SGTO-MAS: Secure Gorilla Troops Optimization for Multi-Agent LLM Systems

Computer Science > Cryptography and Security [Submitted on 6 Jun 2026] SGTO-MAS: Secure Gorilla Troops Optimization for Multi-Agent LLM Systems Saeid Jamshidi Multi-agent large language model (LLM) systems offer strong capabilities for complex reasoning and decision-making, yet coordination across agents introduces error propagation, security risks, and inefficient use of resources. Existing methods often rely on heuristic, static strategies and lack a principled mechanism for balancing performance, security, and computational cost. This paper formulates multi-agent LLM coordination as a constrained optimization problem and proposes a security-aware method for adaptive agent selection. The method integrates trust modeling, risk-aware evaluation, and collective intelligence within a unified optimization objective. To solve the problem efficiently, we use a swarm-intelligence strategy inspired by Gorilla Troops Optimization (GTO), enabling adaptive coordination under varying threat conditions. Controlled experiments across 500 independent runs demonstrate the effectiveness of the proposed method. The system achieves a stable average performance score of 0.5281, with high consensus (0.8764), controlled risk (0.3000), and compact agent subsets averaging 4.04 selected agents. The optimization process converges efficiently, with an average runtime of 24.09 seconds per run and low score variability (standard deviation = 0.0173). Robustness analysis indicates graceful degradation under perturbations, with performance drops limited to 2.5% under agent removal and 5.3% under consensus disruption. These results show that effective multi-agent coordination can be achieved through structured optimization that jointly manages performance, security, and efficiency. The proposed method provides a practical security-aware solution for coordinating multi-agent LLM systems in complex adversarial settings. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.07940 [cs.CR]   (or arXiv:2606.07940v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.07940 Focus to learn more Submission history From: Saeid Jamshidi [view email] [v1] Sat, 6 Jun 2026 02:00:36 UTC (7,091 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)