MOLOT System Card: Malicious Operational Logic Observation Transformer

About

MOLOT (Malicious Operational Logic Observation Transformer) is a static malicious-code detection system designed for SAST setup where package metadata, maintainer history, and dynamic execution traces may be unavailable or unreliable. The system represents source code as behavior sequences derived from static call graphs, includes an explanation stage that ranks suspicious behavior activities and maps them back to source-code locations. The approach is evaluated on Python and JavaScript packages from PyPI and npm, compared with opensource detection tools, and validated under product constraints including runtime, memory use, and false-positive rates observed in a real moderation workflow. We also release Open Malicious-Code Bench, a public benchmark for reproducible evaluation of malicious-package detection methods. The results show that static behavior-sequence modeling can provide accurate, explainable, and deployable malicious-code detection for modern DevSecOps workflows.

Daniil Lopatkin, Maksim Mitrofanov, Stanislav Rakovsky, Aleksandr Khalikov · 2026

Related benchmarks

Task	Dataset	Result
File-level Malicious Code Detection	Malicious Package Python, File-level (val)	Precision84.6	4
Malicious code detection	Public Benchmark Python, File-level	Precision84.9	4
Malicious code detection	Open Malicious-code Bench Public Subset Python (Package-level)	Precision89.6	4
Package-level evaluation	Python Full (val)	Precision92.4	4
File-level Malicious Code Detection	Malicious Package JavaScript, File-level (full val)	Precision90.8	3
Malicious code detection	Public Benchmark Subset JavaScript File-level	Precision83.8	3
Malicious code detection	Open Malicious-code Bench Public Subset JavaScript Package-level	Precision91.2	3
Package-level evaluation	JavaScript (val)	Precision96.4	3