Compile-time Security Analysis and Optimization of Sensitive String Producers

2026-05-15

AI Summary

This work addresses the persistent challenge of content composition vulnerabilities in software, which remain difficult to mitigate through developer training, static analysis, or templating languages, and are often reproduced in AI-generated code. The paper proposes a general-purpose secure content composition framework that incrementally extends the string expression syntax of general-purpose programming languages to enable precise security analysis and optimization at compile time. Its core innovation lies in a language design principle that minimizes the lexical distance between secure and insecure idioms, combined with dynamic-semantics-informed static analysis, compile-time diagnostics, and library-based encapsulation of secure logic. This approach facilitates collaborative remediation by both developers and AI systems, offering strong compile-time safety guarantees while maintaining performance comparable to native string concatenation, thereby significantly enhancing both the security and usability of string composition.

Abstract

Content composition vulnerabilities remain among the most prevalent and persistent classes of security weakness in deployed software. Prior mitigations, including developer training, static analysis tools, and domain-specific template languages, each face diminishing returns; AI code generation inherits these limitations and introduces new ones, reproducing insecure patterns from training data and lacking reliable context for self-correction. This paper introduces a general framework for secure content composition that extends across content languages and integrates directly into general-purpose programming languages via additive changes to string expression syntax. We define a language design goal of minimizing the lexical distance between secure and insecure idioms, and show that this goal admits practical compilation strategies: static analyses specified in terms of dynamic semantics, runtime performance approaching naïve string concatenation, and developer-facing diagnostics surfaced as compile-time errors or warnings. The approach enables an effective division of labor: security engineers encode composition hazards in libraries once; developers and AI coding agents select the appropriate library primitive to implement features correctly without needing to internalize specialist security knowledge; compiler diagnostics provide objective, position-keyed feedback that grounds both human review and iterative AI self-correction; and security responders focus on keeping libraries current rather than auditing ad-hoc security decisions distributed across a codebase.

Problem

• content composition vulnerabilities
• compile-time security
• sensitive string producers
• AI code generation
• string expression syntax

Innovation

• compile-time security
• string composition
• static analysis
• secure by design
• AI code generation