Impossible to hide secret ...: Uncovering Security and Privacy Issues in LLM-native IDEs

2026-07-28

AI Summary

This study addresses critical security and privacy concerns in Large Language Model–native Integrated Development Environments (LIDEs), which undermine developer trust. Through a large-scale mixed-methods analysis of over 1.1 million Reddit posts and more than 6,000 comments, we systematically demonstrate that these issues primarily stem from system-level design flaws rather than inherent limitations of the underlying large language models. We introduce the first comprehensive taxonomy of LIDE-specific security and privacy risks, identifying 446 relevant discussions and distilling core threat categories such as uncontrolled data access and unconstrained autonomous behavior. Furthermore, we synthesize practical mitigation strategies employed by developers and derive actionable design principles for building trustworthy LIDEs.

Abstract

LLM-native IDEs (Integrated Development Environments), aka LIDEs, are designed from the ground up to work with Large Language Models (LLMs). LIDEs have found remarkable success in Software Engineering (SE) tasks such as coding, debugging, and program comprehension. LIDEs are software systems, and, like any system, they can exhibit vulnerabilities. In this paper, we study the security and privacy issues that developers reported while using popular LIDEs in their development tasks. We collected 1.1M posts from 29 popular subreddits related to LIDEs. We identified 446 posts and analyzed over 6K comments to the posts that discussed security and privacy issues in almost all popular LIDEs, such as Cursor, Copilot, Codex, etc. Using a mix of qualitative and quantitative methods, we constructed a taxonomy of the reported security and privacy issues. Our results show that most issues in LIDEs stem from system-level design choices, rather than the underlying LLMs, such as user data access, unchecked autonomous actions, etc. To overcome these issues, developers frequently relied on external safeguards like code sandboxing and manual reviewing, highlighting prevalent mistrust among developers about LIDEs. We share lessons from our study to support future design of secure and privacy-aware LIDEs.

Problem

• LLM-native IDEs
• security
• privacy
• software engineering
• vulnerabilities

Innovation

• LLM-native IDEs
• security and privacy
• empirical study
• system-level vulnerabilities
• developer trust