Does Runtime Topology Context Improve LLM-Generated Kubernetes Security Patches?
cs.CR, cs.AI
Submitted: 2026-07-28
Updated: 2026-08-31
Comments: Accepted at the Workshop on the Use of Large Language Models for Cybersecurity (LLMSec), co-located with ESORICS 2026
Code: https://github.com/dynatrace-research/vulncare
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- KubeIntellect: A Modular LLM-Orchestrated Agent Framework for End-to-End Kubernetes Management
- Red Teaming Program Repair Agents: When Correct Patches can Hide Vulnerabilities
- Graphene: Infrastructure Security Posture Analysis with AI-generated Attack Graphs
- GenKubeSec: LLM-Based Kubernetes Misconfiguration Detection, Localization, Reasoning, and Remediation
- How Safe Are AI-Generated Patches? A Large-scale Study on Security Risks in LLM and Agentic Automated Program Repair on SWE-bench
- KubeGuard: LLM-Assisted Kubernetes Hardening via Configuration Files and Runtime Logs Analysis
- MetaKube: An Experience-Aware LLM Framework for Kubernetes Failure Diagnosis
- Risk Assessment Graphs: Utilizing Attack Graphs for Risk Assessment
- A Systematic Study of LLM-Based Architectures for Automated Patching
- LLMSecConfig: An LLM-Based Approach for Fixing Software Container Misconfigurations
- MicroRemed: Benchmarking LLMs in Microservices Remediation
- AttacKG+:Boosting Attack Knowledge Graph Construction with Large Language Models
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