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October 3, 2024

What are the Top Security Risks of Using Large Language Models (LLMs)?

Discover the hidden dangers of Large Language Models (LLMs). Learn how LLMs can expose sensitive information, and how to protect your organisation from these risks.

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Key points

  • Large Language Models (LLMs) are advanced tools for natural language processing with diverse applications in organisations.
  • The use of LLMs introduces risks such as sensitive information disclosure, requiring careful management.
  • Implementing effective security strategies and best practices is essential to mitigate these risks.
  • Metomic helps you to secure LLM usage by managing sensitive data, automating access controls, and monitoring data transfers within SaaS tools.

With the increasing use of Generative AI like ChatGPT and Gemini, what are the key security risks organisations need to be aware of?

Large Language Models (LLMs) like ChatGPT and Google’s Gemini are becoming a big part of how businesses operate, transforming everything from customer service to content creation.

While these models are incredibly powerful, they also come with some serious security concerns. It’s crucial to understand and manage these risks to keep your sensitive information safe.

In this article, we'll break down what you need to know about LLM security, share practical tips, and help you navigate the best practices for protecting your organisation’s data in this new era of AI.

What are LLMs?

Large Language Models (LLMs) are a type of artificial intelligence that can read, understand, and generate text that sounds just like a human wrote it.

They work by using a technology called deep learning, which helps them learn from massive amounts of text data. This allows them to chat with users, draft content, and assist with a variety of tasks in a way that feels natural and intuitive.

It’s also becoming increasingly popular. At the moment, 58% of organisations are making use of LLMs - however, it's worth noting that 44% of these are still experimenting with how best to use them.

Clearly, there’s a growing curiosity about what LLMs can do, even as businesses figure out their full potential.

How can they be used in organisations?

Large Language Models (LLMs) are proving to be game-changers for organisations in several ways.

They can handle customer queries efficiently, help marketing departments plan and create engaging content, and it can even draft detailed reports with minimal human input.

By incorporating LLMs, businesses can streamline their operations, cut down on repetitive tasks, and spark new ideas and innovations.

Take the medical field, for example. A recent study shared on ArXiv shows that modern medical LLMs, when trained with the latest data, can support clinical decisions by incorporating cutting-edge research.

This means faster, more accurate support for healthcare professionals, and a big step forward in how organisations use data to make informed decisions.

What are the security risks of using LLMs?

When using Large Language Models (LLMs), one major security concern is the risk of accidentally exposing sensitive information.

These models learn from huge amounts of data, and sometimes they might unintentionally reveal confidential details from their training or user inputs.

A recent study from the University of North Carolina found that even after attempts to remove sensitive data, models like ChatGPT and Gemini can still output this information.

This means that, despite our best efforts to clean up data, it can linger and potentially be exposed.

For organisations, this could lead to accidental leaks of private or sensitive information, which might damage their reputation, lead to legal issues, or breach privacy regulations.

Keeping on top of these risks is key to making sure LLMs are used safely and responsibly.

What are the vulnerabilities?

Large Language Models (LLMs) have a few key vulnerabilities that can be exploited if we're not careful.

One such issue is data poisoning, where attackers deliberately insert misleading or harmful data into the model’s training set. This can skew the model’s responses and undermine its reliability.

Another vulnerability is prompt injection, where malicious inputs are crafted to trick the LLM into producing unintended or harmful outputs.

For example, an attacker might insert misleading prompts that cause the model to generate biased or incorrect information.

These vulnerabilities can seriously compromise the integrity of an LLM, making it unreliable or even dangerous to use.

Why is it important to secure LLMs?

When LLMs process confidential information, any security breach can lead to severe consequences. Imagine the impact of sensitive customer data or proprietary business information being exposed.

Not only could this result in significant data loss, but it could also damage your organisation's finances and reputation. IBM’s 2024 “Cost of a Data Breach,” report highlighted that the global average cost of a data breach has risen by 10%, now standing at $4.88 million.

Beyond the financial costs, there's the trust factor—customers and clients need to know their data is safe with you. Losing that trust can really harm your company with 66% of consumers stating they would not trust a company following a data breach.

Ensuring your LLMs are secure helps protect against these potential breaches, maintaining both your data integrity and your organisation's reputation.

How can organisations create an LLM security strategy?

Creating a comprehensive security strategy for LLMs is essential for safeguarding your data and maintaining operational integrity.

Here are some key steps to developing a comprehensive LLM security strategy:

1. Data Encryption

Encrypting data both at rest and in transit ensures that sensitive information remains protected from unauthorised access.

2. Model Integrity Checks

Regularly perform integrity checks on your LLMs to detect any tampering or anomalies.

3. Secure Infrastructure

Implement secure infrastructure protocols, including firewalls, intrusion detection systems, and multi-factor authentication.

4. Access Controls

Limit access to your LLMs to authorised personnel only. Use Role-Based Access Controls (RBAC) to manage who can view or alter the models and their data.

5. Regular Audits and Monitoring

Conduct regular security audits and continuous monitoring to identify and address vulnerabilities promptly.

6. Training and Awareness

Educate your team about the importance of LLM security and best practices. An informed team is better equipped to handle and prevent security issues.

Best practices for securing LLMs

Securing LLMs requires a combination of best practices to ensure data integrity and privacy. Here are some essential strategies to consider:

1. Adversarial Training

Train your LLMs using adversarial examples to make them more resilient against attacks.

2. Input Validation

Implement strict input validation protocols to prevent harmful data from compromising your models. Ensuring that inputs are clean and safe reduces the risk of data poisoning.

3. Access Controls

Use RBAC to manage who can interact with your LLMs.

4. Secure Execution Environments

Run your LLMs in secure, isolated environments to prevent unauthorised access and tampering.

5. Federated Learning

Implement federated learning to train LLMs across multiple devices or locations without centralising data. This method enhances privacy by keeping sensitive data local while still allowing the model to learn from a diverse dataset.

6. Differential Privacy

Integrate differential privacy techniques to add noise to the data, making it difficult to identify individual data points. This protects user privacy while still allowing useful insights to be drawn from the data.

How Metomic can help

Metomic provides a comprehensive suite of tools designed to enhance the security of LLMs and protect sensitive data.

  • Data discovery and classification: Metomic can help you identify and protect sensitive information across various SaaS applications.
  • Automated access controls: Our platform ensures only authorised users can access critical data, reducing the risk of data breaches.
  • Data retention policies: With Metomic, you can automatically manage and enforce data retention policies to protect sensitive information.
  • Monitoring and alerts: You can monitor data transfers and receive alerts when sensitive information is shared outside the corporate domain.
  • Visibility and management: You’re able to gain clear visibility into where sensitive data resides within your organisation to effectively manage LLM security.

Getting started with Metomic

Free risk assessment scans

Metomic’s free risk assessment scans can identify potential vulnerabilities and areas for improvement, and are available for Google Drive, Slack, ChatGPT and more.

These scans provide valuable insights into the security posture of your large language models, helping you understand where enhancements are needed.

Book a personalised demo

For a more in depth look at what Metomic can do for your organisation, book a personalised demo with Metomic’s team of security experts.

They’ll guide you through the features and benefits of Metomic’s solutions, tailoring advice and strategies to your organisation’s unique needs.

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Jules Thorpe-Smith

Content Creator

Jules Thorpe-Smith is a Content Creator at Metomic

Key points

  • Large Language Models (LLMs) are advanced tools for natural language processing with diverse applications in organisations.
  • The use of LLMs introduces risks such as sensitive information disclosure, requiring careful management.
  • Implementing effective security strategies and best practices is essential to mitigate these risks.
  • Metomic helps you to secure LLM usage by managing sensitive data, automating access controls, and monitoring data transfers within SaaS tools.

With the increasing use of Generative AI like ChatGPT and Gemini, what are the key security risks organisations need to be aware of?

Large Language Models (LLMs) like ChatGPT and Google’s Gemini are becoming a big part of how businesses operate, transforming everything from customer service to content creation.

While these models are incredibly powerful, they also come with some serious security concerns. It’s crucial to understand and manage these risks to keep your sensitive information safe.

In this article, we'll break down what you need to know about LLM security, share practical tips, and help you navigate the best practices for protecting your organisation’s data in this new era of AI.

What are LLMs?

Large Language Models (LLMs) are a type of artificial intelligence that can read, understand, and generate text that sounds just like a human wrote it.

They work by using a technology called deep learning, which helps them learn from massive amounts of text data. This allows them to chat with users, draft content, and assist with a variety of tasks in a way that feels natural and intuitive.

It’s also becoming increasingly popular. At the moment, 58% of organisations are making use of LLMs - however, it's worth noting that 44% of these are still experimenting with how best to use them.

Clearly, there’s a growing curiosity about what LLMs can do, even as businesses figure out their full potential.

How can they be used in organisations?

Large Language Models (LLMs) are proving to be game-changers for organisations in several ways.

They can handle customer queries efficiently, help marketing departments plan and create engaging content, and it can even draft detailed reports with minimal human input.

By incorporating LLMs, businesses can streamline their operations, cut down on repetitive tasks, and spark new ideas and innovations.

Take the medical field, for example. A recent study shared on ArXiv shows that modern medical LLMs, when trained with the latest data, can support clinical decisions by incorporating cutting-edge research.

This means faster, more accurate support for healthcare professionals, and a big step forward in how organisations use data to make informed decisions.

What are the security risks of using LLMs?

When using Large Language Models (LLMs), one major security concern is the risk of accidentally exposing sensitive information.

These models learn from huge amounts of data, and sometimes they might unintentionally reveal confidential details from their training or user inputs.

A recent study from the University of North Carolina found that even after attempts to remove sensitive data, models like ChatGPT and Gemini can still output this information.

This means that, despite our best efforts to clean up data, it can linger and potentially be exposed.

For organisations, this could lead to accidental leaks of private or sensitive information, which might damage their reputation, lead to legal issues, or breach privacy regulations.

Keeping on top of these risks is key to making sure LLMs are used safely and responsibly.

What are the vulnerabilities?

Large Language Models (LLMs) have a few key vulnerabilities that can be exploited if we're not careful.

One such issue is data poisoning, where attackers deliberately insert misleading or harmful data into the model’s training set. This can skew the model’s responses and undermine its reliability.

Another vulnerability is prompt injection, where malicious inputs are crafted to trick the LLM into producing unintended or harmful outputs.

For example, an attacker might insert misleading prompts that cause the model to generate biased or incorrect information.

These vulnerabilities can seriously compromise the integrity of an LLM, making it unreliable or even dangerous to use.

Why is it important to secure LLMs?

When LLMs process confidential information, any security breach can lead to severe consequences. Imagine the impact of sensitive customer data or proprietary business information being exposed.

Not only could this result in significant data loss, but it could also damage your organisation's finances and reputation. IBM’s 2024 “Cost of a Data Breach,” report highlighted that the global average cost of a data breach has risen by 10%, now standing at $4.88 million.

Beyond the financial costs, there's the trust factor—customers and clients need to know their data is safe with you. Losing that trust can really harm your company with 66% of consumers stating they would not trust a company following a data breach.

Ensuring your LLMs are secure helps protect against these potential breaches, maintaining both your data integrity and your organisation's reputation.

How can organisations create an LLM security strategy?

Creating a comprehensive security strategy for LLMs is essential for safeguarding your data and maintaining operational integrity.

Here are some key steps to developing a comprehensive LLM security strategy:

1. Data Encryption

Encrypting data both at rest and in transit ensures that sensitive information remains protected from unauthorised access.

2. Model Integrity Checks

Regularly perform integrity checks on your LLMs to detect any tampering or anomalies.

3. Secure Infrastructure

Implement secure infrastructure protocols, including firewalls, intrusion detection systems, and multi-factor authentication.

4. Access Controls

Limit access to your LLMs to authorised personnel only. Use Role-Based Access Controls (RBAC) to manage who can view or alter the models and their data.

5. Regular Audits and Monitoring

Conduct regular security audits and continuous monitoring to identify and address vulnerabilities promptly.

6. Training and Awareness

Educate your team about the importance of LLM security and best practices. An informed team is better equipped to handle and prevent security issues.

Best practices for securing LLMs

Securing LLMs requires a combination of best practices to ensure data integrity and privacy. Here are some essential strategies to consider:

1. Adversarial Training

Train your LLMs using adversarial examples to make them more resilient against attacks.

2. Input Validation

Implement strict input validation protocols to prevent harmful data from compromising your models. Ensuring that inputs are clean and safe reduces the risk of data poisoning.

3. Access Controls

Use RBAC to manage who can interact with your LLMs.

4. Secure Execution Environments

Run your LLMs in secure, isolated environments to prevent unauthorised access and tampering.

5. Federated Learning

Implement federated learning to train LLMs across multiple devices or locations without centralising data. This method enhances privacy by keeping sensitive data local while still allowing the model to learn from a diverse dataset.

6. Differential Privacy

Integrate differential privacy techniques to add noise to the data, making it difficult to identify individual data points. This protects user privacy while still allowing useful insights to be drawn from the data.

How Metomic can help

Metomic provides a comprehensive suite of tools designed to enhance the security of LLMs and protect sensitive data.

  • Data discovery and classification: Metomic can help you identify and protect sensitive information across various SaaS applications.
  • Automated access controls: Our platform ensures only authorised users can access critical data, reducing the risk of data breaches.
  • Data retention policies: With Metomic, you can automatically manage and enforce data retention policies to protect sensitive information.
  • Monitoring and alerts: You can monitor data transfers and receive alerts when sensitive information is shared outside the corporate domain.
  • Visibility and management: You’re able to gain clear visibility into where sensitive data resides within your organisation to effectively manage LLM security.

Getting started with Metomic

Free risk assessment scans

Metomic’s free risk assessment scans can identify potential vulnerabilities and areas for improvement, and are available for Google Drive, Slack, ChatGPT and more.

These scans provide valuable insights into the security posture of your large language models, helping you understand where enhancements are needed.

Book a personalised demo

For a more in depth look at what Metomic can do for your organisation, book a personalised demo with Metomic’s team of security experts.

They’ll guide you through the features and benefits of Metomic’s solutions, tailoring advice and strategies to your organisation’s unique needs.

Key points

  • Large Language Models (LLMs) are advanced tools for natural language processing with diverse applications in organisations.
  • The use of LLMs introduces risks such as sensitive information disclosure, requiring careful management.
  • Implementing effective security strategies and best practices is essential to mitigate these risks.
  • Metomic helps you to secure LLM usage by managing sensitive data, automating access controls, and monitoring data transfers within SaaS tools.

With the increasing use of Generative AI like ChatGPT and Gemini, what are the key security risks organisations need to be aware of?

Large Language Models (LLMs) like ChatGPT and Google’s Gemini are becoming a big part of how businesses operate, transforming everything from customer service to content creation.

While these models are incredibly powerful, they also come with some serious security concerns. It’s crucial to understand and manage these risks to keep your sensitive information safe.

In this article, we'll break down what you need to know about LLM security, share practical tips, and help you navigate the best practices for protecting your organisation’s data in this new era of AI.

What are LLMs?

Large Language Models (LLMs) are a type of artificial intelligence that can read, understand, and generate text that sounds just like a human wrote it.

They work by using a technology called deep learning, which helps them learn from massive amounts of text data. This allows them to chat with users, draft content, and assist with a variety of tasks in a way that feels natural and intuitive.

It’s also becoming increasingly popular. At the moment, 58% of organisations are making use of LLMs - however, it's worth noting that 44% of these are still experimenting with how best to use them.

Clearly, there’s a growing curiosity about what LLMs can do, even as businesses figure out their full potential.

How can they be used in organisations?

Large Language Models (LLMs) are proving to be game-changers for organisations in several ways.

They can handle customer queries efficiently, help marketing departments plan and create engaging content, and it can even draft detailed reports with minimal human input.

By incorporating LLMs, businesses can streamline their operations, cut down on repetitive tasks, and spark new ideas and innovations.

Take the medical field, for example. A recent study shared on ArXiv shows that modern medical LLMs, when trained with the latest data, can support clinical decisions by incorporating cutting-edge research.

This means faster, more accurate support for healthcare professionals, and a big step forward in how organisations use data to make informed decisions.

What are the security risks of using LLMs?

When using Large Language Models (LLMs), one major security concern is the risk of accidentally exposing sensitive information.

These models learn from huge amounts of data, and sometimes they might unintentionally reveal confidential details from their training or user inputs.

A recent study from the University of North Carolina found that even after attempts to remove sensitive data, models like ChatGPT and Gemini can still output this information.

This means that, despite our best efforts to clean up data, it can linger and potentially be exposed.

For organisations, this could lead to accidental leaks of private or sensitive information, which might damage their reputation, lead to legal issues, or breach privacy regulations.

Keeping on top of these risks is key to making sure LLMs are used safely and responsibly.

What are the vulnerabilities?

Large Language Models (LLMs) have a few key vulnerabilities that can be exploited if we're not careful.

One such issue is data poisoning, where attackers deliberately insert misleading or harmful data into the model’s training set. This can skew the model’s responses and undermine its reliability.

Another vulnerability is prompt injection, where malicious inputs are crafted to trick the LLM into producing unintended or harmful outputs.

For example, an attacker might insert misleading prompts that cause the model to generate biased or incorrect information.

These vulnerabilities can seriously compromise the integrity of an LLM, making it unreliable or even dangerous to use.

Why is it important to secure LLMs?

When LLMs process confidential information, any security breach can lead to severe consequences. Imagine the impact of sensitive customer data or proprietary business information being exposed.

Not only could this result in significant data loss, but it could also damage your organisation's finances and reputation. IBM’s 2024 “Cost of a Data Breach,” report highlighted that the global average cost of a data breach has risen by 10%, now standing at $4.88 million.

Beyond the financial costs, there's the trust factor—customers and clients need to know their data is safe with you. Losing that trust can really harm your company with 66% of consumers stating they would not trust a company following a data breach.

Ensuring your LLMs are secure helps protect against these potential breaches, maintaining both your data integrity and your organisation's reputation.

How can organisations create an LLM security strategy?

Creating a comprehensive security strategy for LLMs is essential for safeguarding your data and maintaining operational integrity.

Here are some key steps to developing a comprehensive LLM security strategy:

1. Data Encryption

Encrypting data both at rest and in transit ensures that sensitive information remains protected from unauthorised access.

2. Model Integrity Checks

Regularly perform integrity checks on your LLMs to detect any tampering or anomalies.

3. Secure Infrastructure

Implement secure infrastructure protocols, including firewalls, intrusion detection systems, and multi-factor authentication.

4. Access Controls

Limit access to your LLMs to authorised personnel only. Use Role-Based Access Controls (RBAC) to manage who can view or alter the models and their data.

5. Regular Audits and Monitoring

Conduct regular security audits and continuous monitoring to identify and address vulnerabilities promptly.

6. Training and Awareness

Educate your team about the importance of LLM security and best practices. An informed team is better equipped to handle and prevent security issues.

Best practices for securing LLMs

Securing LLMs requires a combination of best practices to ensure data integrity and privacy. Here are some essential strategies to consider:

1. Adversarial Training

Train your LLMs using adversarial examples to make them more resilient against attacks.

2. Input Validation

Implement strict input validation protocols to prevent harmful data from compromising your models. Ensuring that inputs are clean and safe reduces the risk of data poisoning.

3. Access Controls

Use RBAC to manage who can interact with your LLMs.

4. Secure Execution Environments

Run your LLMs in secure, isolated environments to prevent unauthorised access and tampering.

5. Federated Learning

Implement federated learning to train LLMs across multiple devices or locations without centralising data. This method enhances privacy by keeping sensitive data local while still allowing the model to learn from a diverse dataset.

6. Differential Privacy

Integrate differential privacy techniques to add noise to the data, making it difficult to identify individual data points. This protects user privacy while still allowing useful insights to be drawn from the data.

How Metomic can help

Metomic provides a comprehensive suite of tools designed to enhance the security of LLMs and protect sensitive data.

  • Data discovery and classification: Metomic can help you identify and protect sensitive information across various SaaS applications.
  • Automated access controls: Our platform ensures only authorised users can access critical data, reducing the risk of data breaches.
  • Data retention policies: With Metomic, you can automatically manage and enforce data retention policies to protect sensitive information.
  • Monitoring and alerts: You can monitor data transfers and receive alerts when sensitive information is shared outside the corporate domain.
  • Visibility and management: You’re able to gain clear visibility into where sensitive data resides within your organisation to effectively manage LLM security.

Getting started with Metomic

Free risk assessment scans

Metomic’s free risk assessment scans can identify potential vulnerabilities and areas for improvement, and are available for Google Drive, Slack, ChatGPT and more.

These scans provide valuable insights into the security posture of your large language models, helping you understand where enhancements are needed.

Book a personalised demo

For a more in depth look at what Metomic can do for your organisation, book a personalised demo with Metomic’s team of security experts.

They’ll guide you through the features and benefits of Metomic’s solutions, tailoring advice and strategies to your organisation’s unique needs.

Jules Thorpe-Smith

Content Creator

Jules Thorpe-Smith is a Content Creator at Metomic

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