MoonshotAI
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Kimi-K3
Kimi K3 is an open-weight, 2.8T-parameter native multimodal agentic model with a 1M-token context window, designed for frontier coding, knowledge work, and reasoning tasks.
AI 仓库情报摘要
FR-AI / ANALYSIS为什么值得关注
它为快速发展的AI驱动渗透测试领域提供了结构化的最新分类，追踪了基准测试与智能体的协同演化，由社区持续维护更新，并附有经过验证的商用系统列表。
适合谁使用
- 网络安全研究人员与实践者
- AI 和 LLM 智能体开发者
- 渗透测试人员和红队操作者
- 网络安全与人工智能方向的研究生
典型使用场景
- 进行自动化渗透测试的文献综述
- 识别LLM驱动进攻性安全领域的研究空白与趋势
- 基于提供的分类法和语料库对新系统进行基准测试
- 对照学术工作评估商用渗透测试平台
项目优势
- 包含81篇论文的全面语料库，系统分类为6个研究类别
- 清晰定义了从纯文本推理到强化学习的四阶段架构演化
- 通过拉取请求持续维护，超出原调查报告范围的更新也被收录
- 包含8个经核实的商用系统列表，提供行业背景参考
使用前须知
- 调查语料库固定为2023年至2026年6月期间发表的论文，可能遗漏后续工作
- 并非所有列出的论文均有公开代码或数据集
- 每篇论文仅归入一个主要类别，可能简化了多方面的贡献
README 快速开始
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges
Agent4Pentest: A Curated Paper List and Survey Companion
English | 简体中文
Survey corpus: 81 papers · 6 research categories · 2023–June 2026
Agent4Pentest is a curated paper list for LLM-driven and agent-based penetration testing and the official companion repository for A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges.
We continuously maintain this collection, add newly released work, and welcome researchers to contribute their papers, benchmarks, systems, datasets, and code through Pull Requests.
[!NOTE] The survey analyzes a fixed corpus of 81 works released between 2023 and June 2026. This repository is a living index and may grow beyond the original survey corpus as the field evolves.
Table of Contents
- Research Landscape
- Paper Collection
- Evaluation Benchmarks
- General-Purpose Systems
- Domain-Specific Frameworks
- CTF-Based Systems
- Defense-Oriented Research
- Surveys
- Commercial Agents & Platforms
- Contributing
- Citation
Research Landscape
The survey maps Agent4Pentest through a six-category taxonomy and a four-phase architectural evolution, then relates this progression to the parallel expansion of benchmark and CTF-based training infrastructure.
Four-Phase Architectural Evolution
| Phase | Core shift | Main bottleneck |
|---|---|---|
| I. Text-only Reasoning (2023) | LLMs reason over the engagement state while humans execute every command. | Execution autonomy and high human dependence |
| II. Tool-augmented Single Agents (2023–2024) | A single agent directly invokes scanners, exploit frameworks, and shells. | Context management and reasoning degradation on long tasks |
| III. Multi-agent Coordination (2024–2025) | Specialized subagents split the attack pipeline under an orchestrator, enabling structured handoffs and parallel execution. | Training-data scarcity and dependence on human demonstrati |
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