Best LLM Security Tools of 2024: Safeguarding Your Large Language Models

As large language models (LLMs) continue to push the boundaries of natural language processing, their widespread adoption across various industries has highlighted the critical need for robust security measures. These powerful AI systems, while immensely beneficial, are not immune to potential risks and vulnerabilities. In 2024, the landscape of LLM security tools has evolved to address the unique challenges posed by these advanced models, ensuring their safe and responsible deployment.

Here, we explore the top 10 LLM security tools of 2024, designed to safeguard your LLM applications from potential threats. 

Top 10 LLM Security Tools of 2024

1. WhyLabs LLM Security

WhyLabs LLM Security is a comprehensive solution that protects large language models from various security threats. This tool employs a multi-layered approach to safeguard LLM applications against malicious prompts while ensuring safe response handling.

WhyLabs LLM Security offers robust protection against data leakage. It can detect targeted attacks aimed at leaking confidential data, including evaluating prompts for these attacks and blocking responses containing personally identifiable information (PII). This feature is essential for production LLMs, where data privacy and security are paramount.

Also, WhyLabs LLM Security incorporates prompt injection monitoring, which is vital in maintaining a consistent and safe user experience. By monitoring for malicious prompts designed to confuse the system into providing harmful outputs, this tool helps mitigate potential risks associated with prompt injection attacks.

Misinformation prevention is another crucial aspect addressed by WhyLabs LLM Security. The platform helps identify and manage content generated by LLMs that might be misinformation or inappropriate due to "hallucinations."

2. Lasso Security

Lasso Security presents a comprehensive end-to-end solution explicitly designed for large language models. Their flagship offering, LLM Guardian, addresses the unique challenges and threats LLMs pose in the rapidly evolving cybersecurity landscape, tailored to meet the specific security needs of LLM applications.

Lasso Security provides robust security assessments. The company comprehensively evaluates LLM applications to identify potential vulnerabilities and security risks. These assessments enable organizations to understand their security posture and the challenges they may face when deploying LLMs, allowing them to take proactive measures.

Lasso Security's LLM Guardian also offers advanced threat modeling capabilities, empowering organizations to anticipate and prepare for potential cyber threats targeting their LLM applications.

3. CalypsoAI Moderator

CalypsoAI Moderator is a comprehensive security solution designed to address various challenges associated with deploying Large Language Models in enterprises. This tool's key features cater to a wide range of security needs, making it a robust choice for organizations looking to safeguard their LLM applications.

A standout feature of CalypsoAI Moderator is its data loss prevention capabilities. It screens for sensitive data like code and intellectual property, ensuring that such information is blocked before leaving the organization. This feature is crucial in preventing the unauthorized sharing of proprietary information.

Additionally, CalypsoAI Moderator provides full auditability, offering a comprehensive record of all interactions, including prompt content, sender details, and timestamps. Malicious code detection is another critical aspect addressed by this solution. CalypsoAI Moderator can identify and block malware, thus safeguarding the organization's ecosystem from potential infiltrations via LLM responses.

4. Protecto / GPTGuard

Protecto and GPTGuard go hand in hand in providing businesses with a safe, secure way of using AI tools without exposing personal or sensitive information. These tools can go a long way in lessening compliance worries and mitigating key security and privacy pain points while allowing organizations to make the most productive and effective use of AI tools.

Protecto safeguards data across the entire AI lifecycle, using cutting-edge techniques to obfuscate data while preserving context, thereby making sure that model accuracy remains unaffected. This is a marked improvement on traditional masking tools that can often distort the meaning and context of data, making security efforts counterproductive in terms of efficacy. It can help enforce role-based access in RAG workflows and offers detailed monitoring, analysis, and reporting capabilities while also helping with compliance.

GPTGuard can be a worthy complimentary service by allowing organizations to use conversation AI tools like ChatGPT without compromising sensitive data. This is accomplished using intelligent tokenization, which detects the specific parts of prompts that contain sensitive data and transforms them.

5. BurpGPT

BurpGPT is a Burp Suite extension designed to enhance web security testing by integrating OpenAI's LLMs. It provides advanced vulnerability scanning and traffic-based analysis capabilities, making it a robust tool for beginners and seasoned security testers.

BurpGPT has a passive scan check capability, which allows users to submit HTTP data to an OpenAI-controlled GPT model for analysis, helping detect vulnerabilities and issues that traditional scanners might miss in scanned applications.

BurpGPT offers granular control, allowing users to choose from multiple OpenAI models and control the number of GPT tokens used in the analysis. Seamless integration with Burp Suite is another significant advantage of BurpGPT.

6. Rebuff

Rebuff is a self-hardening prompt injection detector specifically designed to protect AI applications from prompt injection (PI) attacks. It employs a multi-layered defense mechanism to enhance the security of LLM applications, providing a robust line of defense against this growing threat.

One of the critical features of Rebuff is its multi-layered defense approach. The tool incorporates four layers of defense to provide comprehensive protection against PI attacks, ensuring that multiple safeguards are in place to mitigate potential vulnerabilities.

Rebuff employs a dedicated LLM to analyze incoming prompts and identify potential attacks. This LLM-based approach allows for more nuanced and context-aware threat detection, enhancing the tool's overall accuracy and effectiveness. Rebuff also leverages a vector database to store embeddings of previous attacks.

7. Garak

Garak is an exhaustive LLM vulnerability scanner designed to find security holes in technologies, systems, apps, and services that use language models. It's a versatile tool capable of simulating attacks and probing for vulnerabilities in various potential failure modes, making it an invaluable asset for organizations seeking to bolster their LLM security posture.

Garak can autonomously run a range of probes over a model, managing tasks like finding appropriate detectors and handling rate limiting. This automated approach allows for a full standard scan and report without manual intervention, streamlining the vulnerability assessment process.

Garak supports numerous LLMs, including OpenAI, Hugging Face, Cohere, Replicate, and custom Python integrations. Moreover, Garak possesses a self-adapting capability, allowing it to evolve and improve over time.

8. LLMFuzzer

LLMFuzzer is an open-source fuzzing framework designed explicitly for large language models, with a primary focus on their integration into applications via LLM APIs. This tool is handy for security enthusiasts, pen-testers, or cybersecurity researchers keen on exploring and exploiting vulnerabilities in AI systems.

LLMFuzzer has robust fuzzing capabilities that are explicitly tailored for LLMs. It is built to test LLMs for vulnerabilities rigorously, ensuring these powerful models are thoroughly evaluated for potential security risks.

LLMFuzzer can test LLM integrations in various applications, providing a comprehensive assessment of the security posture of LLM deployments across different software environments. To identify vulnerabilities effectively, LLMFuzzer employs a wide range of fuzzing strategies. Its modular architecture allows for easy extension and customization according to specific testing needs.

9. LLM Guard

LLM Guard, developed by Laiyer.ai, is a comprehensive tool designed to enhance the security of Large Language Models. LLM Guard has the ability to identify and manage harmful language in LLM interactions. By sanitizing and detecting harmful content, the tool ensures that the output generated by LLMs remains appropriate and safe, mitigating potential risks associated with inappropriate or offensive language.

Data leakage prevention is another crucial aspect addressed by LLM Guard. The tool is adept at preventing the leakage of sensitive information during LLM interactions, a vital component of maintaining data privacy and security. Furthermore, LLM Guard offers robust protection against prompt injection attacks, a growing concern in the LLM security landscape. 

10. Vigil

Vigil is a Python library and REST API designed explicitly to assess Large Language Model prompts and responses. Its primary function is to detect prompt injections, jailbreaks, and other potential risks associated with LLM interactions.

A key strength of Vigil is its ability to analyze LLM prompts for prompt injections and risky inputs, a crucial aspect of maintaining the integrity of LLM interactions. By identifying potential threats early on, Vigil helps mitigate the risks associated with prompt injection attacks, ensuring that LLM outputs remain reliable and trustworthy.

Vigil's modular design makes its scanners easily extensible, allowing for adaptation to evolving security needs and threats. Vigil employs various methods for prompt analysis to detect potential risks, including vector database/text similarity, YARA/heuristics, transformer model analysis, prompt-response similarity, and Canary Tokens.

Final Thoughts

Sticking to industry standards and best practices is essential when implementing LLM security solutions. Organizations should consider tools that align with relevant frameworks or guidelines, such as the OWASP Top 10 for LLM Applications. The top 10 LLM security tools in 2024 discussed in this article represent the cutting edge of LLM security solutions, offering robust protection against a wide range of threats and vulnerabilities.