[HN Gopher] JavelinGuard: Low-Cost Transformer Architectures for...
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       JavelinGuard: Low-Cost Transformer Architectures for LLM Security
        
       We present JavelinGuard, a suite of low-cost, high-performance
       model architectures designed for detecting malicious intent in
       Large Language Model (LLM) interactions, optimized specifically for
       production deployment.  Recent advances in transformer
       architectures, including compact BERT(Devlin et al. 2019) variants
       (e.g., ModernBERT (Warner et al. 2024)), allow us to build highly
       accurate classifiers with as few as approximately 400M parameters
       that achieve rapid inference speeds even on standard CPU hardware.
       We systematically explore five progressively sophisticated
       transformer-based architectures: Sharanga (baseline transformer
       classifier), Mahendra (enhanced attention-weighted pooling with
       deeper heads), Vaishnava and Ashwina (hybrid neural ensemble
       architectures), and Raudra (an advanced multi-task framework with
       specialized loss functions).  Our models are rigorously benchmarked
       across nine diverse adversarial datasets, including popular sets
       like the NotInject series, BIPIA, Garak, ImprovedLLM, ToxicChat,
       WildGuard, and our newly introduced JavelinBench, specifically
       crafted to test generalization on challenging borderline and hard-
       negative cases.  Additionally, we compare our architectures against
       leading open-source guardrail models as well as large decoder-only
       LLMs such as gpt-4o, demonstrating superior cost-performance trade-
       offs in terms of accuracy, and latency. Our findings reveal that
       while Raudra's multi-task design offers the most robust performance
       overall, each architecture presents unique trade-offs in speed,
       interpretability, and resource requirements, guiding practitioners
       in selecting the optimal balance of complexity and efficiency for
       real-world LLM security applications.
        
       Author : sharathr
       Score  : 25 points
       Date   : 2025-06-10 15:59 UTC (7 hours ago)
        
 (HTM) web link (arxiv.org)
 (TXT) w3m dump (arxiv.org)
        
       | mjburgess wrote:
       | As far as I can tell this is an ad disguised as an academic
       | paper.
        
       | mountainriver wrote:
       | Great work by Sharath and team!
        
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       (page generated 2025-06-10 23:02 UTC)