10 Best AI Security Tools (2026)
Vendor-neutral comparison of 10 AI security tools for LLMs. Covers prompt injection, jailbreaks, and data leakage testing. Includes 6 open-source options.
- We reviewed 10 AI security tools — 7 open-source, 1 freemium, and 2 commercial — split between testing/red-teaming (Garak, PyRIT, DeepTeam, Promptfoo) and runtime protection (LLM Guard, NeMo Guardrails, Lakera Guard).
- Prompt injection is the #1 vulnerability in the OWASP Top 10 for LLM Applications (2025). Microsoft research shows just 5 crafted documents can manipulate AI responses 90% of the time via RAG poisoning.
- Garak (NVIDIA) and Promptfoo are the go-to free testing tools — Garak covers the widest attack range, Promptfoo has first-class CI/CD support.
- Major acquisitions reshaped this space: Lakera Guard acquired by Check Point (September 2025), Protect AI Guardian by Palo Alto Networks (Apr 2025), and Rebuff was archived in May 2025.
What is AI Security?
As we integrate LLMs into our applications, traditional scanners are not enough.
We need specialized tools to test for hallucinations, prompt injection, jailbreaks, and data leakage.
The OWASP Top 10 for LLM Applications provides a framework for understanding these risks.
According to OWASP’s 2025 Top 10 for LLM Applications, prompt injection is the #1 critical vulnerability. Microsoft’s security research confirms that indirect prompt injection is one of the most widely-used attack techniques against AI systems. Research demonstrates that just five carefully crafted documents can manipulate AI responses 90% of the time through Retrieval-Augmented Generation (RAG) poisoning (Microsoft Research, 2024).
Prompt injection is sometimes compared to SQL injection — both exploit insufficient input handling, but the mitigations are fundamentally different because LLM behavior cannot be fully constrained with deterministic rules.
AppSec Santa reviews and compares all AI security tools in this category to help you find the right fit for your stack. The tools listed here help you proactively identify and mitigate AI-specific vulnerabilities before they reach production.
Advantages
- • Tests for novel AI-specific risks
- • Catches prompt injection and jailbreaks
- • Essential for GenAI applications
- • Most tools are free and open-source
Limitations
- • Rapidly evolving field
- • No established standards yet
- • Limited coverage of all AI risk types
- • Requires AI/ML expertise to interpret results
OWASP Top 10 for LLM Applications
These are the top risks you should test for when deploying LLM-based applications:
Prompt Injection
Malicious input that hijacks the model to perform unintended actions or reveal system prompts. The most critical and common LLM vulnerability.
Sensitive Information Disclosure
Model leaking PII, credentials, or proprietary data from training or context. LLM Guard can anonymize PII in prompts and responses.
Supply Chain Vulnerabilities
Compromised models, datasets, or plugins from third-party sources. HiddenLayer and Protect AI Guardian scan for malicious models.
Data and Model Poisoning
Malicious data introduced during training or fine-tuning that causes the model to behave incorrectly. Relevant if you fine-tune models on external data.
Improper Output Handling
LLM output used directly without validation, leading to XSS, SSRF, or code execution. Always sanitize LLM responses before rendering or executing them.
Excessive Agency
LLM-based systems granted excessive functionality, permissions, or autonomy, enabling harmful actions triggered by unexpected outputs.
System Prompt Leakage
Attackers extracting or inferring system prompts, revealing business logic, filtering criteria, or access controls embedded in the prompt.
Vector and Embedding Weaknesses
Vulnerabilities in how vector databases and embeddings are generated, stored, or retrieved, enabling data poisoning or unauthorized access in RAG systems.
Misinformation
LLMs generating false or misleading content that appears authoritative. Critical for applications where users rely on model outputs for decision-making.
Unbounded Consumption
Attacks that consume excessive resources or cause the model to hang on crafted inputs. Rate limiting and input validation help mitigate this.
Quick Comparison of AI Security Tools
| Tool | USP | Type | License |
|---|---|---|---|
| Testing / Red Teaming (Open Source) | |||
| Garak | NVIDIA's "Nmap for LLMs" | Testing | Open Source |
| PyRIT | Microsoft's AI red team framework | Testing | Open Source |
| DeepTeam | 40+ attack simulations, OWASP coverage | Testing | Open Source |
| Promptfoo | Developer CLI, CI/CD integration | Testing | Open Source |
| Runtime Protection (Open Source) | |||
| LLM Guard | PII anonymization, content moderation | Runtime | Open Source |
| NeMo Guardrails | NVIDIA's programmable guardrails | Runtime | Open Source |
| Rebuff ARCHIVED | Prompt injection detection SDK; archived May 2025 | Runtime | Open Source |
| Commercial | |||
| Lakera Guard ACQUIRED | Gandalf game creator; acquired by Check Point (September 2025) | Runtime | Commercial |
| HiddenLayer AISec | ML model security platform | Both | Commercial |
| Protect AI Guardian ACQUIRED | ML model scanning; acquired by Palo Alto Networks (Apr 2025) | Testing | Commercial |
Testing Tools vs Runtime Protection
AI security tools fall into two categories: those that test your LLM before deployment, and those that protect it at runtime.
| Aspect | Testing Tools | Runtime Protection |
|---|---|---|
| When it runs | Before deployment, in CI/CD | At runtime, on every request |
| Purpose | Find vulnerabilities proactively | Block attacks in real-time |
| Examples | Garak, PyRIT, Promptfoo, DeepTeam | Lakera Guard, LLM Guard, NeMo Guardrails |
| Performance impact | None (runs offline) | Adds latency to requests |
| Best for | Development and QA | Production applications |
My recommendation: Use both. Run testing tools like Garak, Promptfoo, or DeepTeam in CI/CD to catch issues early. Deploy runtime protection like Lakera Guard or LLM Guard for production applications that handle user input.
How to Choose an AI Security Tool
The AI security space is new, but these factors help narrow down your options:
Testing or Runtime Protection?
For vulnerability scanning before deployment, use Garak, PyRIT, Promptfoo, or DeepTeam. For runtime protection, use Lakera Guard, LLM Guard, or NeMo Guardrails.
LLM Provider Compatibility
Most tools work with any LLM via API. Garak, PyRIT, and NeMo Guardrails support local models. For ML model security scanning (not just LLMs), consider HiddenLayer or Protect AI Guardian.
Open-source vs Commercial
Six tools are fully open-source: Garak, PyRIT, DeepTeam, LLM Guard, NeMo Guardrails, and Promptfoo (core). Rebuff was archived in May 2025 and is no longer maintained. HiddenLayer is commercial for enterprise ML security. Lakera Guard and Protect AI Guardian were acquired in 2025 (by Check Point and Palo Alto Networks respectively).
CI/CD Integration
Promptfoo has first-class CI/CD support. Garak, PyRIT, and DeepTeam can run in CI with some setup. For runtime protection, LLM Guard and Lakera Guard are single API calls.
Akto
AI Agent & MCP Security Platform
Arthur AI
NEWAI Observability and Bias Detection
DeepTeam
LLM Red Teaming Framework
Garak
NEWNVIDIA's LLM Vulnerability Scanner
HiddenLayer AISec
ML Model Security Platform — 48+ CVEs, 25+ Patents
LLM Guard
Open-Source LLM Guardrails
Mindgard
NEWDAST-AI Continuous Red Teaming
NVIDIA NeMo Guardrails
NVIDIA's Programmable LLM Guardrails
Promptfoo
NEWLLM Evaluation & Red Teaming CLI
PyRIT
NEWMicrosoft's AI Red Team Framework
Show 5 deprecated/acquired tools
Frequently Asked Questions
What is AI Security?
What is prompt injection?
What is the OWASP Top 10 for LLM Applications?
Do I need AI security tools if I use OpenAI or Anthropic APIs?
Which AI security tool should I start with?
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Application Security @ Invicti
10+ years in application security. Reviews and compares 170 AppSec tools across 11 categories to help teams pick the right solution. More about me →