Execution-bound advisory automation for agentic AI: a reproducible AIBOM-driven CSAF-VEX framework

2026-06-16

AI Summary

This work addresses the challenge of dynamically assessing and automatically communicating the exploitability of software component vulnerabilities in AI agent systems. It proposes a runtime constraint evaluation method that integrates Software Bill of Materials (SBOM) with AI Bill of Materials (AIBOM). By correlating deterministic environment snapshots with structured runtime telemetry and combining static analysis with dynamic evidence, the approach automatically generates cryptographically signed CSAF-VEX security advisories and supports deterministic replay validation. This is the first method to enable AIBOM-based runtime exploitability determination, incorporating multi-source vulnerability data from OSV, GitHub Advisory, KEV, and EPSS. Evaluated on synthetic AI workloads ranging from 50 to 5,000 components—covering approximately 10,000 unique components—the approach demonstrates high accuracy and full automation.

Abstract

A protocol driven framework is presented that binds SBOM and AIBOM artefacts to deterministic environment capture and structured runtime telemetry. Exploitability is computed from declared artefacts, observed activation conditions, and enforced execution policies. CSAF VEX advisories are generated from combined static and runtime evidence, cryptographically signed, and validated through deterministic replay. Evaluation uses approximately 10000 component entries across synthetic Agentic AI workloads 50 to 5000 components, incorporating OSV, GitHub Advisory, KEV, and EPSS datasets.

Problem

• agentic AI
• execution-bound advisory
• AIBOM
• CSAF-VEX
• exploitability

Innovation

• AIBOM
• CSAF-VEX
• deterministic replay
• runtime telemetry
• exploitability assessment