Agent-BOM: New framework for AI agent security auditing
Researchers have introduced Agent-BOM, a unified graph representation designed to facilitate security auditing of LLM-based agent systems, addressing the challenges of complex AI architectures.
What happened?
A research team has presented Agent-BOM, a new structural representation designed for the security auditing of autonomous LLM-based agent systems. The model aims to bridge the gap between low-level events and high-level intention in the execution of AI systems. Agent-BOM models an agent system as a hierarchical attributed directed graph that distinguishes static capability bases (models, tools, long-term memory) from dynamic semantic elements during runtime.
Key facts
| Publikationsdatum | 2026-05-23 |
|---|---|
| Kategori | cs.AI |
| Metod | Hierarkisk attribuerad riktad graf |
”LLM-based agentic systems are rapidly evolving to perform complex autonomous tasks through dynamic tool invocation, stateful memory management, and multi-agent collaboration.”
”Existing representation mechanisms, including static SBOMs and runtime logs, provide only fragmented evidence and fail to capture cognitive-state evolution, capability bindings, persistent memory contamination, and cascading risk propagation across interacting agents.”
”We propose Agent-BOM, a unified structural representation for agent security auditing. Agent-BOM models an agentic system as a hierarchical attributed directed graph that separates static capability bases, such as models, tools, and long-term memory, from dynamic runtime semantic”
Why it matters
The development of LLM-based agent systems creates challenges for security auditing, as their complex tasks involve dynamic tool usage, memory management, and multi-agent collaboration. Existing methods such as SBOMs (Software Bill of Materials) and execution logs provide fragmented information and fail to capture cognitive state evolution or risk propagation. Agent-BOM is proposed as a solution to facilitate a deeper understanding of these systems' security aspects.
Who is affected?
Researchers and developers of LLM-based agent systems are directly affected. Furthermore, companies and organisations implementing or planning to implement such systems are concerned, as proper security auditing is vital for reliability and compliance. End-users are also potentially affected through improved system security.
What else you should know
The research presented is a technical publication describing a new approach to improving the traceability and auditing of AI agent behaviour, which is of great importance for future regulations in the AI field.
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