# Graphene: Infrastructure Security Posture Analysis with AI-generated Attack Graphs

arXiv: https://arxiv.org/abs/2312.13119 · HTML: https://mlsanity.com/paper/2312.13119/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 66 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Automatic Labeling for Entity Extraction in Cyber Security](https://mlsanity.com/paper/1308.4941/) (2013)
- [Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Models](https://mlsanity.com/paper/2312.09601/) (2023)
- [Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering](https://mlsanity.com/paper/2303.16416/) (2023)
- [Edge Security: Challenges and Issues](https://mlsanity.com/paper/2206.07164/) (2022)

## Built on by (most cited first, 3 of 3)

- [Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2402.10350/) (2024)
- [Does Runtime Topology Context Improve LLM-Generated Kubernetes Security Patches?](https://mlsanity.com/paper/2607.25995/) (2026)
- [Backdoor Learning in Language Models and Vision-Language Models](https://mlsanity.com/paper/2608.18095/) (2026)

## Abstract (arXiv)

The rampant occurrence of cybersecurity breaches imposes substantial limitations on the progress of network infrastructures, leading to compromised data, financial losses, potential harm to individuals, and disruptions in essential services. The current security landscape demands the urgent development of a holistic security assessment solution that encompasses vulnerability analysis and investigates the potential exploitation of these vulnerabilities as attack paths. In this paper, we propose Graphene, an advanced system designed to provide a detailed analysis of the security posture of computing infrastructures. Using user-provided information, such as device details and software versions, Graphene performs a comprehensive security assessment. This assessment includes identifying associated vulnerabilities and constructing potential attack graphs that adversaries can exploit. Furthermore, Graphene evaluates the exploitability of these attack paths and quantifies the overall security posture through a scoring mechanism. The system takes a holistic approach by analyzing security layers encompassing hardware, system, network, and cryptography. Furthermore, Graphene delves into the interconnections between these layers, exploring how vulnerabilities in one layer can be leveraged to exploit vulnerabilities in others. In this paper, we present the end-to-end pipeline implemented in Graphene, showcasing the systematic approach adopted for conducting this thorough security analysis.