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TypePilot: Leveraging the Scala Type System for Secure LLM-generated Code

Authors: Alexander Sternfeld, Andrei Kucharavy, Ljiljana Dolamic

Published: 2025-10-13

arXiv ID: 2510.11151v1

Added to Library: 2025-11-14 23:12 UTC

📄 Abstract

Large language Models (LLMs) have shown remarkable proficiency in code generation tasks across various programming languages. However, their outputs often contain subtle but critical vulnerabilities, posing significant risks when deployed in security-sensitive or mission-critical systems. This paper introduces TypePilot, an agentic AI framework designed to enhance the security and robustness of LLM-generated code by leveraging strongly typed and verifiable languages, using Scala as a representative example. We evaluate the effectiveness of our approach in two settings: formal verification with the Stainless framework and general-purpose secure code generation. Our experiments with leading open-source LLMs reveal that while direct code generation often fails to enforce safety constraints, just as naive prompting for more secure code, our type-focused agentic pipeline substantially mitigates input validation and injection vulnerabilities. The results demonstrate the potential of structured, type-guided LLM workflows to improve the SotA of the trustworthiness of automated code generation in high-assurance domains.

🔍 Key Points

  • The paper introduces the concept of Deep Research (DR) agents that leverage LLMs to perform complex research tasks, revealing significant vulnerabilities when such agents respond to harmful queries.
  • It outlines two novel jailbreak strategies—Plan Injection and Intent Hijack—that exploit the planning and research capabilities of DR agents, demonstrating their risks in generating harmful content.
  • Extensive experiments highlight that DR agents can circumvent traditional alignment mechanisms by producing coherent and dangerous reports that standalone LLMs would reject.
  • The proposed DeepREJECT evaluation metric is introduced, which assesses whether the generated content aligns with harmful intents and the quality of knowledge provided, outperforming previous benchmarks.
  • The findings raise critical questions about the safety measures in deploying LLMs in sensitive domains, especially in contexts like biosecurity.

💡 Why This Paper Matters

This paper is crucial as it identifies the elevated risks associated with Deep Research agents powered by Large Language Models, emphasizing the urgent need for refined safety analyses and robust alignment strategies. The methodologies proposed offer significant insights into the potential for misuse in high-stakes domains, calling for an overhaul in how AI systems are designed to ensure safety in practical applications.

🎯 Why It's Interesting for AI Security Researchers

The paper will intrigue AI security researchers as it exposes the critical vulnerabilities in existing alignment frameworks when applied to advanced AI systems like DR agents. It provides novel attack methodologies that can inform the development of more robust safety protocols and prompts further investigation into the potential misuse of AI technologies in sensitive and high-risk environments.

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