Agentic AI Shielding Software Supply Chains from Within
📝 Original Paper Info
- Title: Agentic AI for Autonomous Defense in Software Supply Chain Security Beyond Provenance to Vulnerability Mitigation- ArXiv ID: 2512.23480
- Date: 2025-12-29
- Authors: Toqeer Ali Syed, Mohammad Riyaz Belgaum, Salman Jan, Asadullah Abdullah Khan, Saad Said Alqahtani
📝 Abstract
The software supply chain attacks are becoming more and more focused on trusted development and delivery procedures, so the conventional post-build integrity mechanisms cannot be used anymore. The available frameworks like SLSA, SBOM and in toto are majorly used to offer provenance and traceability but do not have the capabilities of actively identifying and removing vulnerabilities in software production. The current paper includes an example of agentic artificial intelligence (AI) based on autonomous software supply chain security that combines large language model (LLM)-based reasoning, reinforcement learning (RL), and multi-agent coordination. The suggested system utilizes specialized security agents coordinated with the help of LangChain and LangGraph, communicates with actual CI/CD environments with the Model Context Protocol (MCP), and documents all the observations and actions in a blockchain security ledger to ensure integrity and auditing. Reinforcement learning can be used to achieve adaptive mitigation strategies that consider the balance between security effectiveness and the operational overhead, and LLMs can be used to achieve semantic vulnerability analysis, as well as explainable decisions. This framework is tested based on simulated pipelines, as well as, actual world CI/CD integrations on GitHub Actions and Jenkins, including injection attacks, insecure deserialization, access control violations, and configuration errors. Experimental outcomes indicate better detection accuracy, shorter mitigation latency and reasonable build-time overhead than rule-based, provenance only and RL only baselines. These results show that agentic AI can facilitate the transition to self defending, proactive software supply chains rather than reactive verification ones.💡 Summary & Analysis
1. **Importance of Climate Change**: The study highlights that climate change is a global issue and significantly affects food production. 2. **Data and Modeling**: Utilizing extensive data from 1960 to 2020 and applying advanced econometric models to predict future trends in food security. 3. **Need for Policy Intervention**: Emphasizes the urgency of policy actions, as without them, food security could severely decline.📄 Full Paper Content (ArXiv Source)
📊 논문 시각자료 (Figures)
