# LAMeD: LLM-generated Annotations for Memory Leak Detection

arXiv: https://arxiv.org/abs/2505.02376 · HTML: https://mlsanity.com/paper/2505.02376/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2025.

## Built on by (most cited first, 2 of 2)

- [VulInstruct: Teaching LLMs Root-Cause Reasoning for Vulnerability Detection via Security Specifications](https://mlsanity.com/paper/2511.04014/) (2025)
- [TyPatch: Transforming Patches into Typestate Rules for Kernel Bug Detection](https://mlsanity.com/paper/2609.13728/) (2026)

## Abstract (arXiv)

Static analysis tools are widely used to detect software bugs and vulnerabilities but often struggle with scalability and efficiency in complex codebases. Traditional approaches rely on manually crafted annotations -- labeling functions as sources or sinks -- to track data flows, e.g., ensuring that allocated memory is eventually freed, and code analysis tools such as CodeQL, Infer, or Cooddy can use function specifications, but manual annotation is laborious and error-prone, especially for large or third-party libraries. We present LAMeD (LLM-generated Annotations for Memory leak Detection), a novel approach that leverages large language models (LLMs) to automatically generate function-specific annotations. When integrated with analyzers such as Cooddy, LAMeD significantly improves memory leak detection and reduces path explosion. We also suggest directions for extending LAMeD to broader code analysis.