Agentic AI for Autonomous Defense in Software Supply Chain Security: Beyond Provenance to Vulnerability Mitigation

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

This paper introduces an agentic AI-based framework for autonomous software supply chain defense, shifting from reactive verification to proactive intervention. By leveraging large language models, reinforcement learning, and multi-agent coordination, the framework aims to identify and mitigate vulnerabilities in real-time, creating a self-defending software ecosystem.

Key Insights

• •The framework employs a multi-layered, multi-agent system orchestrated by LangChain and LangGraph to monitor and secure each stage of the software supply chain lifecycle, including source code management, dependency resolution, and deployment.
• •Large language models (LLMs) are utilized for semantic vulnerability analysis, enabling contextual detection beyond simple syntactic pattern matching through chain-of-thought prompting and retrieval-augmented generation (RAG).
• •Reinforcement learning (RL) is implemented to enable adaptive mitigation strategies, balancing security effectiveness with operational overhead by optimizing a reward function that considers factors like attack mitigation, false positives, build time, and developer acceptance.
• •The Model Context Protocol (MCP) facilitates real-world interaction by providing standardized interfaces with external systems like GitHub Actions and Jenkins, allowing the agents to operate across heterogeneous toolchains.
• •A permissioned blockchain is used to create a security ledger, ensuring integrity, non-repudiation, and auditability of agent actions throughout the software supply chain.

Practical Implications

• •The agentic AI framework can be applied to enhance the security of CI/CD pipelines by autonomously detecting and mitigating vulnerabilities such as injection attacks, insecure deserialization, and configuration errors.
• •The research suggests the potential for developing more robust and resilient software ecosystems by transitioning from reactive security measures to proactive, AI-driven defense mechanisms.
• •Future research could focus on improving the robustness of LLMs against adversarial attacks, developing adaptive mechanisms for evolving threat landscapes, and extending the framework to other security domains.
• •The framework's complexity highlights the need for further investigation into implementation and maintenance challenges, especially in diverse and complex software development environments.

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