Source URL: https://simonwillison.net/2024/Aug/28/how-anthropic-built-artifacts/#atom-everything
Source: Simon Willison’s Weblog
Title: How Anthropic built Artifacts
Feedly Summary: How Anthropic built Artifacts
Gergely Orosz interviews five members of Anthropic about how they built Artifacts on top of Claude 3.5 Sonnet with a small team in just three months.
The initial prototype used Streamlit, and the biggest challenge was building a robust sandbox to run the LLM-generated code in:
We use iFrame sandboxes with full-site process isolation. This approach has gotten robust over the years. This protects users’ main Claude.ai browsing session from malicious artifacts. We also use strict Content Security Policies (CSPs) to enforce limited and controlled network access.
Tags: claude-artifacts, anthropic, claude, gergely-orosz, ai, llms
AI Summary and Description: Yes
Summary: The text discusses Anthropic’s development of Artifacts, a tool built on the Claude 3.5 Sonnet. It highlights the innovative approach to sandboxing LLM-generated code, underscoring the importance of security measures like process isolation and Content Security Policies (CSPs) for user protection.
Detailed Description: The text provides insights into Anthropic’s engineering process and the security measures employed during the development of Artifacts. This content is particularly relevant to professionals involved in AI, AI security, and software security, as it addresses both the technical and security aspects of working with large language models (LLMs).
– Anthropic’s Artifacts were built by a small team within a three-month timeline.
– Initial prototype development utilized Streamlit, a popular framework for building data applications.
– A major challenge faced was creating a secure and robust sandbox for executing code generated by the LLM.
– The use of iFrame sandboxes helps in ensuring process isolation, protecting users’ primary browsing sessions from any harmful outputs or malicious activities.
– Strict Content Security Policies (CSPs) have been implemented which enforce limited and controlled network access, enhancing security by restricting where and how the code can interact with networks.
These security practices not only serve to protect end-users but also contribute to the overall robustness and reliability of AI applications. The insights gathered from this development can inform best practices for similar projects and help address vital concerns surrounding AI security and user privacy within cloud-based AI systems. This text is significant for developers and security professionals as it illustrates practical implementations of LLM security strategies.