AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Experimental results show that LLMs, when guided by rubric-based prompts and supplemented with ATT&CK domain knowledge, achieve robust performance across detection, localization, and TTP mapping tasks.
This paper presents a framework for evaluating prompt injection attacks against LLM-based log interpretation using log traces generated during real cyber attacks, and creates adversarial examples through generic injection generation, refinement, and attack-specific optimization.
Evaluation across semantic similarity, distributional consistency, discriminability tests, and structural fidelity shows that LLM-generated logs—particularly using GPT-4o-mini, closely approximate real MHMS activity, enabling safe and scalable experimentation for intrusion-detection research.
Large language models (LLMs) embedded in enterprise workflows cannot structurally distinguish legitimate instructions from adversarial ones in the same token stream, making prompt injection OWASP's top LLM risk for two consecutive editions a persistent threat across direct and indirect vectors. This paper presents Prom...
The reasoning model DeepSeek-R1 was more susceptible to jailbreak attacks than the non-reasoning model DeepSeek-V3, and different prompt-level defenses were effective against static jailbreaks.
It is shown that the quality of the generated risk indicators is the main driver of zero-shot cyber threat detection performance, and that retrieval mainly benefits weaker LLMs by generating more discriminative risk indicators, whereas stronger models achieve comparable performance without retrieved context.
DeCNIP (Defense with Critical Neuron Isolation Pruning), which leverages representational analysis to identify and neutralize backdoors in a unified pipeline, is introduced, which achieves over 95% relative reduction in Attack Success Rate (ASR), outperforming seven state-of-the-art defenses with only 0.1% neuron inter...
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