语言防火墙：多智能体系统路由中的几何防御方法

Computer Science > Artificial Intelligence

arXiv:2606.30555v1 (cs) [Submitted on 29 Jun 2026]

Title:Linguistic Firewall: Geometry as Defense in Multi-Agent Systems Routing

Authors:Dvir Alsheich, Adar Peleg, Ben Hagag, Rom Himelstein, Amit Levi, Avi Mendelson View a PDF of the paper titled Linguistic Firewall: Geometry as Defense in Multi-Agent Systems Routing, by Dvir Alsheich and 5 other authors View PDF HTML (experimental)

Abstract:The rapid integration of Large Language Models (LLMs) has driven the evolution of Multi-Agent Systems (MAS), where specialized agents collaborate to execute complex workflows. Effective orchestration in these environments requires robust routing mechanisms to efficiently allocate tasks to the most suitable agent. However, existing routers fundamentally rely on unverified proxies, ranging from textual self-descriptions to static surrogate representations, to gauge an agent's competence. This reliance on non-empirical data creates a critical gap between an agent's projected profile and its actual operational capabilities, introducing severe security vulnerabilities. Malicious agents can easily misrepresent their proficiencies or harbor covert backdoors that evade both standard external analysis and static representation-learning techniques. In this work, we introduce ANTAP (Automatic Non-Textual Agent Picker), an evaluation-driven routing architecture that discards indirect proxies in favor of active capability testing. By dynamically querying agents to ascertain their true competencies empirically, ANTAP distills performance into fixed behavioral operators within a shared semantic space. At inference time, routing is performed via a purely non-textual algebraic projection, establishing a "linguistic firewall" that renders metadata-based attacks inexpressible. In our experiments, ANTAP achieves near-zero ASR against description-based injection attacks, compared to 67.3\% and above for the description-based router baseline. Against adaptive embedding attacks, ANTAP achieves substantially lower ASR than the embedding-based baseline, with a 20\% reduction, while remaining resilient to description manipulation by design.

Comments:	8 pages (9 more for appendix), 3 figures. Published at the Second Workshop on Agents in the Wild: Safety, Security, and Beyond (AIWILD) at ICML 2026
Subjects:	Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as:	arXiv:2606.30555 [cs.AI]
	(or arXiv:2606.30555v1 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2606.30555 Focus to learn more arXiv-issued DOI via DataCite

Submission history

From: Dvir Alsheich [view email]
[v1] Mon, 29 Jun 2026 16:51:06 UTC (230 KB)
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