Autonomous Cyber Defense

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6 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 41

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SHIELDS: Automating OS Hardening with Iterative Multi-Agent Remediation
Jun 3, 2026Andrew Hamara, Dwight Horne, Aldehir Rojas +5Multi-Agent LLM Systems Autonomous Cyber Defense
1L3Harris Technologies. · 2Texas A&M University.
PocketAgents: A Manifest-Driven Library of Autonomous Defense Agents
May 20, 2026Sidnei Barbieri, Ágney Lopes Roth Ferraz, Lourenço Alves Pereira JúniorAutonomous Cyber Defense LLM Agent Security
Aeronautics Institute of Technology (ITA), São José dos Campos/SP, Brazil
GenAI-Driven Threat Detection with Microsoft Security Copilot
May 20, 2026Scott Freitas, Amir GharibAutonomous Cyber Defense Cybersecurity
Microsoft Security Research
Strategic commitments shape collective cybersecurity under AI inequality
May 10, 2026Adeela Bashir, Zia Ush Shamszaman, Zhao Song +2Autonomous Cyber Defense AI Governance
School of Computing, Engineering and Digital Technologies, Teesside University · Center for Digital Innovation, Teesside University · Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia +3
Stable Agentic Control: Tool-Mediated LLM Architecture for Autonomous Cyber Defense
May 4, 2026Kerri Prinos, Lilianne Brush, Cameron Denton +5Autonomous Cyber Defense Language Model-Based Control
Horizon3.ai San Francisco, CA
APIOT: Autonomous Vulnerability Management Across Bare-Metal Industrial OT Networks
May 4, 2026Adel ElZemity, Budi Arief, Shujun Li +6Autonomous Cyber Defense Cybersecurity
University of Kent, Canterbury, United Kingdom · University of Bristol, United Kingdom
CyberAId: AI-Driven Cybersecurity for Financial Service Providers
May 3, 2026George Fatouros, Georgios Makridis, John Soldatos +18Multi-Agent LLM Systems Autonomous Cyber Defense
Innov-Acts, Cyprus · University of Piraeus, Greece · Unparallel Innovation, Portugal +7
Adaptive Defense Orchestration for RAG: A Sentinel-Strategist Architecture against Multi-Vector Attacks
Apr 22, 2026Pranav Pallerla, Wilson Naik Bhukya, Bharath Vemula +1Autonomous Cyber Defense RAG Security
School of Computer and Information Sciences University of Hyderabad Hyderabad, Telangana, India · Purdue University West Lafayette, USA
AgentSOC: A Multi-Layer Agentic AI Framework for Security Operations Automation
Apr 22, 2026Joyjit Roy, Samaresh Kumar SinghAutonomous Cyber Defense Cybersecurity
Austin, Texas, USA · Leander, Texas, USA
CSLE: A Reinforcement Learning Platform for Autonomous Security Management
Apr 16, 2026Kim HammarReinforcement Learning Autonomous Cyber Defense
Division of Network and Systems Engineering, KTH Royal Institute of Technology, Sweden
NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions
Apr 10, 2026Igor JankowskiAutonomous Cyber Defense Cybersecurity

Jun 3, 2026, cs.CRJ/K move · Enter open · S save

SHIELDS: Automating OS Hardening with Iterative Multi-Agent Remediation

Andrew Hamara, Dwight Horne, Aldehir Rojas, Timothy Kurniawan+4

1L3Harris Technologies. · 2Texas A&M University.

Multi-Agent LLM Systems Autonomous Cyber Defense Cybersecurity

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Security misconfigurations remain a leading cause of OS-level compromise, and manually keeping systems compliant with standards like Defense Information Systems Agency (DISA) Security Technical Implementation Guides (STIGs) is a tedious and expensive process. Existing compliance automation tools can reduce some of this burden, but they depend on static, pre-written corrective actions. In this paper, we introduce SHIELDS, a multi-agent system that uses large language models (LLMs) to approach OS hardening as an iterative, feedback-driven process. Instead of applying fixed remediations, SHIELDS continuously proposes fixes and refines them based on feedback from target system execution and validation scans. We evaluate the system across multiple virtual machine configurations using six contemporary LLMs ranging from 20B to 400B parameters, and find that SHIELDS successfully remediates up to 73% of scan findings. Our results also suggest that success in this setting depends less on model size (parameter count) than on effective tool use and information gathering, paving a practical path toward reducing the burden of security compliance in environments where compute is limited or security and privacy needs drive local model use.

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