SandboxEval introduces a comprehensive test suite for evaluating security vulnerabilities in environments that execute untrusted LLM-generated code.

• Identifies potential exploitation pathways in code testing infrastructure
• Reduces risks of compromised assessment systems
• Establishes security best practices for LLM code evaluation
• Prevents data exfiltration and system compromise

This research is critical for organizations implementing AI coding assistants, as it provides a systematic approach to prevent security breaches when testing or deploying LLM-generated code in production environments.

SandboxEval: Towards Securing Test Environment for Untrusted Code