Emerging Threat Vectors: How Malicious Actors Exploit LLMs to Undermine Border Security

The rapid proliferation of Large Language Models (LLMs) has democratized access to advanced generative capabilities while raising urgent concerns about misuse in sensitive security domains. Border security, in particular, represents a high-risk environment where malicious actors may exploit LLMs for document forgery, synthetic identity creation, logistics planning, or disinformation campaigns. Existing studies often highlight such risks in theory, yet few provide systematic empirical evidence of how state-of-the-art LLMs can be exploited. This paper introduces the Silent Adversary Framework (SAF), a structured pipeline that models the sequential stages by which obfuscated prompts can covertly bypass safeguards. We evaluate ten high-risk scenarios using five leading models—GPT-4o, Claude 3.7 Sonnet, Gemini 2.5 Flash, Grok 3, and Runway Gen-2—and assess outputs through three standardized metrics: Bypass Success Rate (BSR), Output Realism Score (ORS), and Operational Risk Level (ORL). Results reveal that, while all models exhibited some susceptibility, vulnerabilities were heterogeneous. Claude showed greater resistance in chemistry-related prompts, whereas GPT-4o and Gemini generated highly realistic outputs in identity fraud and logistics optimization tasks. Document forgery attempts produced only partially successful templates that lacked critical security features. These findings highlight the uneven distribution of risks across models and domains. By combining a reproducible adversarial framework with empirical testing, this study advances the evidence base on LLM misuse and provides actionable insights for policymakers and border security agencies, underscoring the need for stronger safeguards and oversight in the deployment of generative AI.