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The paper introduces STELP, a Secure Transpiler and Executor of LLM-Generated Programs, to address the safety and reliability issues associated with directly executing code generated by Large Language Models in production systems. STELP operates by transpiling LLM-generated code into a safer, controlled environment, mitigating vulnerabilities such as data poisoning and malicious attacks. The authors demonstrate STELP's effectiveness through benchmarks on correctness, safety, and latency, showing it outperforms existing methods in safely executing risky code snippets using a newly created human-validated dataset of insecure code.
Stop blindly trusting LLM-generated code: STELP offers a secure execution environment that significantly outperforms existing methods in safely handling malicious code snippets.
Rapid evolution of Large Language Models (LLMs) has achieved major advances in reasoning, planning, and function-calling capabilities. Multi-agentic collaborative frameworks using such LLMs place them at the center of solving software development-related tasks such as code generation. However, direct use of LLM generated code in production software development systems is problematic. The code could be unstable or erroneous and contain vulnerabilities such as data poisoning, malicious attacks, and hallucinations that could lead to widespread system malfunctions. This prohibits the adoption of LLM generated code in production AI systems where human code reviews and traditional secure testing tools are impractical or untrustworthy. In this paper, we discuss safety and reliability problems with the execution of LLM generated code and propose a Secure Transpiler and Executor of LLM-Generated Program (STELP), capable of executing LLM-generated code in a controlled and safe manner. STELP secures autonomous production AI systems involving code generation, filling the critical void left by the impracticality or limitations of traditional secure testing methodologies and human oversight. This includes applications such as headless code generation-execution and LLMs that produce executable code snippets as an action plan to be executed in real time. We contribute a human-validated dataset of insecure code snippets and benchmark our approach on publicly available datasets for correctness, safety, and latency. Our results demonstrate that our approach outperforms an existing method by a significant margin, particularly in its ability to safely execute risky code snippets. Warning: This paper contains malicious code snippets that should be run with caution.
