arXiv:2607.02873v1 Announce Type: cross Abstract: Large language model agents driving security tool suites over the Model Context Protocol are increasingly common. Yet the factors that bound their capability remain poorly characterized: how much depends on the model versus the client that drives it, whether constraining the agent to the orchestrator's own tools helps, and where capability is limited by reasoning rather than by missing tools. Using HexStrikeAI, an open-source orchestrator that exposes 150+ tools, as a testbed, we follow a methodology that evaluates the system, diagnoses its fai
The proliferation of LLM agents in critical applications like cybersecurity necessitates a deeper understanding of their capabilities and limitations in orchestrating security tools.
This research provides crucial insights into the performance boundaries of LLM-driven security tools, informing the development of more robust AI agents and highlighting areas needing human oversight.
Our understanding of what limits LLM agent performance for security tasks shifts from solely model-centric views to include the tooling, orchestration, and intrinsic reasoning abilities.
- · Cybersecurity firms adopting AI agents
- · Developers of LLM orchestration platforms
- · Organizations with advanced threat detection needs
- · Cybersecurity firms relying solely on traditional methods
- · Developers of unoptimized LLM agents
- · Adversaries vulnerable to sophisticated AI-driven defenses
Improved design and deployment of AI-powered cybersecurity tools become possible by identifying specific failure modes.
Increased trust and adoption of autonomous AI agents in sensitive cybersecurity roles may emerge as their limitations are better characterized.
The definition of 'human in the loop' for AI security operations could evolve to focus on areas where LLM reasoning is inherently limited, not just tool access.
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Read at arXiv cs.AI