# HW-V2W-Map: Hardware Vulnerability to Weakness Mapping Framework for Root Cause Analysis with GPT-assisted Mitigation Suggestion

arXiv: https://arxiv.org/abs/2312.13530 · HTML: https://mlsanity.com/paper/2312.13530/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 65 references that are in the map.
- First posted on arXiv 21 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Examining Zero-Shot Vulnerability Repair with Large Language Models](https://mlsanity.com/paper/2112.02125/) (2021)
- [14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon](https://mlsanity.com/paper/2306.06283/) (2023)
- [Spectre Attacks: Exploiting Speculative Execution](https://mlsanity.com/paper/1801.01203/) (2018)
- [Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning](https://mlsanity.com/paper/2212.01117/) (2022)
- [What are the attackers doing now? Automating cyber threat intelligence extraction from text on pace with the changing threat landscape: A survey](https://mlsanity.com/paper/2109.06808/) (2021)
- [Security Vulnerability Detection Using Deep Learning Natural Language Processing](https://mlsanity.com/paper/2105.02388/) (2021)
- [Flush+Flush: A Fast and Stealthy Cache Attack](https://mlsanity.com/paper/1511.04594/) (2015)
- [CVE based classification of vulnerable IoT systems](https://mlsanity.com/paper/2006.16640/) (2020)
- [The Effect of IoT New Features on Security and Privacy: New Threats, Existing Solutions, and Challenges Yet to Be Solved](https://mlsanity.com/paper/1802.03110/) (2018)

## Built on by (most cited first, 8 of 8)

- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [A Survey of Circuit Foundation Model: Foundation AI Models for VLSI Circuit Design and EDA](https://mlsanity.com/paper/2504.03711/) (2025)
- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [Can Agents Secure Hardware? Evaluating Agentic LLM-Driven Obfuscation for IP Protection](https://mlsanity.com/paper/2604.13298/) (2026)
- [LLM-HyPZ: Hardware Vulnerability Discovery using an LLM-Assisted Hybrid Platform for Zero-Shot Knowledge Extraction and Refinement](https://mlsanity.com/paper/2509.00647/) (2025)
- [RAG-PRISM: A Personalized, Rapid, and Immersive Skill Mastery Framework with Adaptive Retrieval-Augmented Tutoring](https://mlsanity.com/paper/2509.00646/) (2025)
- [FaRAccel: FPGA-Accelerated Defense Architecture for Efficient Bit-Flip Attack Resilience in Transformer Models](https://mlsanity.com/paper/2510.24985/) (2025)
- [RuleForge: Automated Generation and Validation for Web Vulnerability Detection at Scale](https://mlsanity.com/paper/2604.01977/) (2026)

## Abstract (arXiv)

The escalating complexity of modern computing frameworks has resulted in a surge in the cybersecurity vulnerabilities reported to the National Vulnerability Database (NVD) by practitioners. Despite the fact that the stature of NVD is one of the most significant databases for the latest insights into vulnerabilities, extracting meaningful trends from such a large amount of unstructured data is still challenging without the application of suitable technological methodologies. Previous efforts have mostly concentrated on software vulnerabilities; however, a holistic strategy incorporates approaches for mitigating vulnerabilities, score prediction, and a knowledge-generating system that may extract relevant insights from the Common Weakness Enumeration (CWE) and Common Vulnerability Exchange (CVE) databases is notably absent. As the number of hardware attacks on Internet of Things (IoT) devices continues to rapidly increase, we present the Hardware Vulnerability to Weakness Mapping (HW-V2W-Map) Framework, which is a Machine Learning (ML) framework focusing on hardware vulnerabilities and IoT security. The architecture that we have proposed incorporates an Ontology-driven Storytelling framework, which automates the process of updating the ontology in order to recognize patterns and evolution of vulnerabilities over time and provides approaches for mitigating the vulnerabilities. The repercussions of vulnerabilities can be mitigated as a result of this, and conversely, future exposures can be predicted and prevented. Furthermore, our proposed framework utilized Generative Pre-trained Transformer (GPT) Large Language Models (LLMs) to provide mitigation suggestions.