Safeguarding Your First LLM-Powered Innovation: Essential Practices for Security
Manouk
May 13, 2024
In the domain of Language Learning Models, ensuring robust security is not just a necessity; it's a commitment to your users. When launching your first LLM-powered product, you embark on a journey filled with potential, but also with challenges that need addressing from the get-go. I'm excited to share some insights on safeguarding your first LLM-powered product. From all people we spoke to we have discovered the the highs and lows of building these products, I've learned a thing or two about keeping them secure.
Identify and Mitigate Risks in LLM-Powered Solutions
Understanding the various risks associated with LLMs is the first step in building a secure product. Here are some key risks we have seen:
Prompt injection: In short, "hijacking" the output of the language model by providing certain (false) input to the LLM. A huge and growing problem - highlighted as the number 1 hazard of the Owasp top 10 , see their blog article to how prevent it - it is important to detect and prevent while monitoring your LLM-powered product.
Jailbreaking an LLM means using tricky or daring questions to take advantage of the LLM's weaknesses and its ability to create harmful content. This is done to get answers from the LLM that break the company's rules about what can be said or shared. Putting measurement in place will make it possible to detect these users and take actions.
Guarding Against Training Data Compromise: A compromised dataset can significantly skew your LLM's output. Regular audits and data validation processes are essential to maintain the integrity of your model.
Preventing unauthorized code execution: As your LLM interfaces with other systems, the risk of unauthorized access or code execution increases. Ensuring a secure integration environment is key.
Practical Strategies for LLM Security
I spent many hours reading documentation, watching videos, and speaking to various Engineers, Security people and data scientists. Adopting the right strategies can greatly enhance the security of your LLM-powered product, here you’ll find the top strategies we have learned:
Be a Detective with Inputs: Look closely at what’s going into your LLM. If something looks fishy, filter out potentially harmful or manipulative inputs.
Prompt Security: Build prompts that are like a security training for your LLM. Teach it to handle the bad stuff like a pro.
Rethink User Interaction: Rethink how users interact with your LLM. Limiting direct access and employing pre-processing layers can greatly reduce vulnerabilities.
Keep an Eagle Eye with Monitoring: Utilize tools like for ongoing monitoring of user interactions with your LLM. This enables you to detect and respond to unusual patterns and showing you how people are using (or misusing) your LLM.
Building your first LLM-powered product is an awesome journey, and keeping it secure is part of the adventure. It’s about being smart, staying vigilant, and sometimes learning the hard way. You not only protect your product but also build trust with your users, ensuring a safe and productive experience with your LLM technology. LangWatch.ai is the analytics tool that gives you that visibility. With it, you can monitor behavior at the individual user level and put a stop to bad users.
Ready to safeguard your AI?
Request a demo now!
In the domain of Language Learning Models, ensuring robust security is not just a necessity; it's a commitment to your users. When launching your first LLM-powered product, you embark on a journey filled with potential, but also with challenges that need addressing from the get-go. I'm excited to share some insights on safeguarding your first LLM-powered product. From all people we spoke to we have discovered the the highs and lows of building these products, I've learned a thing or two about keeping them secure.
Identify and Mitigate Risks in LLM-Powered Solutions
Understanding the various risks associated with LLMs is the first step in building a secure product. Here are some key risks we have seen:
Prompt injection: In short, "hijacking" the output of the language model by providing certain (false) input to the LLM. A huge and growing problem - highlighted as the number 1 hazard of the Owasp top 10 , see their blog article to how prevent it - it is important to detect and prevent while monitoring your LLM-powered product.
Jailbreaking an LLM means using tricky or daring questions to take advantage of the LLM's weaknesses and its ability to create harmful content. This is done to get answers from the LLM that break the company's rules about what can be said or shared. Putting measurement in place will make it possible to detect these users and take actions.
Guarding Against Training Data Compromise: A compromised dataset can significantly skew your LLM's output. Regular audits and data validation processes are essential to maintain the integrity of your model.
Preventing unauthorized code execution: As your LLM interfaces with other systems, the risk of unauthorized access or code execution increases. Ensuring a secure integration environment is key.
Practical Strategies for LLM Security
I spent many hours reading documentation, watching videos, and speaking to various Engineers, Security people and data scientists. Adopting the right strategies can greatly enhance the security of your LLM-powered product, here you’ll find the top strategies we have learned:
Be a Detective with Inputs: Look closely at what’s going into your LLM. If something looks fishy, filter out potentially harmful or manipulative inputs.
Prompt Security: Build prompts that are like a security training for your LLM. Teach it to handle the bad stuff like a pro.
Rethink User Interaction: Rethink how users interact with your LLM. Limiting direct access and employing pre-processing layers can greatly reduce vulnerabilities.
Keep an Eagle Eye with Monitoring: Utilize tools like for ongoing monitoring of user interactions with your LLM. This enables you to detect and respond to unusual patterns and showing you how people are using (or misusing) your LLM.
Building your first LLM-powered product is an awesome journey, and keeping it secure is part of the adventure. It’s about being smart, staying vigilant, and sometimes learning the hard way. You not only protect your product but also build trust with your users, ensuring a safe and productive experience with your LLM technology. LangWatch.ai is the analytics tool that gives you that visibility. With it, you can monitor behavior at the individual user level and put a stop to bad users.
Ready to safeguard your AI?
Request a demo now!
In the domain of Language Learning Models, ensuring robust security is not just a necessity; it's a commitment to your users. When launching your first LLM-powered product, you embark on a journey filled with potential, but also with challenges that need addressing from the get-go. I'm excited to share some insights on safeguarding your first LLM-powered product. From all people we spoke to we have discovered the the highs and lows of building these products, I've learned a thing or two about keeping them secure.
Identify and Mitigate Risks in LLM-Powered Solutions
Understanding the various risks associated with LLMs is the first step in building a secure product. Here are some key risks we have seen:
Prompt injection: In short, "hijacking" the output of the language model by providing certain (false) input to the LLM. A huge and growing problem - highlighted as the number 1 hazard of the Owasp top 10 , see their blog article to how prevent it - it is important to detect and prevent while monitoring your LLM-powered product.
Jailbreaking an LLM means using tricky or daring questions to take advantage of the LLM's weaknesses and its ability to create harmful content. This is done to get answers from the LLM that break the company's rules about what can be said or shared. Putting measurement in place will make it possible to detect these users and take actions.
Guarding Against Training Data Compromise: A compromised dataset can significantly skew your LLM's output. Regular audits and data validation processes are essential to maintain the integrity of your model.
Preventing unauthorized code execution: As your LLM interfaces with other systems, the risk of unauthorized access or code execution increases. Ensuring a secure integration environment is key.
Practical Strategies for LLM Security
I spent many hours reading documentation, watching videos, and speaking to various Engineers, Security people and data scientists. Adopting the right strategies can greatly enhance the security of your LLM-powered product, here you’ll find the top strategies we have learned:
Be a Detective with Inputs: Look closely at what’s going into your LLM. If something looks fishy, filter out potentially harmful or manipulative inputs.
Prompt Security: Build prompts that are like a security training for your LLM. Teach it to handle the bad stuff like a pro.
Rethink User Interaction: Rethink how users interact with your LLM. Limiting direct access and employing pre-processing layers can greatly reduce vulnerabilities.
Keep an Eagle Eye with Monitoring: Utilize tools like for ongoing monitoring of user interactions with your LLM. This enables you to detect and respond to unusual patterns and showing you how people are using (or misusing) your LLM.
Building your first LLM-powered product is an awesome journey, and keeping it secure is part of the adventure. It’s about being smart, staying vigilant, and sometimes learning the hard way. You not only protect your product but also build trust with your users, ensuring a safe and productive experience with your LLM technology. LangWatch.ai is the analytics tool that gives you that visibility. With it, you can monitor behavior at the individual user level and put a stop to bad users.
Ready to safeguard your AI?