# Automatically Generating Rules of Malicious Software Packages via Large Language Model

arXiv: https://arxiv.org/abs/2504.17198 · HTML: https://mlsanity.com/paper/2504.17198/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 76 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 30 of 30)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Teaching Large Language Models to Self-Debug](https://mlsanity.com/paper/2304.05128/) (2023)
- [Large Language Models Can Self-Improve](https://mlsanity.com/paper/2210.11610/) (2022)
- [Detecting Pretraining Data from Large Language Models](https://mlsanity.com/paper/2310.16789/) (2023)
- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [Benchmark Data Contamination of Large Language Models: A Survey](https://mlsanity.com/paper/2406.04244/) (2024)
- [Keep the Conversation Going: Fixing 162 out of 337 bugs for 0.42 each using ChatGPT](https://mlsanity.com/paper/2304.00385/) (2023)
- [Examining Zero-Shot Vulnerability Repair with Large Language Models](https://mlsanity.com/paper/2112.02125/) (2021)
- [LLMs Cannot Reliably Identify and Reason About Security Vulnerabilities (Yet?): A Comprehensive Evaluation, Framework, and Benchmarks](https://mlsanity.com/paper/2312.12575/) (2023)
- [Backstabber's Knife Collection: A Review of Open Source Software Supply Chain Attacks](https://mlsanity.com/paper/2005.09535/) (2020)
- [Taxonomy of Attacks on Open-Source Software Supply Chains](https://mlsanity.com/paper/2204.04008/) (2022)
- [Prompting Is All You Need: Automated Android Bug Replay with Large Language Models](https://mlsanity.com/paper/2306.01987/) (2023)
- [Small World with High Risks: A Study of Security Threats in the npm Ecosystem](https://mlsanity.com/paper/1902.09217/) (2019)
- [Automatic Code Summarization via ChatGPT: How Far Are We?](https://mlsanity.com/paper/2305.12865/) (2023)
- [An Empirical Study of Malicious Code In PyPI Ecosystem](https://mlsanity.com/paper/2309.11021/) (2023)
- [The Hitchhiker's Guide to Program Analysis: A Journey with Large Language Models](https://mlsanity.com/paper/2308.00245/) (2023)
- [Automatic Yara Rule Generation Using Biclustering](https://mlsanity.com/paper/2009.03779/) (2020)
- [What are Weak Links in the npm Supply Chain?](https://mlsanity.com/paper/2112.10165/) (2021)
- [Pop Quiz! Can a Large Language Model Help With Reverse Engineering?](https://mlsanity.com/paper/2202.01142/) (2022)
- [How Far Have We Gone in Binary Code Understanding Using Large Language Models](https://mlsanity.com/paper/2404.09836/) (2024)
- [On the evolution of technical lag in the npm package dependency network](https://mlsanity.com/paper/1806.01545/) (2018)
- [An Empirical Comparison of Developer Retention in the RubyGems and npm Software Ecosystems](https://mlsanity.com/paper/1708.02618/) (2017)
- [Beyond Metadata: Code-centric and Usage-based Analysis of Known Vulnerabilities in Open-source Software](https://mlsanity.com/paper/1806.05893/) (2018)
- [Towards the Detection of Malicious Java Packages](https://mlsanity.com/paper/2210.03998/) (2022)

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

- [FALCON: Transforming Cyber Threat Intelligence into Deployable IDS Rules with Self-Reflection](https://mlsanity.com/paper/2508.18684/) (2025)
- [ML-Powered LDAP Reconnaissance Detection using Weak Supervision](https://mlsanity.com/paper/2606.28917/) (2026)

## Abstract (arXiv)

Today's security tools predominantly rely on predefined rules crafted by experts, making them poorly adapted to the emergence of software supply chain attacks. To tackle this limitation, we propose a novel tool, RuleLLM, which leverages large language models (LLMs) to automate rule generation for OSS ecosystems. RuleLLM extracts metadata and code snippets from malware as its input, producing YARA and Semgrep rules that can be directly deployed in software development. Specifically, the rule generation task involves three subtasks: crafting rules, refining rules, and aligning rules. To validate RuleLLM's effectiveness, we implemented a prototype system and conducted experiments on the dataset of 1,633 malicious packages. The results are promising that RuleLLM generated 763 rules (452 YARA and 311 Semgrep) with a precision of 85.2% and a recall of 91.8%, outperforming state-of-the-art (SOTA) tools and scored-based approaches. We further analyzed generated rules and proposed a rule taxonomy: 11 categories and 38 subcategories.