# AgenticVM: Agentic AI for Adaptive Software Vulnerability Management arXiv: https://arxiv.org/abs/2605.01739 ยท HTML: https://mlsanity.com/paper/2605.01739/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. - Builds on 31 of its 77 references that are in the map. - First posted on arXiv 3 May 2026. ## Placed in - LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/) ## Builds on (most cited first, 30 of 31) - [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023) - [Automated Vulnerability Detection in Source Code Using Deep Representation Learning](https://mlsanity.com/paper/1807.04320/) (2018) - [Large Language Models for Cyber Security: A Systematic Literature Review](https://mlsanity.com/paper/2405.04760/) (2024) - [Large Language Model for Vulnerability Detection and Repair: Literature Review and the Road Ahead](https://mlsanity.com/paper/2404.02525/) (2024) - ["I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data](https://mlsanity.com/paper/2212.05856/) (2022) - [Securing Agentic AI: A Comprehensive Threat Model and Mitigation Framework for Generative AI Agents](https://mlsanity.com/paper/2504.19956/) (2025) - [PyRIT: A Framework for Security Risk Identification and Red Teaming in Generative AI System](https://mlsanity.com/paper/2410.02828/) (2024) - [A Case Study of LLM for Automated Vulnerability Repair: Assessing Impact of Reasoning and Patch Validation Feedback](https://mlsanity.com/paper/2405.15690/) (2024) - [VUDENC: Vulnerability Detection with Deep Learning on a Natural Codebase for Python](https://mlsanity.com/paper/2201.08441/) (2022) - [ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models](https://mlsanity.com/paper/2412.04756/) (2024) - [RegimeFolio: A Regime Aware ML System for Sectoral Portfolio Optimization in Dynamic Markets](https://mlsanity.com/paper/2510.14986/) (2025) - [A Review on C3I Systems' Security: Vulnerabilities, Attacks, and Countermeasures](https://mlsanity.com/paper/2104.11906/) (2021) - [Smart HPA: A Resource-Efficient Horizontal Pod Auto-scaler for Microservice Architectures](https://mlsanity.com/paper/2403.07909/) (2024) - [Towards Deep Learning Enabled Cybersecurity Risk Assessment for Microservice Architectures](https://mlsanity.com/paper/2403.15169/) (2024) - [Robust Partial Least Squares Using Low Rank and Sparse Decomposition](https://mlsanity.com/paper/2407.06936/) (2024) - [A Survey on Immersive Cyber Situational Awareness Systems](https://mlsanity.com/paper/2408.07456/) (2024) - [Machine Learning Driven Smishing Detection Framework for Mobile Security](https://mlsanity.com/paper/2412.09641/) (2024) - [The Future of AI: Exploring the Potential of Large Concept Models](https://mlsanity.com/paper/2501.05487/) (2025) - [Agentic AI: Autonomy, Accountability, and the Algorithmic Society](https://mlsanity.com/paper/2502.00289/) (2025) - [Co-Evolutionary Defence of Active Directory Attack Graphs via GNN-Approximated Dynamic Programming](https://mlsanity.com/paper/2505.11710/) (2025) - [Resilient Auto-Scaling of Microservice Architectures with Efficient Resource Management](https://mlsanity.com/paper/2506.05693/) (2025) - [On the Use of Fine-grained Vulnerable Code Statements for Software Vulnerability Assessment Models](https://mlsanity.com/paper/2203.08417/) (2022) - [Microservice Vulnerability Analysis: A Literature Review with Empirical Insights](https://mlsanity.com/paper/2408.03960/) (2024) - [SCALAR: Self-Calibrating Adaptive Latent Attention Representation Learning](https://mlsanity.com/paper/2510.16474/) (2025) - [Australian Bushfire Intelligence with AI-Driven Environmental Analytics](https://mlsanity.com/paper/2601.06105/) (2026) - [Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.04442/) (2026) - [Automatically Assessing Vulnerabilities Discovered by Compositional Analysis](https://mlsanity.com/paper/1807.09160/) (2018) - [Dynamic Neural Control Flow Execution: An Agent-Based Deep Equilibrium Approach for Binary Vulnerability Detection](https://mlsanity.com/paper/2404.08562/) (2024) - [Runtime Software Patching: Taxonomy, Survey and Future Directions](https://mlsanity.com/paper/2203.12132/) (2022) - [LibVulnWatch: A Deep Assessment Agent System and Leaderboard for Uncovering Hidden Vulnerabilities in Open-Source AI Libraries](https://mlsanity.com/paper/2505.08842/) (2025) ## Built on by (most cited first, 2 of 2) - [When the Judge Should Not Decide: Evidence-Locked, Non-Compensatory Selection Bounds LLM-Judge Failure in Reasoning Pipelines](https://mlsanity.com/paper/2608.07813/) (2026) - [VACS: Value-Aligned Compositional Shielding for Multi-Agent Reasoning](https://mlsanity.com/paper/2609.26135/) (2026) ## Abstract (arXiv) As software systems grow in scale and complexity, vulnerability management is increasingly strained by high alert volumes, fragmented toolchains, and manual triage processes. We introduce AgenticVM, a multi-agent framework that integrates large language models with security tools to automate vulnerability detection, assessment, prioritization, and reporting. AgenticVM combines rule-based processing, a BERT-based CVSS prediction module, and specialised LLM-driven agents, leveraging data from sources such as the National Vulnerability Database and the European Union Vulnerability Database. Across multiple evaluation scenarios, AgenticVM reduces raw scanner outputs into compact, actionable queues, achieving up to 98% alert reduction (e.g., from 3,983 findings to 82 high-priority items), while predicting missing CVSS attributes with 89.3% accuracy. These results demonstrate improved prioritisation efficiency and reduced analyst workload without compromising risk visibility. Beyond performance, the framework provides practical design insights into agent decomposition, tool-LLM integration, and human-in-the-loop governance for real-world deployment.