Author

Roshan Sood

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Preprint Aug 2026

COPA: Continual Preference Optimization for Adaptive Prompt Injection Defense

LLMs remain vulnerable to prompt injection attacks, where adversarial instructions embedded in user inputs or external content manipulate model behavior and bypass safeguards. Existing defenses are predominantly static, relying on fixed alignment objectives or attack-specific filtering mechanisms that require redesign...

Roshan Sood, Onat Gungor, T. Rosing · 2 citations

Sep 2026

EAGER: Edge-Aligned LLM Defense for Robust Cybersecurity Question Answering

Large Language Models (LLMs) are highly effective for cybersecurity question answering (QA) but are difficult to deploy on edge devices due to their size. Quantization reduces memory and compute requirements but often degrades accuracy and increases vulnerability to adversarial attacks. We present EAGER, an edge-aligne...

Onat Gungor, Roshan Sood, Jia-Sheng Zhou et al. · International Symposium on N... · 0 citations