BioShield: A Context-Aware Firewall for Securing Bio-LLMs

Bio-LLMs create dual-use risks: While Large Language Models trained on biological data accelerate legitimate research by making complex bioinformatics knowledge more accessible, they can also be exploited to generate harmful biological insights when users disguise malicious intent as legitimate research queries.

Existing safeguards are inadequate: Static prompt filtering and policy-based restrictions alone are insufficient to protect Bio-LLMs, particularly when these models are embedded within dynamic biological workflows and real-world application systems where context matters.

BioShield uses context-aware screening: The proposed firewall analyzes incoming queries for biological dual-use threats before they reach the model, using a domain-specific risk scoring mechanism designed to identify prompts that attempt to conceal malicious intent within seemingly benign research requests.

Multi-layered defense approach: BioShield combines pre-generation protection (blocking risky queries before processing) with post-generation verification (inspecting model outputs for actionable or weaponizable biological content), providing protection at multiple stages of the AI system.

Application-level protection is necessary: Rather than relying solely on model-level safeguards, securing Bio-LLMs requires application-layer firewalls that can understand the specific context of biological research workflows and implement targeted risk thresholds appropriate to the domain.