mlsanityData to 7 Oct 2026

RACONTEUR: A Knowledgeable, Insightful, and Portable LLM-Powered Shell Command Explainer

arXiv 2409.02074 ↗First posted 3 Sep 2024cs.CR cs.HC cs.LGcited by 9

Not on the map. Why?

Abstract (arXiv)

Malicious shell commands are linchpins to many cyber-attacks, but may not be easy to understand by security analysts due to complicated and often disguised code structures. Advances in large language models (LLMs) have unlocked the possibility of generating understandable explanations for shell commands. However, existing general-purpose LLMs suffer from a lack of expert knowledge and a tendency to hallucinate in the task of shell command explanation. In this paper, we present Raconteur, a knowledgeable, expressive and portable shell command explainer powered by LLM. Raconteur is infused with professional knowledge to provide comprehensive explanations on shell commands, including not only what the command does (i.e., behavior) but also why the command does it (i.e., purpose). To shed light on the high-level intent of the command, we also translate the natural-language-based explanation into standard technique & tactic defined by MITRE ATT&CK, the worldwide knowledge base of cybersecurity. To enable Raconteur to explain unseen private commands, we further develop a documentation retriever to obtain relevant information from complementary documentations to assist the explanation process. We have created a large-scale dataset for training and conducted extensive experiments to evaluate the capability of Raconteur in shell command explanation. The experiments verify that Raconteur is able to provide high-quality explanations and in-depth insight of the intent of the command.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 43 of its 62 references that are in the map. First posted on arXiv 3 Sep 2024.

Builds on

43 of 62 references.

References · 43

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  5. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  6. 2020Scaling Laws for Neural Language Modelscited by 6,411
  7. 2014CIDEr: Consensus-based Image Description Evaluationcited by 2,027
  8. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  9. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,413
  10. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,286
  11. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
  12. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,026
31 more
  1. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  2. 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 890
  3. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 837
  4. 2023StarCoder: may the source be with you!cited by 790
  5. 2021CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generationcited by 732
  6. 2022MTEB: Massive Text Embedding Benchmarkcited by 623
  7. 2020GraphCodeBERT: Pre-training Code Representations with Data Flowcited by 600
  8. 2021Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Modelscited by 462
  9. 2021Large Dual Encoders Are Generalizable Retrieverscited by 454
  10. 2022The Stack: 3 TB of permissively licensed source codecited by 354
  11. 2023CodeT5+: Open Code Large Language Models for Code Understanding and Generationcited by 327
  12. 2022SGPT: GPT Sentence Embeddings for Semantic Searchcited by 204
  13. 2023Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulnesscited by 130
  14. 2022Automatic Generation of Programming Exercises and Code Explanations using Large Language Modelscited by 124
  15. 2023WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferencescited by 100
  16. 2018Improving Automatic Source Code Summarization via Deep Reinforcement Learningcited by 99
  17. 2018NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating Systemcited by 97
  18. 2016A Convolutional Attention Network for Extreme Summarization of Source Codecited by 90
  19. 2022Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Bookcited by 84
  20. 2020PyMT5: multi-mode translation of natural language and Python code with transformerscited by 80
  21. 2023Comparing Code Explanations Created by Students and Large Language Modelscited by 63
  22. 2019Code Generation as a Dual Task of Code Summarizationcited by 62
  23. 2018Structured Neural Summarizationcited by 53
  24. 2023Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreakscited by 45
  25. 2023Goal-Oriented Prompt Attack and Safety Evaluation for LLMscited by 23
  26. 2021CoCoSum: Contextual Code Summarization with Multi-Relational Graph Neural Networkcited by 22
  27. 2020TranS^3: A Transformer-based Framework for Unifying Code Summarization and Code Searchcited by 11
  28. 2022A new hope for network model generalizationcited by 11
  29. 2022Robosourcing Educational Resources -- Leveraging Large Language Models for Learnersourcingcited by 9
  30. 2022GypSum: Learning Hybrid Representations for Code Summarizationcited by 5
  31. 2021Identifying botnet IP address clusters using natural language processing techniques on honeypot command logscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 4.

9 papers.