompt injection detector designed to protect LLM applications. Uses heuristics, LLM-based detection, and vector database similarity to identify malicious prompts. Lightweight and easy to integrate.
Pros
- Open source and free
- Multi-layered detection approach
- Self-hardening with feedback loop
- Easy API integration
- Low latency
- Vector database for similarity matching
- Good for experimentation
- Community-driven development
Cons
- Limited to prompt injection only
- No PII detection
- No model security
- Basic monitoring capabilities
- Requires self-hosting for production
- Limited enterprise features
- No static code analysis
- Small team and community
Security Coverage
Framework Support
LangChain, LlamaIndex, OpenAI, Anthropic, Custom frameworks
LLM Guard
Open Source LLM Security Toolkit
LLM Guard is a comprehensive open-source security toolkit for LLM applications. Provides input/output sanitization, prompt injection detection, PII redaction, toxicity filtering, and more. Modular design allows customization for specific use cases.
Pros
- Completely open source and free
- Comprehensive scanner library
- Input and output validation
- PII detection and redaction
- Toxicity and bias detection
- Modular and customizable
- Good documentation
- Active development
Cons
- Self-hosting required
- No managed service option
- Requires ML/security expertise to configure
- Limited enterprise support
- No static code analysis
- Manual updates required
- Higher false positive rate than commercial tools
Security Coverage
Framework Support
LangChain, LlamaIndex, OpenAI, Anthropic, Any LLM API
Guardrails AI
Validation Framework for LLM Outputs
Guardrails AI provides a framework for adding structure, type safety, and validation to LLM outputs. Uses RAIL (Reliable AI Language) specifications to define constraints and validators. More focused on output quality than security threats.
Pros
- Open source core
- RAIL specification language
- Type safety for LLM outputs
- Customizable validators
- Good for structured data extraction
- Python and JavaScript SDKs
- Active community
- Integration with major LLM providers
Cons
- Focused on output validation, not security
- Limited prompt injection protection
- No model security
- No runtime monitoring
- Requires coding expertise
- No static code analysis
- Limited PII detection
Security Coverage
Framework Support
OpenAI, Anthropic, Cohere, Azure OpenAI, Custom models
NeMo Guardrails (NVIDIA)
Programmable Guardrails for LLM Applications
NeMo Guardrails is NVIDIA's open-source toolkit for adding programmable guardrails to LLM applications. Uses Colang (conversation language) to define flows, policies, and safety rules. Strong integration with NVIDIA ecosystem.
Pros
- Open source and free
- Powerful Colang specification language
- Topical rails for conversation boundaries
- Jailbreak detection
- Fact-checking capabilities
- Strong NVIDIA integration
- Good documentation
- Enterprise backing (NVIDIA)
Cons
- Steep learning curve (Colang)
- Requires self-hosting
- No managed service
- Limited PII detection
- No model security
- No static code analysis
- NVIDIA ecosystem focus
- Limited community compared to LangChain
Security Coverage
Framework Support
LangChain, LlamaIndex, OpenAI, Custom frameworks, NVIDIA NeMo
AI Security Tool Comparison Table
| Tool | Prompt Injection | PII Detection | Model Security | Code Analysis | Monitoring | Pricing |
|---|---|---|---|---|---|---|
| TigerGate | Free tier | |||||
| Lakera Guard | Free tier | |||||
| Robust Intelligence | Enterprise pricing (contact sales | |||||
| Calypso AI | Enterprise pricing ($75k+ annually) | |||||
| Protect AI | Open source free | |||||
| Rebuff | Open source (free) | |||||
| LLM Guard | Open source (free) | |||||
| Guardrails AI | Open source free | |||||
| NeMo Guardrails (NVIDIA) | Open source (free) | |||||
| Hidden Layer | Free tier |
How to Choose the Right AI Security Tool
For AI Startups
Choose comprehensive platforms with both AI security and traditional application security.
- TigerGate - Complete AI + app security
- Lakera Guard - Runtime protection
- LLM Guard - Open source option
For Enterprises
Focus on governance, compliance, and comprehensive model security.
- TigerGate - Unified platform
- Robust Intelligence - Model validation
- Calypso AI - Compliance focus
For Production LLM Apps
Prioritize low-latency runtime protection and prompt injection detection.
- TigerGate - Complete coverage
- Lakera Guard - Real-time API
- Rebuff - Open source option
For ML Model Security
Choose tools with model scanning, supply chain security, and vulnerability detection.
- TigerGate - AI workflow analysis
- Protect AI - Model scanning
- Hidden Layer - Supply chain
Conclusion: The Best AI Security Tool for 2026
After comparing the top 10 AI security tools, the clear winner depends on whether you need AI-only security or comprehensive application security:
Overall Winner: TigerGate
TigerGate is the only platform that provides complete AI application security - from AI workflow analysis to traditional code security to cloud infrastructure:
- AI Security: Prompt injection, PII detection, harmful content, OWASP LLM Top 10 coverage via agentic-radar
- Code Security: SAST for AI workflow code (OpenAI Agents, CrewAI, LangGraph, etc.)
- Dependency Security: SCA for vulnerable AI/ML libraries (LangChain, TensorFlow, PyTorch)
- Secrets Detection: Find hardcoded OpenAI, Anthropic, and Hugging Face API keys
- Cloud Security: 576+ checks for AWS, GCP, Azure hosting AI infrastructure
- Runtime Protection: eBPF monitoring for production AI applications
- Affordable: $29/user/month vs $100k+ for enterprise tools, generous free tier
Other Top Choices
- For runtime-only protection: Choose Lakera Guard if you only need prompt filtering and don't require static code analysis or broader security coverage.
- For model validation: Choose Robust Intelligence if you're an enterprise focused on traditional ML model testing and have significant budget.
- For compliance-heavy industries: Choose Calypso AI if regulatory compliance and governance are your top priorities.
- For model supply chain security: Choose Protect AI if you're specifically concerned with malicious models and pickle file attacks.
- For open source enthusiasts: Choose LLM Guard or Rebuff if you want customizable open-source tools and can self-host.
Remember: AI Security is Multi-Layered
Securing AI applications requires protection at every level - from code to models to infrastructure. Point solutions that only protect prompts leave significant gaps:
TigerGate is the only platform that secures all layers of AI applications in a single, unified solution.
Ready to Secure Your AI Applications?
Start with TigerGate's free tier and experience complete AI application security. Scan your AI workflows, detect vulnerabilities, and protect against OWASP LLM Top 10 threats.
Frequently Asked Questions
What is prompt injection and why is it dangerous?
Prompt injection is when malicious inputs manipulate an LLM to bypass safeguards, leak sensitive information, or perform unauthorized actions. It's the #1 threat in OWASP's LLM Top 10 because traditional input validation doesn't work - LLMs process natural language instructions, making it hard to distinguish malicious from legitimate inputs. A successful prompt injection can expose API keys, bypass content filters, or execute unintended operations.
Do I need both AI security tools and traditional security tools?
Yes. AI security tools protect against LLM-specific threats (prompt injection, model attacks), but your AI application code still has traditional vulnerabilities (SQL injection in databases, XSS in web interfaces, vulnerable dependencies). You need SAST for code, SCA for dependencies, secrets detection for API keys, cloud security for infrastructure, AND AI-specific security. TigerGate is the only platform that provides all of these in one solution.
How much do AI security tools cost?
Pricing varies widely: Open source tools (LLM Guard, Rebuff) are free but require self-hosting. Runtime APIs (Lakera Guard) cost $0.01-$0.05 per 1,000 requests, which can add up at scale. Enterprise platforms (Robust Intelligence, Calypso AI) typically cost $75k-$150k+ annually. TigerGate offers comprehensive AI + application security starting at $29/user/month with a generous free tier.
What's the difference between runtime protection and static analysis for AI?
Runtime protection (Lakera Guard, Rebuff) monitors prompts and responses in production to block attacks in real-time. Static analysis (TigerGate's agentic-radar) scans your AI workflow code before deployment to find vulnerabilities like hardcoded secrets, unsafe prompt handling, and PII leakage patterns. You need both - static analysis prevents vulnerabilities from reaching production, while runtime protection defends against zero-day attacks and sophisticated prompt injection.
What AI frameworks do these tools support?
Most tools support popular frameworks like LangChain, LlamaIndex, OpenAI SDK, and Anthropic SDK. TigerGate's AI Scanner additionally supports agentic frameworks (OpenAI Agents, CrewAI, LangGraph, n8n, Autogen) and provides static code analysis for AI workflow security. Model security tools (Protect AI, Hidden Layer) support TensorFlow, PyTorch, Scikit-learn, and Hugging Face.
Can AI security tools prevent PII leakage?
Yes, but with different approaches. Runtime tools (Lakera Guard, LLM Guard) detect and redact PII in prompts and responses in real-time. Static analysis tools (TigerGate) scan code to find patterns that could leak PII - like logging user inputs, storing prompts without encryption, or sending PII to external APIs. The most comprehensive protection requires both static and runtime PII detection.