MalTotal: Cost-Effective and Language-Agnostic Malicious Code Poisoning Detection for Millions of Repositories

arXiv:2608.03232 · cs.CR · Submitted 2026-08-04 · Read on arXiv

Jian Zhao, Shenao Wang, Qingyang Wu, Yanjie Zhao, Xiao Cheng, Haoyu Wang

cs.CR

Submitted: 2026-08-04

Comments: Accepted by ISSTA'26

DOI: 10.1145/3832228

Code: https://github.com/security-pride/MalTotal

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Generalization — 12× in this paper · explained in 14 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Ablation Study — 6× in this paper · explained in 3 episodes
• Core — 5× in this paper · explained in 2 episodes
• IEEE Symposium on Security and Privacy — 5× in this paper
• Trace — 5× in this paper · explained in 2 episodes
• ACM International Conference on Automated Software Engineering — 4× in this paper
• Accuracy — 3× in this paper · explained in 1 episode
• Augmentation — 3× in this paper · explained in 2 episodes
• International Conference on Software Engineering — 3× in this paper
• Actor — 2× in this paper · explained in 3 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Data Augmentation — 2× in this paper · explained in 10 episodes
• False Negatives — 2× in this paper · explained in 2 episodes
• Generalization Capabilities — 2× in this paper · explained in 2 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Hallucination — 2× in this paper · explained in 4 episodes
• LLM-as-a-Judge — 2× in this paper · explained in 4 episodes
• Robustness — 2× in this paper · explained in 6 episodes
• Adam — 1× in this paper · explained in 3 episodes
• Chain-of-Thought (CoT) — 1× in this paper · explained in 5 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Elias — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 4 episodes
• Feature Extraction — 1× in this paper · explained in 3 episodes
• Few-Shot Learning — 1× in this paper · explained in 6 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 7 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Model Context Protocol — 1× in this paper
• Model Context Protocol (MCP) — 1× in this paper · explained in 8 episodes
• Modular Architecture — 1× in this paper · explained in 4 episodes
• Nadia — 1× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 6 episodes
• Prompt Injection Attacks — 1× in this paper · explained in 2 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Semantic Integrity — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• DeepSeek-V3 Technical Report
• Developers Are Victims Too : A Comprehensive Analysis of The VS Code Extension Ecosystem
• MalGuard: Towards Real-Time, Accurate, and Actionable Detection of Malicious Packages in PyPI Ecosystem
• Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions
• Detecting Malicious Source Code in PyPI Packages with LLMs: Does RAG Come in Handy?
• LLMs Caught in the Crossfire: Malware Requests and Jailbreak Challenges
• Unveiling A Hidden Risk: Exposing Educational but Malicious Repositories in GitHub
• DySec: A Machine Learning-based Dynamic Analysis for Detecting Malicious Packages in PyPI Ecosystem
• Agent Skills Enable a New Class of Realistic and Trivially Simple Prompt Injections
• YASA: Scalable Multi-Language Taint Analysis on the Unified AST at Ant Group

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