Top 12 LLM Security Tools: Paid & Free (Overview)

15 min read
April 20, 2026

Large Language Models (LLMs) such as OpenAI's GPT-3 and GPT-4 have revolutionized the way we interact with technology, from automated customer service to content creation.

Yet, their widespread adoption surfaces complex cybersecurity challenges that cannot be overlooked.

To maintain the integrity and reliability of systems that leverage LLMs, it's crucial to address risks such as unauthorized access and model exploitation.

In this article, we’ll be looking at 12 security tools currently in use to address vulnerabilities in LLMs, reflecting the ongoing commitment within the tech community to enhance the security measures surrounding these powerful AI models.

Here are the tools we cover:

• Lakera Guard
• WhyLabs LLM Security
• Lasso Security
• CalypsoAI Moderator
• BurpGPT
• Rebuff
• Garak
• LLMFuzzer
• LLM Guard
• Vigil
• G-3PO
• EscalateGPT

The Enterprise Playbook for Agentic AI Security

AI systems now retrieve data, invoke tools, and act across enterprise workflows. Get the playbook to learn how to secure AI across employees, applications, and agents.

Inside the Playbook:

• Why traditional security models fall short
• The three new AI exposure surfaces
• How to secure the execution layer
• What a unified AI Defense Plane looks like in practice

Lakera Guard

Lakera Guard is a developer-first AI security tool designed to protect Large Language Models (LLMs) applications across enterprises. It focuses on mitigating risks such as prompt injections, data loss, insecure output handling, and others. Lakera Guard's API seamlessly integrates with existing applications and workflows, it is completely model-agnostic, and enables developers to secure their LLM applications instantly.

Key features:

• Prompt Injection Protection: Lakera Guard offers practical defenses against direct and indirect prompt injection attacks that could lead to unintended downstream actions.
• Leakage of Sensitive Information: The tool helps mitigate risks when LLMs are connected to personally identifiable information (PII) or corporate data that should remain confidential.
• Detection of Hallucinations: It can identify outputs from models misaligned with the input context or expected behavior.

Lakera Guard is known for its ease of integration, requiring just a single line of code, and offers industry-leading response times, typically assessing prompts in less than 50ms. This makes it a user-friendly option for developers looking to secure their LLM applications without significant overhead or complexity.

WhyLabs LLM Security

WhyLabs LLM Security offers robust protection for LLMs against various security threats. It's designed to safeguard LLM applications against malicious prompts while ensuring safe response handling, which is crucial for maintaining the integrity of production LLMs.

Key features:

• Protection Against Data Leakage: It can detect targeted attacks aimed at leaking confidential data.
• Prompt Injection Monitoring: WhyLabs monitors for malicious prompts designed to confuse the system into providing harmful outputs.
• Misinformation Prevention: The platform helps identify and manage content generated by LLMs that might be misinformation or inappropriate due to "hallucinations."
• OWASP Top 10 for LLM Applications: Telemetry captures vulnerabilities unique to LLMs.

Lasso Security

Lasso Security presents an end-to-end solution explicitly designed for LLMs, addressing the unique challenges and threats LLMs pose in a rapidly evolving cybersecurity landscape.

Key features:

• Security Assessments: Lasso Security conducts comprehensive evaluations of LLM applications to identify vulnerabilities.
• Threat Modeling: The tool offers advanced capabilities to prepare for potential cyber threats.
• Specialized Training Programs: Provides specialized training programs to enhance teams' cybersecurity knowledge.

CalypsoAI Moderator

CalypsoAI Moderator is designed to address various security challenges associated with deploying LLMs in enterprises.

Key features:

• Data Loss Prevention: Blocks sensitive data before leaving the organization.
• Full Auditability: Provides comprehensive records of all interactions, enhancing transparency.
• Malicious Code Detection: Identifies and blocks malware.
• Easy-To-Use Interface: Can be easily integrated into existing workflows.

BurpGPT

BurpGPT is a Burp Suite extension designed to enhance web security testing by integrating OpenAI's LLMs.

Key Features:

• Passive Scan Check: Detect vulnerabilities traditional scanners might miss.
• Granular Control: Multiple OpenAI models to choose from.
• Integration with Burp Suite: Displays analysis results within the Burp UI.

Rebuff

Rebuff is a self-hardening prompt injection detector specifically designed to protect AI applications from prompt injection attacks.

Key Features:

• Multi-Layered Defense: Provides comprehensive protection against prompt injection attacks.
• LLM-Based Detection: Uses a dedicated LLM to analyze incoming prompts.
• VectorDB: Stores embeddings of previous attacks to recognize and prevent similar attacks.

Garak

Garak is an exhaustive LLM vulnerability scanner designed to find security holes in models.

Key Features:

• Automated Scanning: Runs a range of probes to find vulnerabilities.
• Connectivity with Various LLMs: Supports numerous LLMs.
• Self-Adapting Capability: Adapts over time based on found LLM failures.

LLMFuzzer

LLMFuzzer is an open-source fuzzing framework for Large Language Models.

Key Features:

• Robust Fuzzing for LLMs: Built to rigorously test LLMs for vulnerabilities.
• LLM API Integration Testing: Can test LLM integrations in various applications.
• Wide Range of Fuzzing Strategies: Employs diverse strategies to identify vulnerabilities.

LLM Guard

LLM Guard is a comprehensive tool designed to enhance the security of LLMs.

Key Features:

• Sanitization and Detection of Harmful Language: Identifies and manages harmful language in LLM interactions.
• Prevention of Data Leakage: Prevents leakage of sensitive information during interactions.
• Resistance Against Prompt Injection Attacks: Offers robust protection against prompt injection attacks.

Vigil

Vigil is a Python library and REST API designed to assess LLM prompts and responses.

Key Features:

• Prompt Analysis: Analyzes prompts for prompt injections and risky inputs.
• Modular Scanners: Easily extensible design for evolving security needs.
• Diverse Detection Methods: Employs various methods for prompt analysis.

G-3PO

G-3PO is a protocol droid for Ghidra for analyzing and annotating decompiled code, leveraging the power of LLMs for security analytics.

Key Features:

• Automated Analysis: Generates comments and insights on decompiled code.
• Vulnerability Identification: Helps identify potential security vulnerabilities in code.
• Code Annotation and Documentation: Suggests meaningful names for functions and variables.

EscalateGPT

EscalateGPT is an AI-powered Python tool identifying privilege escalation opportunities in AWS IAM configurations.

Key features:

• IAM Policy Retrieval and Analysis: Analyzes IAM policies for potential privilege escalation opportunities.
• Detailed Results in JSON Format: Returns results in JSON format, detailing potential risks and mitigations.
• Performance with Different OpenAI Models: Identifies complex privilege escalation scenarios effectively.

Overview of Risks and Effectiveness of Tools

The tools mentioned above are designed to address various risks associated with Large Language Models (LLMs). These risks continually evolve, but several frameworks like OWASP and ATLAS/MITRE help systematize and categorize these risks.

Identifying the Main Risks Associated with LLMs

• Prompt Injection: Unauthorized injection of malicious prompts into LLMs.
• Insecure Output Handling: Unvalidated outputs can lead to security exploits.
• Training Data Poisoning: Tampering with training data affects responses and compromises security.
• Model Denial of Service (MDoS): Overloading LLMs can disrupt operations.
• Supply Chain Vulnerabilities: Dependence on compromised components can lead to data breaches.
• Data Leakage and Misinformation: Risk of exposing sensitive data and spreading misinformation.

The OWASP Top 10 for Large Language Model Applications provides a comprehensive list of vulnerabilities.

Key Takeaways

Exploring the realm of cybersecurity for Large Language Models (LLMs), several specialized tools have emerged, each designed to fortify these AI systems against various risks. These tools cater to a variety of security concerns, from data breaches and unauthorized prompt manipulations to the unintended generation of harmful content. With the continuous evolution of threats in the LLM space, security solutions must be both flexible and forward-looking.