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Related Experiment Videos

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

Petar Radanliev1,2, Omar Santos3, Carsten Maple2,4

  • 1Department of Computer Sciences, University of Oxford, Oxford, United Kingdom.

Frontiers in Artificial Intelligence
|June 1, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for Agentic AI supply chain security. It combines Software Bills of Materials (SBOM) and Artificial Intelligence Bills of Materials (AIBOM) with runtime telemetry for accurate exploitability assessment.

Keywords:
AI supply chain securityCSAF-VEX automationagentic AIartificial intelligence bill of materialsexecution-bound exploitabilityruntime telemetry

Related Experiment Videos

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Agentic AI systems present unique security challenges due to complex integrations not visible in static Software Bills of Materials (SBOMs).
  • Existing security advisory workflows (CSAF/VEX) lack runtime validation, relying on static component-CVE correlation.
  • Artificial Intelligence Bills of Materials (AIBOM) enhance transparency for AI-specific components but require integration with runtime data.

Purpose of the Study:

  • To develop a protocol-driven framework for dynamic exploitability assessment of Agentic AI supply chains.
  • To integrate static SBOM and AIBOM data with runtime telemetry for comprehensive security analysis.
  • To improve the accuracy and reduce false positives in security advisories (CSAF/VEX).

Main Methods:

  • A protocol-driven framework was developed, binding SBOM and AIBOM artifacts to deterministic environment capture and structured runtime telemetry.
  • Exploitability was computed using declared artifacts, observed activation conditions, and enforced execution policies.
  • CSAF/VEX advisories were generated from combined static and runtime evidence, validated through deterministic replay, and evaluated on synthetic Agentic AI workloads.

Main Results:

  • The framework achieved a high F1-score of 0.93 (precision 0.96, recall 0.92) in exploitability assessment.
  • False positives were reduced by up to 42% compared to static SBOM-CVE matching.
  • Exploitability outcomes were altered in 31% of AI-specific artifact cases by incorporating AIBOM data and runtime validation.

Conclusions:

  • Binding AIBOM artifacts to runtime telemetry transforms security advisory generation from static disclosure to execution-grounded exploitability assessment.
  • The proposed framework enhances the security and reliability of Agentic AI supply chains.
  • Deterministic replay ensures the reproducibility and trustworthiness of generated security advisories.