# EAGER: Edge-Aligned LLM Defense for Robust, Efficient, and Accurate Cybersecurity Question Answering

arXiv: https://arxiv.org/abs/2511.19523 · HTML: https://mlsanity.com/paper/2511.19523/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Safety alignment and jailbreak attacks, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 19 of its 43 references that are in the map.
- First posted on arXiv 24 Nov 2025.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Safety alignment and jailbreak attacks](https://mlsanity.com/alignment/safety-alignment-and-jailbreak-attacks/)

## Builds on (most cited first, 19 of 19)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [StruQ: Defending Against Prompt Injection with Structured Queries](https://mlsanity.com/paper/2402.06363/) (2024)
- [SecAlign: Defending Against Prompt Injection with Preference Optimization](https://mlsanity.com/paper/2410.05451/) (2024)
- [Instructional Segment Embedding: Improving LLM Safety with Instruction Hierarchy](https://mlsanity.com/paper/2410.09102/) (2024)
- [Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression](https://mlsanity.com/paper/2403.15447/) (2024)
- [Defense Against Prompt Injection Attack by Leveraging Attack Techniques](https://mlsanity.com/paper/2411.00459/) (2024)
- [SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security](https://mlsanity.com/paper/2312.15838/) (2023)
- [Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge](https://mlsanity.com/paper/2312.05693/) (2023)
- [Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report](https://mlsanity.com/paper/2504.21039/) (2025)
- [Fine-Tuning, Quantization, and LLMs: Navigating Unintended Outcomes](https://mlsanity.com/paper/2404.04392/) (2024)
- [Investigating the Impact of Quantization on Adversarial Robustness](https://mlsanity.com/paper/2404.05639/) (2024)
- [Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment](https://mlsanity.com/paper/2407.03051/) (2024)
- [A RAG-Based Question-Answering Solution for Cyber-Attack Investigation and Attribution](https://mlsanity.com/paper/2408.06272/) (2024)
- [Edge Intelligence Optimization for Large Language Model Inference with Batching and Quantization](https://mlsanity.com/paper/2405.07140/) (2024)
- [AQUA-LLM: Evaluating Accuracy, Quantization, and Adversarial Robustness Trade-offs in LLMs for Cybersecurity Question Answering](https://mlsanity.com/paper/2509.13514/) (2025)
- [A Threat-Intelligence Driven Methodology to Incorporate Uncertainty in Cyber Risk Analysis and Enhance Decision Making](https://mlsanity.com/paper/2302.13082/) (2023)

## Built on by (most cited first, 2 of 2)

- [COPA: Continual Preference Optimization for Adaptive Prompt Injection Defense](https://mlsanity.com/paper/2608.19982/) (2026)
- [CyberMaskQA: A Privacy-Aware Benchmark for Evaluating Large Language Models in Cybersecurity Question Answering](https://mlsanity.com/paper/2605.24765/) (2026)

## Abstract (arXiv)

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-aligned defense framework that integrates parameter-efficient quantization with domain-specific preference alignment to jointly optimize efficiency, robustness, and accuracy. Unlike prior methods that address these aspects separately, EAGER leverages Quantized Low-Rank Adaptation (QLoRA) for low-cost fine-tuning and Direct Preference Optimization (DPO) on a self-constructed cybersecurity preference dataset, eliminating the need for human labels. Experiments show that EAGER reduces adversarial attack success rates by up to 7.3x and improves QA accuracy by up to 55% over state-of-the-art defenses, while achieving the lowest response latency on a Jetson Orin, demonstrating its practical edge deployment.