Testing machine learning systems is currently more of an art form than a standardized engineering practice. This is particularly problematic for machine learning in mission-critical contexts. This article summarizes three steps from our ML testing series that any development team can take when testing their ML systems.
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
In-context learning
As users increasingly rely on Large Language Models (LLMs) to accomplish their daily tasks, their concerns about the potential leakage of private data by these models have surged.
[Provide the input text here]
[Provide the input text here]
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.
Lorem ipsum dolor sit amet, Q: I had 10 cookies. I ate 2 of them, and then I gave 5 of them to my friend. My grandma gave me another 2boxes of cookies, with 2 cookies inside each box. How many cookies do I have now?
Title italic
A: At the beginning there was 10 cookies, then 2 of them were eaten, so 8 cookies were left. Then 5 cookieswere given toa friend, so 3 cookies were left. 3 cookies + 2 boxes of 2 cookies (4 cookies) = 7 cookies. Youhave 7 cookies.
English to French Translation:
Q: A bartender had 20 pints. One customer has broken one pint, another has broken 5 pints. A bartender boughtthree boxes, 4 pints in each. How many pints does bartender have now?
Lorem ipsum dolor sit amet, line first
line second
line third
Lorem ipsum dolor sit amet, Q: I had 10 cookies. I ate 2 of them, and then I gave 5 of them to my friend. My grandma gave me another 2boxes of cookies, with 2 cookies inside each box. How many cookies do I have now?
Title italic Title italicTitle italicTitle italicTitle italicTitle italicTitle italic
A: At the beginning there was 10 cookies, then 2 of them were eaten, so 8 cookies were left. Then 5 cookieswere given toa friend, so 3 cookies were left. 3 cookies + 2 boxes of 2 cookies (4 cookies) = 7 cookies. Youhave 7 cookies.
English to French Translation:
Q: A bartender had 20 pints. One customer has broken one pint, another has broken 5 pints. A bartender boughtthree boxes, 4 pints in each. How many pints does bartender have now?
Testing machine learning (ML) systems is currently more of an art form than a standardized engineering practice. This is particularly problematic for machine learning in mission-critical contexts. In these use cases, strict performance guarantees and regulatory compliance are a must. The best engineering teams at companies like Tesla have built sophisticated testing infrastructure to ensure the reliability of their ML systems. Now it is time to make effective and systematic ML testing a reality for the rest of us–including smaller engineering teams–as well.
This article summarizes three steps from our ML testing series that any development team can take when testing their ML systems:
Adopting these three strategies is the first step to making ML testing more systematic and effective. They often provide a high return on investment. This is the case especially for smaller engineering teams that don’t have Tesla’s resources but that require strict performance guarantees and want to move through product development quickly and efficiently.
Lakera’s validation engine, MLTest, finds critical performance vulnerabilities in computer vision systems before they enter operation. Built with industry-leading AI and safety expertise, MLTest makes reliability a no-brainer for entire development teams. Get in touch if you want to learn more!
[1] Definition from ISO 21448: Road vehicles — Safety of the intended functionality.
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
Get Lakera's AI Security Guide for an overview of threats and protection strategies.
Compare the EU AI Act and the White House’s AI Bill of Rights.
Get Lakera's AI Security Guide for an overview of threats and protection strategies.
Explore real-world LLM exploits, case studies, and mitigation strategies with Lakera.
Use our checklist to evaluate and select the best LLM security tools for your enterprise.
Discover risks and solutions with the Lakera LLM Security Playbook.
Discover risks and solutions with the Lakera LLM Security Playbook.
Subscribe to our newsletter to get the recent updates on Lakera product and other news in the AI LLM world. Be sure you’re on track!
Lakera Guard protects your LLM applications from cybersecurity risks with a single line of code. Get started in minutes. Become stronger every day.
Several people are typing about AI/ML security. Come join us and 1000+ others in a chat that’s thoroughly SFW.
