A systematization of knowledge paper that taxonomizes honeypot detection vectors, synthesizes LLM-honeypot literature into canonical architecture and evaluation methods, and proposes a roadmap for autonomous deception systems.
A bibliometric review of large language models research from 2017 to 2023
4 Pith papers cite this work, alongside 51 external citations. Polarity classification is still indexing.
4
Pith papers citing it
51
external citations · external index
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UNVERDICTED 4representative citing papers
Introduces a gradient-based multilingual audit framework for LLM moral decisions in robot assistance scenarios and reports persistent culturally asymmetric gradient tracking failures not fixed by prompting.
GRID trains Qwen-based 4B models on a task-bank reward system of multi-select questions and regex targets to extract security KGs from CTI text, reporting 84.62% precision and 64.91% recall on 249 articles from five sources.
OPT-BENCH and OPT-Agent evaluate LLM self-optimization in large search spaces, showing stronger models improve via feedback but stay constrained by base capacity and below human performance.
citing papers explorer
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SoK: Honeypots & LLMs, More Than the Sum of Their Parts?
A systematization of knowledge paper that taxonomizes honeypot detection vectors, synthesizes LLM-honeypot literature into canonical architecture and evaluation methods, and proposes a roadmap for autonomous deception systems.
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Auditing LLM-Governed Social Robots with Culture-Specific Moral Gradients
Introduces a gradient-based multilingual audit framework for LLM moral decisions in robot assistance scenarios and reports persistent culturally asymmetric gradient tracking failures not fixed by prompting.
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GRID: Graph Representation of Intelligence Data for Security Text Knowledge Graph Construction
GRID trains Qwen-based 4B models on a task-bank reward system of multi-select questions and regex targets to extract security KGs from CTI text, reporting 84.62% precision and 64.91% recall on 249 articles from five sources.
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OPT-BENCH: Evaluating the Iterative Self-Optimization of LLM Agents in Large-Scale Search Spaces
OPT-BENCH and OPT-Agent evaluate LLM self-optimization in large search spaces, showing stronger models improve via feedback but stay constrained by base capacity and below human performance.