Learn how to protect against the most common LLM vulnerabilities
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?
â
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?
â
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?
Ensuring the security of generative AI applications is more critical than ever.
At Lakera, we are committed to providing comprehensive security solutions to protect GenAI applications against various threats, including prompt injections and PII disclosure.
As part of our robust security suite, we are excited to announce a significant upgrade to Lakera Guard's Content Moderation capabilities.
Whatâs New With Lakera Guardâs Content Moderation
The latest version of Lakera Guard brings several key enhancements.
Accuracy Improvements
We have nearly doubled the accuracy across all moderation categories. This means fewer false positives and negatives, ensuring that harmful content is effectively flagged and managed.
Latency Reduction
By optimizing our backend processes, we have reduced latency by 50x for long prompts. This improvement allows for faster content analysis and real-time moderation.
Profanity Detection
We have introduced a new profanity detector within the moderation endpoint. This feature flags any words or phrases considered offensive or inappropriate, including cursing, sexually offensive terms, religious blasphemies, and abusive phrases directed against ethnic groups or minorities.
Broadened Scope
Hate Speech
The hate speech detector now flags any content that expresses, incites, or promotes harassing language directed at any target, including protected groups. This includes content involving violence or harm toward any target and discrimination through the use of slurs.
Sexually Explicit Content
The detector now also flags sex education, medical, or wellness-related content. It will flag sexual content regardless of whether it is vulgar.
Ready for Enterprise Deployment
With these upgrades, Lakera Guard extends its enterprise-grade capabilities to protect GenAI applications in production. Our updated content moderation capabilities provide the unique mix of high accuracy and low latency required for GenAI use cases.
These improvements are available now to all Lakera SaaS and self-hosted customers. Comprehensive documentation and support are available to help with the transition.
Lakera Guardâs new enterprise-grade content moderation capabilities represent a significant leap forward in our mission to provide the strongest defenses for enterprise GenAI infrastructure.
â
For more information, visit our documentation or contact our support team at support@lakera.ai.
Learn how to protect against the most common LLM vulnerabilities
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
GenAI Security Preparedness
Report 2024
Get the first-of-its-kind report on how organizations are preparing for GenAI-specific threats.
Free Download
Explore AI Regulations.
Compare the EU AI Act and the White Houseâs AI Bill of Rights.
Understand AI Security Basics.
Get Lakera's AI Security Guide for an overview of threats and protection strategies.
Uncover LLM Vulnerabilities.
Explore real-world LLM exploits, case studies, and mitigation strategies with Lakera.
Optimize LLM Security Solutions.
Use our checklist to evaluate and select the best LLM security tools for your enterprise.
Master Prompt Injection Attacks.
Discover risks and solutions with the Lakera LLM Security Playbook.
Unlock Free AI Security Guide.
Discover risks and solutions with the Lakera LLM Security Playbook.
Donât miss the updates!
Subscribe to our newsletter to get the recent updates on Lakera product and other news in the AI LLM world. Be sure youâre on track!
You might be interested
Introducing Lakera Chrome Extension - Privacy Guard for Your Conversations with ChatGPT
Lakera introduces Lakera PII Extensionâa user-friendly Chrome plugin that allows you to input prompts to ChatGPT securely.
Lakera Guard Expands Content Moderation Capabilities to Protect Your AI Applications and Users
Lakera Guard now offers expanded coverage to detect violent and dangerous content, ensuring that your AI applications remain safe, secure, and compliant.
Lakera Guard Enhances PII Detection and Data Loss Prevention for Enterprise Applications
Lakera Guard introduces Advanced PII Detection and DLP capabilities.
.png)
Lakeraâs Prompt Injection Test (PINT)âA New Benchmark for Evaluating Prompt Injection Solutions
We've released the first version of a new Prompt Injection Test (PINT) Benchmark that can be used to evaluate any prompt injection detection system with a comprehensive dataset that no model, including ours, is directly trained on.
ChainGuard: Guard Your LangChain Apps with Lakera
In this tutorial, we'll show you how to integrate Lakera Guard into your LangChain applications to protect them from the most common AI security risks, including prompt injections, toxic content, data loss, and more!
Introducing Lakera Guard â Bringing Enterprise-Grade Security to LLMs with One Line of Code
Introducing Lakera Guard: Bringing enterprise-grade security to LLMs with one line of code.
Activate
untouchable mode.
untouchable mode.
Get started for free.
Lakera Guard protects your LLM applications from cybersecurity risks with a single line of code. Get started in minutes. Become stronger every day.
Join our Slack Community.
Several people are typing about AI/ML security. â¨Come join us and 1000+ others in a chat thatâs thoroughly SFW.
