## 📜 Literatures
[← Back to README](README.md)
---
### 📊 RQ1: How to construct cybersecurity-oriented domain LLMs? *(74 papers)*
#### Cybersecurity Evaluation Benchmarks
1. SecLens: Role-Specific Evaluation of LLMs for Security Vulnerability Detection | *arXiv* | 2026.04.02 | [Paper Link](https://arxiv.org/abs/2604.01637)
2. CyberThreat-Eval: Can Large Language Models Automate Real-World Threat Research? | *TMLR* | 2026.03.10 | [Paper Link](https://arxiv.org/abs/2603.09452v1)
3. AthenaBench: A Dynamic Benchmark for Evaluating LLMs in Cyber Threat Intelligence | *arxiv* | 2025.11.03 | [Paper Link](https://arxiv.org/pdf/2511.01144)
4. PACEbench: A Framework for Evaluating Practical AI Cyber-Exploitation Capabilities | *arxiv* | 2025.10.13 | [Paper Link](https://arxiv.org/abs/2510.11688)
5. SecureAgentBench: Benchmarking Secure Code Generation under Realistic Vulnerability Scenarios | *arxiv* | 2025.09.26 | [Paper Link](https://arxiv.org/pdf/2509.22097)
6. CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning | *arxiv* | 2025.09.24 | [Paper Link](https://arxiv.org/pdf/2509.20166)
7. AQUA-LLM: Evaluating Accuracy, Quantization, and Adversarial Robustness Trade-offs in LLMs for Cybersecurity Question Answering | *arxiv* | 2025.09.16 | [Paper Link](https://arxiv.org/pdf/2509.13514)
8. AICrypto: A Comprehensive Benchmark For Evaluating Cryptography Capabilities of Large Language Models | *arxiv* | 2025.07.13 | [Paper Link](https://arxiv.org/pdf/2507.09580)
9. ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat Investigation | *arxiv* | 2025.07.14 | [Paper Link](https://arxiv.org/pdf/2507.14201)
10. DefenderBench: A Toolkit for Evaluating Language Agents in Cybersecurity Environments | *arxiv* | 2025.06.10 | [Paper Link](https://arxiv.org/pdf/2506.00739)
11. CyberGym: Evaluating AI Agents Cybersecurity Capabilities with Real-World Vulnerabilities at Scale | *arxiv* | 2025.06.03 | [Paper Link](https://arxiv.org/pdf/2506.02548)
12. DFIR-Metric: A Benchmark Dataset for Evaluating Large Language Models in Digital Forensics and Incident Response | *arxiv* | 2025.05.26 | [Paper Link](https://arxiv.org/pdf/2505.19973)
13. VADER: A Human-Evaluated Benchmark for Vulnerability Assessment, Detection, Explanation, and Remediation | *arxiv* | 2025.05.26 | [Paper Link](https://arxiv.org/pdf/2505.19395)
14. BinMetric: A Comprehensive Binary Analysis Benchmark for Large Language Models | *arxiv* | 2025.05.12 | [Paper Link](https://arxiv.org/pdf/2505.07360)
15. The Digital Cybersecurity Expert: How Far Have We Come? | *arxiv* | 2025.04.16 | [Paper Link](https://arxiv.org/pdf/2504.11783)
16. On Benchmarking Code LLMs for Android Malware Analysis | *arxiv* | 2025.04.01 | [Paper Link](https://arxiv.org/pdf/2504.00694)
17. CVE-Bench: A Benchmark for AI Agents Ability to Exploit Real-World Web Application Vulnerabilities | *arxiv* | 2025.03.21 | [Paper Link](https://arxiv.org/pdf/2503.17332)
18. Benchmarking LLMs and LLM-based Agents in Practical Vulnerability Detection for Code Repositories | *arxiv* | 2025.03.05 | [Paper Link](https://arxiv.org/pdf/2503.03586)
19. AttackSeqBench: Benchmarking Large Language Models Understanding of Sequential Patterns in Cyber Attacks | *arxiv* | 2025.03.05 | [Paper Link](https://arxiv.org/pdf/2503.03170)
20. CyberLLMInstruct: A New Dataset for Analysing Safety of Fine-Tuned LLMs Using Cyber Security Data | *arxiv* | 2025.03.12 | [Paper Link](https://arxiv.org/pdf/2503.09334)
21. Primus: A Pioneering Collection of Open-Source Datasets for Cybersecurity LLM Training | *arXiv* | 2025.02.16 | [Paper Link](https://arxiv.org/pdf/2502.11191)
22. ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks | *arXiv* | 2025.02.07 | [Paper Link](https://arxiv.org/pdf/2502.05352)
23. SecBench: A Comprehensive Multi-Dimensional Benchmarking Dataset for LLMs in Cybersecurity | *arXiv* | 2024.12.31 | [Paper Link](https://arxiv.org/pdf/2412.20787)
24. AI Cyber Risk Benchmark: Automated Exploitation Capabilities | *arXiv* | 2024.12.09 | [Paper Link](https://arxiv.org/pdf/2410.21939)
25. CS-Eval: A Comprehensive Large Language Model Benchmark for CyberSecurity | *arXiv* | 2024.11.25 | [Paper Link](https://arxiv.org/pdf/2411.16239)
26. AttackER: Towards Enhancing Cyber-Attack Attribution with a Named Entity Recognition Dataset | *arXiv* | 2024.08.09 | [Paper Link](https://arxiv.org/pdf/2408.05149)
27. CYBERSECEVAL 3: Advancing the Evaluation of Cybersecurity Risks and Capabilities in Large Language Models | *arXiv* | 2024.08.03 | [Paper Link](https://arxiv.org/pdf/2408.01605)
28. eyeballvul: a future-proof benchmark for vulnerability detection in the wild | *arXiv* | 2024.07.11 | [Paper Link](https://arxiv.org/pdf/2407.08708)
29. NYU CTF Dataset: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security | *arXiv* | 2024.06.09 | [Paper Link](https://arxiv.org/pdf/2406.05590)
30. SECURE: Benchmarking Generative Large Language Models for Cybersecurity Advisory | *arXiv* | 2024.05.30 | [Paper Link](https://arxiv.org/pdf/2405.20441)
31. Assessing Cybersecurity Vulnerabilities in Code Large Language Models | *arXiv* | 2024.04.29 | [Paper Link](https://arxiv.org/pdf/2404.18567)
32. Can LLMs Understand Computer Networks? Towards a Virtual System Administrator | *arXiv* | 2024.04.22 | [Paper Link](https://arxiv.org/pdf/2404.12689)
33. LLMSecEval: A Dataset of Natural Language Prompts for Security Evaluations | *IEEE/ACM International Conference on Mining Software Repositories* | 2023.03.16 | [Paper Link](https://arxiv.org/abs/2303.09384)
34. OpsEval: A Comprehensive IT Operations Benchmark Suite for Large Language Models | *arXiv* | 2024.02.16 | [Paper Link](https://arxiv.org/abs/2310.07637)
35. Can llms patch security issues? | *arXiv* | 2024.02.19 | [Paper Link](https://arxiv.org/abs/2312.00024)
36. CyberMetric: A Benchmark Dataset for Evaluating Large Language Models Knowledge in Cybersecurity | *arXiv* | 2024.02.12 | [Paper Link](https://arxiv.org/abs/2402.07688)
37. DebugBench: Evaluating Debugging Capability of Large Language Models | *ACL Findings* | 2024.01.11 | [Paper Link](https://arxiv.org/abs/2401.04621)
38. Securityeval dataset: mining vulnerability examples to evaluate machine learning-based code generation techniques. | *Proceedings of the 1st International Workshop on Mining Software Repositories Applications for Privacy and Security* | 2022.11.09 | [Paper Link](https://dl.acm.org/doi/10.1145/3549035.3561184)
39. SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security | *arXiv* | 2023.12.26 | [Paper Link](https://arxiv.org/abs/2312.15838v1)
40. Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models | *arXiv* | 2023.12.07 | [Paper Link](https://arxiv.org/abs/2312.04724)
41. An empirical study of netops capability of pre-trained large language models. | *arXiv* | 2023.09.19 | [Paper Link](https://arxiv.org/abs/2309.05557)
42. SecEval: A Comprehensive Benchmark for Evaluating Cybersecurity Knowledge of Foundation Models | *Github* | 2023 | [Paper Link](https://xuanwuai.github.io/SecEval/)
#### Fine-tuned Domain LLMs for Cybersecurity
1. RedSage: A Cybersecurity Generalist LLM | *arxiv* | 2026.01.29 | [Paper Link](https://arxiv.org/pdf/2601.22159)
2. Llama-3.1-FoundationAI-SecurityLLM-Reasoning-8B Technical Report | *arxiv* | 2026.01.28 | [Paper Link](https://arxiv.org/pdf/2601.21051)
3. Large Language Models for Cyber Security | *arxiv* | 2025.11.06 | [Paper Link](https://arxiv.org/pdf/2511.04508)
4. Toward Cybersecurity-Expert Small Language Models | *arxiv* | 2025.10.15 | [Paper Link](https://arxiv.org/pdf/2510.14113)
5. Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report | *arxiv* | 2025.08.01 | [Paper Link](https://arxiv.org/pdf/2508.01059)
6. Cyber-Zero: Training Cybersecurity Agents without Runtime | *arxiv* | 2025.07.29 | [Paper Link](https://arxiv.org/pdf/2508.00910)
7. PhishIntentionLLM: Uncovering Phishing Website Intentions through Multi-Agent Retrieval-Augmented Generation | *arxiv* | 2025.07.21 | [Paper Link](https://arxiv.org/pdf/2507.15419)
8. Less Data, More Security: Advancing Cybersecurity LLMs Specialization via Resource-Efficient Domain-Adaptive Continuous Pre-training with Minimal Tokens | *arxiv* | 2025.06.30 | [Paper Link](https://arxiv.org/pdf/2507.02964)
9. Large Language Model-driven Security Assistant for Internet of Things via Chain-of-Thought | *arxiv* | 2025.05.08 | [Paper Link](https://arxiv.org/pdf/2505.06307)
10. Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report | *arxiv* | 2025.04.28 | [Paper Link](https://arxiv.org/pdf/2504.21039)
11. TrafficLLM: Enhancing Large Language Models for Network Traffic Analysis with Generic Traffic Representation | *arxiv* | 2025.04.05 | [Paper Link](https://arxiv.org/abs/2504.04222)
12. CyberBOT: Towards Reliable Cybersecurity Education via Ontology-Grounded Retrieval Augmented Generation | *arxiv* | 2025.04.01 | [Paper Link](https://arxiv.org/pdf/2504.00389)
13. Phishsense-1B: A Technical Perspective on an AI-Powered Phishing Detection Model | *arxiv* | 2025.03.14 | [Paper Link](https://arxiv.org/pdf/2503.10944)
14. ELTEX: A Framework for Domain-Driven Synthetic Data Generation | *arXiv* | 2025.03.19 | [Paper Link](https://arxiv.org/abs/2503.15055)
15. Fine-tuning Large Language Models for DGA and DNS Exfiltration Detection | *arXiv* | 2024.11.07 | [Paper Link](https://arxiv.org/pdf/2410.21723)
16. AttackQA: Development and Adoption of a Dataset for Assisting Cybersecurity Operations using Fine-tuned and Open-Source LLMs | *arXiv* | 2024.11.02 | [Paper Link](https://arxiv.org/pdf/2411.01073)
17. Hackphyr: A Local Fine-Tuned LLM Agent for Network Security Environments | *arXiv* | 2024.09.17 | [Paper Link](https://arxiv.org/pdf/2409.11276)
18. CyberPal.AI: Empowering LLMs with Expert-Driven Cybersecurity Instructions | *arXiv* | 2024.08.18 | [Paper Link](https://arxiv.org/pdf/2408.09304)
19. IoT-LM: Large Multisensory Language Models for the Internet of Things | *arXiv* | 2024.07.13 | [Paper Link](https://arxiv.org/pdf/2407.09801)
20. A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Automated Program Repair | *arXiv* | 2024.06.09 | [Paper Link](https://arxiv.org/pdf/2406.05639)
21. Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models | *arXiv* | 2024.06.09 | [Paper Link](https://arxiv.org/pdf/2406.05892)
22. Transforming Computer Security and Public Trust Through the Exploration of Fine-Tuning Large Language Models | *arXiv* | 2024.06.02 | [Paper Link](https://arxiv.org/pdf/2406.00628)
23. Assessing LLMs in Malicious Code Deobfuscation of Real-world Malware Campaigns | *arXiv* | 2024.04.30 | [Paper Link](https://arxiv.org/pdf/2404.19715)
24. Nova+: Generative Language Models for Binaries | *arXiv* | 2023.11.27 | [Paper Link](https://arxiv.org/abs/2311.13721)
25. Instruction Tuning for Secure Code Generation | *ICML* | 2024.02.14 | [Paper Link](https://arxiv.org/abs/2402.09497)
26. Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding | *ISSRE* | 2023.10.06 | [Paper Link](https://arxiv.org/abs/2309.09826)
27. RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair | *arXiv* | 2024.03.11 | [Paper Link](https://arxiv.org/abs/2312.15698)
28. Finetuning Large Language Models for Vulnerability Detection | *arXiv* | 2024.02.29 | [Paper Link](https://arxiv.org/abs/2401.17010)
29. Large Language Models for Test-Free Fault Localization | *ICSE* | 2023.10.03 | [Paper Link](https://arxiv.org/abs/2310.01726)
30. HackMentor: Fine-tuning Large Language Models for Cybersecurity | *TrustCom* | 2023.09 | [Paper Link](https://github.com/tmylla/HackMentor)
31. Owl: A Large Language Model for IT Operations | *ICLR* | 2023.09.17 | [Paper Link](https://arxiv.org/abs/2309.09298)
32. SecureFalcon: The Next Cyber Reasoning System for Cyber Security | *arXiv* | 2023.07.13 | [Paper Link](https://arxiv.org/abs/2307.06616)
### 🎯 RQ2: What are the potential applications of LLMs in cybersecurity? *(514 papers)*
#### Threat Intelligence
1. Minerva: Reinforcement Learning with Verifiable Rewards for Cyber Threat Intelligence LLMs | *arxiv* | 2026.01.31 | [Paper Link](https://arxiv.org/pdf/2602.00513)
2. Large Language Models for Explainable Threat Intelligence | *arxiv* | 2025.11.07 | [Paper Link](https://arxiv.org/pdf/2511.05406)
3. Security Logs to ATT&CK Insights: Leveraging LLMs for High-Level Threat Understanding and Cognitive Trait Inference | *arxiv* | 2025.10.24 | [Paper Link](https://arxiv.org/pdf/2510.20930)
4. CTIArena: Benchmarking LLM Knowledge and Reasoning Across Heterogeneous Cyber Threat Intelligence | *arxiv* | 2025.10.14 | [Paper Link](https://arxiv.org/pdf/2510.11974)
5. POLAR: Automating Cyber Threat Prioritization through LLM-Powered Assessment | *arxiv* | 2025.10.02 | [Paper Link](https://arxiv.org/pdf/2510.01552)
6. OntoLogX: Ontology-Guided Knowledge Graph Extraction from Cybersecurity Logs with Large Language Models | *arxiv* | 2025.10.01 | [Paper Link](https://arxiv.org/pdf/2510.01409)
7. Uncovering Vulnerabilities of LLM-Assisted Cyber Threat Intelligence | *arxiv* | 2025.10.01 | [Paper Link](https://arxiv.org/pdf/2509.23573)
8. DroidTTP: Mapping Android Applications with TTP for Cyber Threat Intelligence | *arxiv* | 2025.03.20 | [Paper Link](https://arxiv.org/pdf/2503.15866)
9. A Systematic Approach to Predict the Impact of Cybersecurity Vulnerabilities Using LLMs | *arxiv* | 2025.08.25 | [Paper Link](https://arxiv.org/pdf/2508.18439)
10. Enabling Transparent Cyber Threat Intelligence Combining Large Language Models and Domain Ontologies | *arxiv* | 2025.08.27 | [Paper Link](https://arxiv.org/pdf/2509.00081)
11. False Alarms, Real Damage: Adversarial Attacks Using LLM-based Models on Text-based Cyber Threat Intelligence Systems | *arxiv* | 2025.07.05 | [Paper Link](https://arxiv.org/pdf/2507.06252)
12. LRCTI: A Large Language Model-Based Framework for Multi-Step Evidence Retrieval and Reasoning in Cyber Threat Intelligence Credibility Verification | *arxiv* | 2025.07.15 | [Paper Link](https://arxiv.org/pdf/2507.11310)
13. Towards Effective Identification of Attack Techniques in Cyber Threat Intelligence Reports using Large Language Models | *arxiv* | 2025.05.05 | [Paper Link](https://arxiv.org/pdf/2505.03147)
14. Can We Enhance Bug Report Quality Using LLMs?: An Empirical Study of LLM-Based Bug Report Generation | *arxiv* | 2025.04.26 | [Paper Link](https://arxiv.org/pdf/2504.18804)
15. MaLAware: Automating the Comprehension of Malicious Software Behaviours using Large Language Models (LLMs) | *arxiv* | 2025.04.01 | [Paper Link](https://arxiv.org/pdf/2504.01145)
16. LLM-Assisted Proactive Threat Intelligence for Automated Reasoning | *arxiv* | 2025.04.01 | [Paper Link](https://arxiv.org/pdf/2504.00428)
17. Large Language Models are Unreliable for Cyber Threat Intelligence | *arxiv* | 2025.03.29 | [Paper Link](https://arxiv.org/pdf/2503.23175)
18. Cyber Defense Reinvented: Large Language Models as Threat Intelligence Copilots | *arXiv* | 2025.02.28 | [Paper Link](https://arxiv.org/abs/2502.20791)
19. Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models | *arXiv* | 2024.12.16 | [Paper Link](https://arxiv.org/pdf/2412.10978)
20. IntellBot: Retrieval Augmented LLM Chatbot for Cyber Threat Knowledge Delivery | *arXiv* | 2024.11.08| [Paper Link](https://arxiv.org/pdf/2411.05442)
21. CTINEXUS: Leveraging Optimized LLM In-Context Learning for Constructing Cybersecurity Knowledge Graphs Under Data Scarcity | *arXiv* | 2024.10.28 | [Paper Link](https://arxiv.org/pdf/2410.21060)
22. AI-Driven Cyber Threat Intelligence Automation | *arXiv* | 2024.10.27 | [Paper Link](https://arxiv.org/pdf/2410.20287)
23. Cyber Knowledge Completion Using Large Language Models | *arXiv* | 2024.09.24 | [Paper Link](https://arxiv.org/pdf/2409.16176)
24. Evaluating the Usability of LLMs in Threat Intelligence Enrichment | *arXiv* | 2024.09.23 | [Paper Link](https://arxiv.org/pdf/2409.15072)
25. KGV: Integrating Large Language Models with Knowledge Graphs for Cyber Threat Intelligence Credibility Assessment | *arXiv* | 2024.08.15 | [Paper Link](https://arxiv.org/pdf/2408.08088)
26. Usefulness of data flow diagrams and large language models for security threat validation: a registered report | *arXiv* | 2024.08.14 | [Paper Link](https://arxiv.org/pdf/2408.07537)
27. A RAG-Based Question-Answering Solution for Cyber-Attack Investigation and Attribution | *arXiv* | 2024.08.12 | [Paper Link](https://arxiv.org/pdf/2408.06272)
28. The Use of Large Language Models (LLM) for Cyber Threat Intelligence (CTI) in Cybercrime Forums | *arXiv* | 2024.08.08 | [Paper Link](https://arxiv.org/pdf/2408.03354)
29. Psychological Profiling in Cybersecurity: A Look at LLMs and Psycholinguistic Features | *arXiv* | 2024.08.09 | [Paper Link](https://arxiv.org/pdf/2406.18783)
30. Using LLMs to Automate Threat Intelligence Analysis Workflows in Security Operation Centers | *arXiv* | 2024.07.18 | [Paper Link](https://arxiv.org/pdf/2407.13093)
31. LLMCloudHunter: Harnessing LLMs for Automated Extraction of Detection Rules from Cloud-Based CTI | *arXiv* | 2024.07.06 | [Paper Link](https://arxiv.org/pdf/2407.05194)
32. Actionable Cyber Threat Intelligence using Knowledge Graphs and Large Language Models | *arXiv* | 2024.06.30 | [Paper Link](https://arxiv.org/pdf/2407.02528)
33. AttacKG+:Boosting Attack Knowledge Graph Construction with Large Language Models | *EuroS&P Workshop* | 2024.05.08 | [Paper Link](https://arxiv.org/pdf/2405.04753)
34. SEvenLLM: Benchmarking, Eliciting, and Enhancing Abilities of Large Language Models in Cyber Threat Intelligence | *arXiv* | 2024.05.06 | [Paper Link](https://arxiv.org/pdf/2405.03446)
35. Crimson: Empowering Strategic Reasoning in Cybersecurity through Large Language Models | *arXiv* | 2024.03.01 | [Paper Link](https://arxiv.org/abs/2403.00878)
36. Evaluation of LLM Chatbots for OSINT-based Cyber Threat Awareness | *Expert Syst. Appl.* | 2024.03.13 | [Paper Link](https://arxiv.org/abs/2401.15127)
37. LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge | *arXiv* | 2024.01.18 | [Paper Link](https://arxiv.org/abs/2401.10036)
38. Advancing TTP Analysis: Harnessing the Power of Encoder-Only and Decoder-Only Language Models with Retrieval Augmented Generation | *arXiv* | 2024.01.12 | [Paper Link](https://arxiv.org/abs/2401.00280)
39. ChatGPT, Llama, can you write my report? An experiment on assisted digital forensics reports written using (Local) Large Language Models | *Forensic Sci. Int. Digit. Investig.* | 2023.12.22 | [Paper Link](https://arxiv.org/abs/2312.14607)
40. HW-V2W-Map: Hardware Vulnerability to Weakness Mapping Framework for Root Cause Analysis with GPT-assisted Mitigation Suggestion | *arXiv* | 2023.12.21 | [Paper Link](https://arxiv.org/abs/2312.13530)
41. AGIR: Automating Cyber Threat Intelligence Reporting with Natural Language Generation | *BigData* | 2023.10.04 | [Paper Link](https://ieeexplore.ieee.org/abstract/document/10386116)
42. Cyber Sentinel: Exploring Conversational Agents in Streamlining Security Tasks with GPT-4 | *arXiv* | 2023.09.28 | [Paper Link](https://arxiv.org/abs/2309.16422)
43. Cupid: Leveraging ChatGPT for More Accurate Duplicate Bug Report Detection | *arXiv* | 2023.08.27 | [Paper Link](https://arxiv.org/abs/2308.10022)
44. On the Uses of Large Language Models to Interpret Ambiguous Cyberattack Descriptions | *arXiv* | 2023.08.22 | [Paper Link](https://arxiv.org/abs/2306.14062)
45. An Empirical Study on Using Large Language Models to Analyze Software Supply Chain Security Failures | *Proceedings of the 2023 Workshop on Software Supply Chain Offensive Research and Ecosystem Defenses* | 2023.08.09 | [Paper Link](https://arxiv.org/abs/2308.04898)
46. Time for aCTIon: Automated Analysis of Cyber Threat Intelligence in the Wild | *arXiv* | 2023.07.14 | [Paper Link](https://arxiv.org/abs/2307.10214)
#### FUZZ
1. LLMs are All You Need? Improving Fuzz Testing for MOJO with Large Language Models | *arxiv* | 2025.10.11 | [Paper Link](https://arxiv.org/pdf/2510.10179)
2. All You Need Is A Fuzzing Brain: An LLM-Powered System for Automated Vulnerability Detection and Patching | *arxiv* | 2025.09.08 | [Paper Link](https://arxiv.org/pdf/2509.07225)
3. LLM-Assisted Model-Based Fuzzing of Protocol Implementations | *arxiv* | 2025.08.03 | [Paper Link](https://arxiv.org/pdf/2508.01750)
4. Fuzzing: Randomness? Reasoning! Efficient Directed Fuzzing via Large Language Models | *arxiv* | 2025.06.30 | [Paper Link](https://arxiv.org/pdf/2507.22065)
5. Directed Greybox Fuzzing via Large Language Model | *arxiv* | 2025.05.06 | [Paper Link](https://arxiv.org/pdf/2505.03425)
6. ToolFuzz -- Automated Agent Tool Testing | *arxiv* | 2025.03.06 | [Paper Link](https://arxiv.org/pdf/2503.04479)
7. Towards Reliable LLM-Driven Fuzz Testing: Vision and Road Ahead | *arxiv* | 2025.03.02 | [Paper Link](https://arxiv.org/pdf/2503.00795)
8. Your Fix Is My Exploit: Enabling Comprehensive DL Library API Fuzzing with Large Language Models | *arXiv* | 2025.01.08 | [Paper Link](https://arxiv.org/pdf/2501.04312)
9. Large Language Model assisted Hybrid Fuzzing | *arXiv* | 2024.12.19 | [Paper Link](https://arxiv.org/pdf/2412.15931)
10. Harnessing Large Language Models for Seed Generation in Greybox Fuzzing | *arXiv* | 2024.11.27 | [Paper Link](https://arxiv.org/pdf/2411.18143)
11. ChatHTTPFuzz: Large Language Model-Assisted IoT HTTP Fuzzing | *arXiv* | 2024.11.18 | [Paper Link](https://arxiv.org/pdf/2411.11929)
12. AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testing | *arXiv* | 2024.11.05 | [Paper Link](https://arxiv.org/pdf/2409.10737)
13. FuzzCoder: Byte-level Fuzzing Test via Large Language Model | *arXiv* | 2024.09.03 | [Paper Link](https://arxiv.org/pdf/2409.01944)
14. An Exploratory Study on Using Large Language Models for Mutation Testing | *arXiv* | 2024.06.14 | [Paper Link](https://arxiv.org/pdf/2406.09843)
15. Prompt Fuzzing for Fuzz Driver Generation | ACM CCS 2024 | 2024.05.29 | [Paper Link](https://arxiv.org/abs/2312.17677)
16. When Fuzzing Meets LLMs: Challenges and Opportunities | *ACM International Conference on the Foundations of Software Engineering* | 2024.04.25 | [Paper Link](https://arxiv.org/pdf/2404.16297)
17. Fuzzing BusyBox: Leveraging LLM and Crash Reuse for Embedded Bug Unearthing | *USENIX* | 2024.03.06 | [Paper Link](https://arxiv.org/abs/2403.03897)
18. Large language model guided protocol fuzzing | *NDSS* | 2024.02.26 | [Paper Link](https://www.ndss-symposium.org/wp-content/uploads/2024-556-paper.pdf?ref=blog.exploits.club)
19. Fuzz4All: Universal Fuzzing with Large Language Models | *ICSE* | 2024.01.15 | [Paper Link](https://arxiv.org/abs/2308.04748)
20. How well does LLM generate security tests? | *arXiv* | 2023.10.03 | [Paper Link](https://arxiv.org/abs/2310.00710)
21. CODAMOSA: Escaping Coverage Plateaus in Test Generation with Pre-trained Large Language Models | *ICSE* | 2023.07.26 | [Paper Link](https://ieeexplore.ieee.org/document/10172800/)
22. Understanding Large Language Model Based Fuzz Driver Generation | *arXiv* | 2023.07.24 | [Paper Link](https://arxiv.org/abs/2307.12469)
23. Large Language Models Are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models | *ISSTA* | 2023.06.07 | [Paper Link](https://arxiv.org/abs/2212.14834)
24. Augmenting Greybox Fuzzing with Generative AI | *arXiv* | 2023.06.11 | [Paper Link](https://arxiv.org/abs/2306.06782)
25. Large Language Models are Edge-Case Fuzzers: Testing Deep Learning Libraries via FuzzGPT | *arXiv* | 2023.04.04 | [Paper Link](https://arxiv.org/abs/2304.02014)
#### Vulnerability Detection
1. LLM Agents for Automated Web Vulnerability Reproduction: Are We There Yet? | *arxiv* | 2025.10.16 | [Paper Link](https://arxiv.org/pdf/2510.14700)
2. Synergizing Static Analysis with Large Language Models for Vulnerability Discovery and beyond | *arxiv* | 2025.09.18 | [Paper Link](https://arxiv.org/pdf/2509.15433)
3. SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks | *arxiv* | 2025.06.13 | [Paper Link](https://arxiv.org/pdf/2506.11791)
4. Large Language Models Versus Static Code Analysis Tools: A Systematic Benchmark for Vulnerability Detection | *arxiv* | 2025.08.06 | [Paper Link](https://arxiv.org/pdf/2508.04448)
5. A Systematic Literature Review on Detecting Software Vulnerabilities with Large Language Models | *arxiv* | 2025.07.30 | [Paper Link](https://arxiv.org/pdf/2507.22659)
6. Out of Distribution, Out of Luck: How Well Can LLMs Trained on Vulnerability Datasets Detect Top 25 CWE Weaknesses? | *arxiv* | 2025.07.29 | [Paper Link](https://arxiv.org/pdf/2507.21817)
7. LLMxCPG: Context-Aware Vulnerability Detection Through Code Property Graph-Guided Large Language Models | *USENIX* | 2025.07.22 | [Paper Link](https://arxiv.org/abs/2507.16585)
8. Revisiting Pre-trained Language Models for Vulnerability Detection | *arxiv* | 2025.07.22 | [Paper Link](https://arxiv.org/pdf/2507.16887)
9. MalCodeAI: Autonomous Vulnerability Detection and Remediation via Language Agnostic Code Reasoning | *arxiv* | 2025.07.15 | [Paper Link](https://arxiv.org/pdf/2507.10898)
10. Identifying Helpful Context for LLM-based Vulnerability Repair: A Preliminary Study | *arxiv* | 2025.06.13 | [Paper Link](https://arxiv.org/pdf/2506.11561)
11. VulStamp: Vulnerability Assessment using Large Language Model | *arxiv* | 2025.06.13 | [Paper Link](https://arxiv.org/pdf/2506.11484)
12. Large Language Models for Multilingual Vulnerability Detection: How Far Are We? | *arxiv* | 2025.06.09 | [Paper Link](https://arxiv.org/pdf/2506.07503)
13. Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data | *arxiv* | 2025.06.09 | [Paper Link](https://arxiv.org/pdf/2506.07390)
14. Let the Trial Begin: A Mock-Court Approach to Vulnerability Detection using LLM-Based Agents | *arxiv* | 2025.05.16 | [Paper Link](https://arxiv.org/pdf/2505.10961)
15. A Preliminary Study of Large Language Models for Multilingual Vulnerability Detection | *arxiv* | 2025.05.12 | [Paper Link](https://arxiv.org/pdf/2505.07376)
16. Enhancing Large Language Models with Faster Code Preprocessing for Vulnerability Detection | *arxiv* | 2025.05.08 | [Paper Link](https://arxiv.org/pdf/2505.05600)
17. LASHED: LLMs And Static Hardware Analysis for Early Detection of RTL Bugs | *arxiv* | 2025.04.30 | [Paper Link](https://arxiv.org/pdf/2504.21770)
18. LLMpatronous: Harnessing the Power of LLMs For Vulnerability Detection | *arxiv* | 2025.04.25 | [Paper Link](https://arxiv.org/pdf/2504.18423)
19. Context-Enhanced Vulnerability Detection Based on Large Language Model | *arxiv* | 2025.04.23 | [Paper Link](https://arxiv.org/pdf/2504.16877)
20. Automated Static Vulnerability Detection via a Holistic Neuro-symbolic Approach | *arxiv* | 2025.04.22 | [Paper Link](https://arxiv.org/pdf/2504.16057)
21. Everything You Wanted to Know About LLM-based Vulnerability Detection But Were Afraid to Ask | *arxiv* | 2025.04.18 | [Paper Link](https://arxiv.org/pdf/2504.13474)
22. MOS: Towards Effective Smart Contract Vulnerability Detection through Mixture-of-Experts Tuning of Large Language Models | *arxiv* | 2025.04.16 | [Paper Link](https://arxiv.org/pdf/2504.12234)
23. Malware analysis assisted by AI with R2AI | *arxiv* | 2025.04.10 | [Paper Link](https://arxiv.org/pdf/2504.07574)
24. Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering | *arxiv* | 2025.04.08 | [Paper Link](https://arxiv.org/pdf/2504.07137)
25. CVE-Bench: Benchmarking LLM-based Software Engineering Agent`s Ability to Repair Real-World CVE Vulnerabilities | *NAACL* | 2025.03 | [Paper Link](https://aclanthology.org/2025.naacl-long.212/)
26. Reasoning with LLMs for Zero-Shot Vulnerability Detection | *arxiv* | 2025.03.22 | [Paper Link](https://arxiv.org/pdf/2503.17885)
27. Vulnerability Detection: From Formal Verification to Large Language Models and Hybrid Approaches: A Comprehensive Overview | *arxiv* | 2025.03.13 | [Paper Link](https://arxiv.org/pdf/2503.10784)
28. CASTLE: Benchmarking Dataset for Static Code Analyzers and LLMs towards CWE Detection | *arxiv* | 2025.03.12 | [Paper Link](https://arxiv.org/pdf/2503.09433)
29. Benchmarking Large Language Models for Multi-Language Software Vulnerability Detection | *arxiv* | 2025.03.03 | [Paper Link](https://arxiv.org/pdf/2503.01449)
30. CVE-LLM : Ontology-Assisted Automatic Vulnerability Evaluation Using Large Language Models | *arXiv* | 2025.02.21 | [Paper Link](https://arxiv.org/pdf/2502.15932)
31. Large Language Models in Software Security: A Survey of Vulnerability Detection Techniques and Insights | *arXiv* | 2025.02.10 | [Paper Link](https://arxiv.org/pdf/2502.07049)
32. Large Language Models for In-File Vulnerability Localization Can Be "Lost in the End" | *arXiv* | 2025.02.09 | [Paper Link](https://arxiv.org/pdf/2502.06898)
33. Streamlining Security Vulnerability Triage with Large Language Models | *arXiv* | 2025.01.31 | [Paper Link](https://arxiv.org/pdf/2501.18908)
34. Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows | *arXiv* | 2025.01.30 | [Paper Link](https://arxiv.org/pdf/2502.00064)
35. Helping LLMs Improve Code Generation Using Feedback from Testing and Static Analysis | *arXiv* | 2025.01.07 | [Paper Link](https://arxiv.org/pdf/2412.14841)
36. CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection | *arXiv* | 2025.01.08 | [Paper Link](https://arxiv.org/pdf/2501.04510)
37. Leveraging Large Language Models and Machine Learning for Smart Contract Vulnerability Detection | *arXiv* | 2025.01.04 | [Paper Link](https://arxiv.org/pdf/2501.02229)
38. Investigating Large Language Models for Code Vulnerability Detection: An Experimental Study | *arXiv* | 2024.12.24 | [Paper Link](https://arxiv.org/pdf/2412.18260)
39. Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection | *arXiv* | 2024.12.16 | [Paper Link](https://arxiv.org/pdf/2412.12039)
40. ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models | *arXiv* | 2024.12.06 | [Paper Link](https://arxiv.org/pdf/2412.04756)
41. CleanVul: Automatic Function-Level Vulnerability Detection in Code Commits Using LLM Heuristics | *arXiv* | 2024.11.26 | [Paper Link](https://arxiv.org/pdf/2411.17274)
42. EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code | *arXiv* | 2024.11.25 | [Paper Link](https://arxiv.org/pdf/2411.16561)
43. CryptoFormalEval: Integrating LLMs and Formal Verification for Automated Cryptographic Protocol Vulnerability Detection | *arXiv* | 2024.11.20 | [Paper Link](https://arxiv.org/pdf/2411.13627)
44. Beyond Static Tools: Evaluating Large Language Models for Cryptographic Misuse Detection | *arXiv* | 2024.11.14 | [Paper Link](https://arxiv.org/pdf/2411.09772)
45. LProtector: An LLM-driven Vulnerability Detection System | *arXiv* | 2024.11.04 | [Paper Link](https://arxiv.org/pdf/2411.06493)
46. Enhancing Reverse Engineering: Investigating and Benchmarking Large Language Models for Vulnerability Analysis in Decompiled Binaries | *arXiv* | 2024.11.07 | [Paper Link](https://arxiv.org/pdf/2411.04981)
47. ProveRAG: Provenance-Driven Vulnerability Analysis with Automated Retrieval-Augmented LLMs | *arXiv* | 2024.10.22 | [Paper Link](https://arxiv.org/pdf/2410.17406)
48. RealVul: Can We Detect Vulnerabilities in Web Applications with LLM? | *arXiv* | 2024.10.10 | [Paper Link](https://arxiv.org/pdf/2410.07573)
49. Code Vulnerability Repair with Large Language Model using Context-Aware Prompt Tuning | *arXiv* | 2024.09.27 | [Paper Link](https://arxiv.org/pdf/2409.18395)
50. Boosting Cybersecurity Vulnerability Scanning based on LLM-supported Static Application Security Testing | *arXiv* | 2024.09.24 | [Paper Link](https://arxiv.org/pdf/2409.15735)
51. VulnLLMEval: A Framework for Evaluating Large Language Models in Software Vulnerability Detection and Patching | *arXiv* | 2024.09.17 | [Paper Link](https://arxiv.org/pdf/2409.10756)
52. Code Vulnerability Detection: A Comparative Analysis of Emerging Large Language Models | *arXiv* | 2024.09.16 | [Paper Link](https://arxiv.org/pdf/2409.10490)
53. Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches | *arXiv* | 2024.09.11 | [Paper Link](https://arxiv.org/pdf/2409.07587)
54. SAFE: Advancing Large Language Models in Leveraging Semantic and Syntactic Relationships for Software Vulnerability Detection | *arXiv* | 2024.09.02 | [Paper Link](https://arxiv.org/pdf/2409.00882)
55. Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection | *European symposium on research in computer security* | 2024.08.29 | [Paper Link](https://arxiv.org/pdf/2408.16400)
56. ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data | *arXiv* | 2024.08.28 | [Paper Link](https://arxiv.org/pdf/2408.16028)
57. LLM-Enhanced Static Analysis for Precise Identification of Vulnerable OSS Versions | *arXiv* | 2024.08.14 | [Paper Link](https://arxiv.org/pdf/2408.07321)
58. Exploring RAG-based Vulnerability Augmentation with LLMs | *arXiv* | 2024.08.08 | [Paper Link](https://arxiv.org/pdf/2408.04125)
59. Harnessing the Power of LLMs in Source Code Vulnerability Detection | *arXiv* | 2024.08.07 | [Paper Link](https://arxiv.org/pdf/2408.03489)
60. Towards Effectively Detecting and Explaining Vulnerabilities Using Large Language Models | *arXiv* | 2024.08.08 | [Paper Link](https://arxiv.org/pdf/2406.09701)
61. Comparison of Static Application Security Testing Tools and Large Language Models for Repo-level Vulnerability Detection | *arXiv* | 2024.07.23 | [Paper Link](https://arxiv.org/pdf/2407.16235)
62. SCoPE: Evaluating LLMs for Software Vulnerability Detection | *arXiv* | 2024.07.19 | [Paper Link](https://arxiv.org/pdf/2407.14372)
63. Static Detection of Filesystem Vulnerabilities in Android Systems | *arXiv* | 2024.07.16 | [Paper Link](https://arxiv.org/pdf/2407.11279)
64. Detect Llama -- Finding Vulnerabilities in Smart Contracts using Large Language Models | *Information Security and Privacy* | 2024.07.12 | [Paper Link](https://arxiv.org/pdf/2407.08969)
65. Assessing the Effectiveness of LLMs in Android Application Vulnerability Analysis | *arXiv* | 2024.06.27 | [Paper Link](https://arxiv.org/pdf/2406.18894)
66. MALSIGHT: Exploring Malicious Source Code and Benign Pseudocode for Iterative Binary Malware Summarization | *arXiv* | 2024.06.26 | [Paper Link](https://arxiv.org/pdf/2406.18379)
67. Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG | *arXiv* | 2024.06.19 | [Paper Link](https://arxiv.org/pdf/2406.11147)
68. Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning | *ACL Findings* | 2024.06.06 | [Paper Link](https://arxiv.org/pdf/2406.03718)
69. LLM-Assisted Static Analysis for Detecting Security Vulnerabilities | *arXiv* | 2024.05.27 | [Paper Link](https://arxiv.org/pdf/2405.17238)
70. Harnessing Large Language Models for Software Vulnerability Detection: A Comprehensive Benchmarking Study | *arXiv* | 2024.05.24 | [Paper Link](https://arxiv.org/pdf/2405.15614)
71. DLAP: A Deep Learning Augmented Large Language Model Prompting Framework for Software Vulnerability Detection | *Journal of Systems and Software* | 2024.05.02 | [Paper Link](https://arxiv.org/pdf/2405.01202)
72. Large Language Model for Vulnerability Detection and Repair: Literature Review and Roadmap | *arXiv* | 2024.04.04 | [Paper Link](https://arxiv.org/pdf/2404.02525)
73. How Far Have We Gone in Vulnerability Detection Using Large Language Models | *arXiv* | 2023.12.22 | [Paper Link](https://arxiv.org/abs/2311.12420)
74. The FormAI Dataset: Generative AI in Software Security through the Lens of Formal Verification | *International Conference on Predictive Models and Data Analytics in Software Engineering* | 2023.09.02 | [Paper Link](https://arxiv.org/abs/2307.02192)
75. DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection | *International Symposium on Research in Attacks, Intrusions and Defenses* | 2023.08.09 | [Paper Link](https://arxiv.org/abs/2304.00409)
76. How ChatGPT is Solving Vulnerability Management Problem | *arXiv* | 2023.11.11 | [Paper Link](https://arxiv.org/abs/2311.06530)
77. Multi-role Consensus through LLMs Discussions for Vulnerability Detection | *arXiv* | 2024.03.21 | [Paper Link](https://arxiv.org/abs/2403.14274)
78. LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning | *arXiv* | 2024.01.29 | [Paper Link](https://arxiv.org/abs/2401.16185)
79. LLbezpeky: Leveraging Large Language Models for Vulnerability Detection | *arXiv* | 2024.01.13 | [Paper Link](https://arxiv.org/abs/2401.01269)
80. Software Vulnerability Detection with GPT and In-Context Learning | *DSC* | 2024.01.08 | [Paper Link](https://ieeexplore.ieee.org/abstract/document/10381286)
81. GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis | *ICSE* | 2023.12.25 | [Paper Link](https://arxiv.org/abs/2308.03314)
82. Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities | *arXiv* | 2023.11.16 | [Paper Link](https://arxiv.org/abs/2311.16169)
83. The Hitchhiker's Guide to Program Analysis: A Journey with Large Language Models | *arXiv* | 2023.11.15 | [Paper Link](https://arxiv.org/abs/2308.00245)
84. Large Language Model-Powered Smart Contract Vulnerability Detection: New Perspectives | *TPS-ISA* | 2023.10.16 | [Paper Link](https://arxiv.org/abs/2310.01152)
85. Large Language Models for Test-Free Fault Localization | *ICSE* | 2023.10.03 | [Paper Link](https://arxiv.org/abs/2310.01726)
86. DefectHunter: A Novel LLM-Driven Boosted-Conformer-based Code Vulnerability Detection Mechanism | *arXiv* | 2023.09.27 | [Paper Link](https://arxiv.org/abs/2309.15324)
87. Software Vulnerability Detection using Large Language Models | *ISSRE Workshop* | 2023.09.02 | [Paper Link](https://ieeexplore.ieee.org/document/10301302/)
88. Using ChatGPT as a Static Application Security Testing Tool | *arXiv* | 2023.08.28 | [Paper Link](https://arxiv.org/abs/2308.14434)
89. Prompt-Enhanced Software Vulnerability Detection Using ChatGPT | *ICSE* | 2023.08.24 | [Paper Link](https://arxiv.org/abs/2308.12697)
90. VulLibGen: Identifying Vulnerable Third-Party Libraries via Generative Pre-Trained Model | *arXiv* | 2023.08.09 | [Paper Link](https://arxiv.org/abs/2308.04662)
91. Evaluation of ChatGPT Model for Vulnerability Detection | *arXiv* | 2023.04.12 | [Paper Link](https://arxiv.org/abs/2304.07232)
92. Software Vulnerability and Functionality Assessment using LLMs | *arXiv* | 2024.03.13 | [Paper Link](https://arxiv.org/abs/2403.08429)
93. Finetuning Large Language Models for Vulnerability Detection | *arXiv* | 2024.03.01 | [Paper Link](https://arxiv.org/abs/2401.17010)
94. Detecting software vulnerabilities using Language Models | *CSR* | 2023.02.23 | [Paper Link](https://arxiv.org/abs/2302.11773)
#### Program or Vulnerability Repair
1. Vul-R2: A Reasoning LLM for Automated Vulnerability Repair | *arxiv* | 2025.10.07 | [Paper Link](https://arxiv.org/pdf/2510.05480)
2. BloomAPR: A Blooms Taxonomy-based Framework for Assessing the Capabilities of LLM-Powered APR Solutions | *arxiv* | 2025.09.30 | [Paper Link](https://arxiv.org/pdf/2509.25465)
3. SecureFixAgent: A Hybrid LLM Agent for Automated Python Static Vulnerability Repair | *arxiv* | 2025.09.18 | [Paper Link](https://arxiv.org/pdf/2509.16275)
4. VulnRepairEval: An Exploit-Based Evaluation Framework for Assessing Large Language Model Vulnerability Repair Capabilities | *arxiv* | 2025.09.03 | [Paper Link](https://arxiv.org/pdf/2509.03331)
5. Automated Repair of C Programs Using Large Language Models | *arxiv* | 2025.09.02 | [Paper Link](https://arxiv.org/pdf/2509.01947)
6. VulnRepairEval: An Exploit-Based Evaluation Framework for Assessing Large Language Model Vulnerability Repair Capabilities | *arXiv* | 2025.09.03 | [Paper Link](https://arxiv.org/pdf/2509.03331)
7. Automated Repair of C Programs Using Large Language Models | *arXiv* | 2025.09.02 | [Paper Link](https://arxiv.org/pdf/2509.01947)
8. On the Evaluation of Large Language Models in Multilingual Vulnerability Repair | *arXiv* | 2025.08.05 | [Paper Link](https://arxiv.org/pdf/2508.03470)
9. Repair-R1: Better Test Before Repair | *arXiv* | 2025.07.30 | [Paper Link](https://arxiv.org/pdf/2507.22853)
10. Repairing vulnerabilities without invisible hands. A differentiated replication study on LLMs | *arXiv* | 2025.07.28 | [Paper Link](https://arxiv.org/pdf/2507.20977)
11. The Impact of Fine-tuning Large Language Models on Automated Program Repair | *arXiv* | 2025.07.26 | [Paper Link](https://arxiv.org/pdf/2507.19909)
12. Bug Fixing with Broader Context: Enhancing LLM-Based Program Repair via Layered Knowledge Injection | *arXiv* | 2025.06.30 | [Paper Link](https://arxiv.org/pdf/2506.24015)
13. Repair Ingredients Are All You Need: Improving Large Language Model-Based Program Repair via Repair Ingredients Search | *arXiv* | 2025.06.30 | [Paper Link](https://arxiv.org/pdf/2506.23100)
14. A Survey of LLM-based Automated Program Repair: Taxonomies, Design Paradigms, and Applications | *arXiv* | 2025.06.30 | [Paper Link](https://arxiv.org/pdf/2506.23749)
15. Empirical Evaluation of Generalizable Automated Program Repair with Large Language Models | *arXiv* | 2025.06.03 | [Paper Link](https://arxiv.org/pdf/2506.03283)
16. Boosting Open-Source LLMs for Program Repair via Reasoning Transfer and LLM-Guided Reinforcement Learning | *arXiv* | 2025.06.04 | [Paper Link](https://arxiv.org/pdf/2506.03921)
17. Fixing 7,400 Bugs for 1$: Cheap Crash-Site Program Repair | *arXiv* | 2025.05.19 | [Paper Link](https://arxiv.org/pdf/2505.13103)
18. Adversarial Reasoning for Repair Based on Inferred Program Intent | *arXiv* | 2025.05.19 | [Paper Link](https://arxiv.org/pdf/2505.13008)
19. Synthetic Code Surgery: Repairing Bugs and Vulnerabilities with LLMs and Synthetic Data | *arXiv* | 2025.05.12 | [Paper Link](https://arxiv.org/pdf/2505.07372)
20. Automated Repair of Ambiguous Natural Language Requirements | *arXiv* | 2025.05.12 | [Paper Link](https://arxiv.org/pdf/2505.07270)
21. Towards Effectively Leveraging Execution Traces for Program Repair with Code LLMs | *arXiv* | 2025.05.07 | [Paper Link](https://arxiv.org/pdf/2505.04441)
22. The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Models | *arXiv* | 2025.05.05 | [Paper Link](https://arxiv.org/pdf/2505.02931)
23. Adapting Knowledge Prompt Tuning for Enhanced Automated Program Repair | *arXiv* | 2025.04.02 | [Paper Link](https://arxiv.org/pdf/2504.01523)
24. LLM4CVE: Enabling Iterative Automated Vulnerability Repair with Large Language Models | *arXiv* | 2025.01.07 | [Paper Link](https://arxiv.org/pdf/2501.03446)
25. From Defects to Demands: A Unified, Iterative, and Heuristically Guided LLM-Based Framework for Automated Software Repair and Requirement Realization | *arXiv* | 2024.12.06 | [Paper Link](https://arxiv.org/pdf/2412.05098)
26. Integrating Various Software Artifacts for Better LLM-based Bug Localization and Program Repair | *arXiv* | 2024.12.05 | [Paper Link](https://arxiv.org/pdf/2412.03905)
27. Fixing Security Vulnerabilities with AI in OSS-Fuzz | *arXiv* | 2024.11.21 | [Paper Link](https://arxiv.org/pdf/2411.03346)
28. A Comprehensive Survey of AI-Driven Advancements and Techniques in Automated Program Repair and Code Generation | *arXiv* | 2024.11.12 | [Paper Link](https://arxiv.org/pdf/2411.07586)
29. The Best Defense is a Good Offense: Countering LLM-Powered Cyberattacks | *arXiv* | 2024.10.20 | [Paper Link](https://arxiv.org/pdf/2410.15396)
30. APOLLO: A GPT-based tool to detect phishing emails and generate explanations that warn users | *arXiv* | 2024.10.10 | [Paper Link](https://arxiv.org/pdf/2410.07997)
31. Fixing Code Generation Errors for Large Language Models | *arXiv* | 2024.09.01 | [Paper Link](https://arxiv.org/pdf/2409.00676)
32. MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair | *arXiv* | 2024.08.26 | [Paper Link](https://arxiv.org/pdf/2408.09568)
33. Automated Software Vulnerability Patching using Large Language Models | *arXiv* | 2024.08.24 | [Paper Link](https://arxiv.org/pdf/2408.13597)
34. Enhancing LLM-Based Automated Program Repair with Design Rationales | *ASE* | 2024.08.22 | [Paper Link](https://arxiv.org/pdf/2408.12056)
35. RePair: Automated Program Repair with Process-based Feedback | *ACL Findings* | 2024.08.21 | [Paper Link](https://arxiv.org/pdf/2408.11296)
36. Revisiting Evolutionary Program Repair via Code Language Model | *arXiv* | 2024.08.20 | [Paper Link](https://arxiv.org/pdf/2408.10486)
37. ThinkRepair: Self-Directed Automated Program Repair | *ACM SIGSOFT International Symposium on Software Testing and Analysis* | 2024.07.30 | [Paper Link](https://arxiv.org/pdf/2407.20898)
38. Automated C/C++ Program Repair for High-Level Synthesis via Large Language Models | *ACM/IEEE International Symposium on Machine Learning for CAD* | 2024.07.04 | [Paper Link](https://arxiv.org/pdf/2407.03889)
39. Hybrid Automated Program Repair by Combining Large Language Models and Program Analysis | *arXiv* | 2024.06.04 | [Paper Link](https://arxiv.org/pdf/2406.00992)
40. A Case Study of LLM for Automated Vulnerability Repair: Assessing Impact of Reasoning and Patch Validation Feedback | *Proceedings of the 1st ACM International Conference on AI-Powered Software* | 2024.05.24 | [Paper Link](https://arxiv.org/pdf/2405.15690)
41. Automated Repair of AI Code with Large Language Models and Formal Verification | *arXiv* | 2024.05.14 | [Paper Link](https://arxiv.org/pdf/2405.08848)
42. A Systematic Literature Review on Large Language Models for Automated Program Repair | *arXiv* | 2024.05.12 | [Paper Link](https://arxiv.org/pdf/2405.01466)
43. Revisiting Unnaturalness for Automated Program Repair in the Era of Large Language Models | *arXiv* | 2024.03.23 | [Paper Link](https://arxiv.org/pdf/2404.15236)
44. How Far Can We Go with Practical Function-Level Program Repair? | *arXiv* | 2024.04.19 | [Paper Link](https://arxiv.org/pdf/2404.12833)
45. Multi-Objective Fine-Tuning for Enhanced Program Repair with LLMs | *arXiv* | 2024.04.22 | [Paper Link](https://arxiv.org/pdf/2404.12636)
46. Aligning LLMs for FL-free Program Repair | *arXiv* | 2024.04.13 | [Paper Link](https://arxiv.org/pdf/2404.08877)
47. When Large Language Models Confront Repository-Level Automatic Program Repair: How Well They Done? | *ICSE* | 2023.03.01 | [Paper Link](https://arxiv.org/abs/2403.00448)
48. ContrastRepair: Enhancing Conversation-Based Automated Program Repair via Contrastive Test Case Pairs | *arXiv* | 2024.03.07 | [Paper Link](https://arxiv.org/abs/2403.01971)
49. LLM-Powered Code Vulnerability Repair with Reinforcement Learning and Semantic Reward | *arXiv* | 2024.02.22 | [Paper Link](https://arxiv.org/abs/2401.03374)
50. Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair | *ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering* | 2023.11.08 | [Paper Link](https://arxiv.org/abs/2309.00608)
51. Better Patching Using LLM Prompting, via Self-Consistency | *ASE* | 2023.08.16 | [Paper Link](https://arxiv.org/abs/2306.00108)
52. Teaching Large Language Models to Self-Debug | *ICLR* | 2023.10.05 | [Paper Link](https://arxiv.org/abs/2304.05128)
53. Enhanced Automated Code Vulnerability Repair using Large Language Models | *Eng. Appl. Artif. Intell.* | 2024.01.08 | [Paper Link](https://arxiv.org/abs/2401.03741)
54. A Study of Vulnerability Repair in JavaScript Programs with Large Language Models | *WWW* | 2023.03.19 | [Paper Link](https://arxiv.org/abs/2403.13193)
55. Fixing Hardware Security Bugs with Large Language Models | *arXiv* | 2023.02.02 | [Paper Link](https://arxiv.org/abs/2302.01215)
56. DIVAS: An LLM-based End-to-End Framework for SoC Security Analysis and Policy-based Protection | *arXiv* | 2023.08.14 | [Paper Link](https://arxiv.org/abs/2308.06932)
57. ZeroLeak: Using LLMs for Scalable and Cost Effective Side-Channel Patching | *arXiv* | 2023.08.24 | [Paper Link](https://arxiv.org/abs/2308.13062)
58. InferFix: End-to-End Program Repair with LLMs | *ESEC/FSE* | 2023.03.13 | [Paper Link](https://arxiv.org/abs/2303.07263)
59. Can LLMs Patch Security Issues? | *arXiv* | 2024.02.19 | [Paper Link](https://arxiv.org/abs/2312.00024)
60. How Effective Are Neural Networks for Fixing Security Vulnerabilities | *ISSTA* | 2023.05.29 | [Paper Link](https://arxiv.org/abs/2305.18607)
61. Examining Zero-Shot Vulnerability Repair with Large Language Models | *SP* | 2022.08.15 | [Paper Link](https://arxiv.org/abs/2112.02125)
62. Security Code Review by LLMs: A Deep Dive into Responses | *arXiv* | 2024.01.29 | [Paper Link](https://arxiv.org/abs/2401.16310)
63. Practical Program Repair in the Era of Large Pre-trained Language Models | *arXiv* | 2022.10.25 | [Paper Link](https://arxiv.org/abs/2210.14179)
64. AI-powered patching: the future of automated vulnerability fixes | *google* | 2024.01.31 | [Paper Link](https://research.google/pubs/ai-powered-patching-the-future-of-automated-vulnerability-fixes/)
65. An Analysis of the Automatic Bug Fixing Performance of ChatGPT | *APR@ICSE* | 2023.01.20 | [Paper Link](https://arxiv.org/abs/2301.08653)
66. Automatic Program Repair with OpenAI's Codex: Evaluating QuixBugs | *arXiv* | 2023.11.06 | [Paper Link](https://arxiv.org/abs/2111.03922)
#### Insecure code Generation
> Since this part has evolved to focus more on Code LLM research, it is no longer actively maintained.
1. Benchmarking Prompt Engineering Techniques for Secure Code Generation with GPT Models | *arXiv* | 2025.02.09 | [Paper Link](https://arxiv.org/pdf/2502.06039)
2. ContractTinker: LLM-Empowered Vulnerability Repair for Real-World Smart Contracts | *arXiv* | 2024.09.15 | [Paper Link](https://arxiv.org/pdf/2409.09661)
3. An Exploratory Study on Fine-Tuning Large Language Models for Secure Code Generation | *arXiv* | 2024.08.17 | [Paper Link](https://arxiv.org/pdf/2408.09078)
4. Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval | *arXiv* | 2024.07.04 | [Paper Link](https://arxiv.org/pdf/2407.02395)
5. DistiLRR: Transferring Code Repair for Low-Resource Programming Languages | *arXiv* | 2024.06.20 | [Paper Link](https://arxiv.org/pdf/2406.14867)
6. Code Repair with LLMs gives an Exploration-Exploitation Tradeoff | *arXiv* | 2024.05.30 | [Paper Link](https://arxiv.org/pdf/2405.17503)
7. LLM Security Guard for Code | *International Conference on Evaluation and Assessment in Software Engineering* | 2024.05.03 | [Paper Link](https://arxiv.org/pdf/2405.01103)
8. Do Neutral Prompts Produce Insecure Code? FormAI-v2 Dataset: Labelling Vulnerabilities in Code Generated by Large Language Models | *arXiv* | 2024.04.29 | [Paper Link](https://arxiv.org/pdf/2404.18353)
9. Evolutionary Large Language Models for Hardware Security: A Comparative Survey | *arXiv* | 2024.04.25 | [Paper Link](https://arxiv.org/abs/2404.16651)
10. FLAG: Finding Line Anomalies (in code) with Generative AI | *arXiv* | 2023.07.22 | [Paper Link](https://arxiv.org/abs/2306.12643)
11. Make LLM a Testing Expert: Bringing Human-like Interaction to Mobile GUI Testing via Functionality-aware Decisions | *ICSE* | 2023.10.24 | [Paper Link](https://arxiv.org/abs/2310.15780)
12. DebugBench: Evaluating Debugging Capability of Large Language Models | *ACL Findings* | 2024.01.11 | [Paper Link](https://arxiv.org/abs/2401.04621)
13. Shifting the Lens: Detecting Malware in npm Ecosystem with Large Language Models | *arXiv* | 2024.03.18 | [Paper Link](https://arxiv.org/abs/2403.12196)
14. Using ChatGPT to Analyze Ransomware Messages and to Predict Ransomware Threats | *Research Square* | 2023.11.21 | [Paper Link](https://assets.researchsquare.com/files/rs-3645967/v1_covered_a2d4c021-581c-44a3-ba60-058002d65bf9.pdf)
15. Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4 | *arXiv* | 2023.12.13 | [Paper Link](https://arxiv.org/abs/2312.08317)
16. Evaluating and Explaining Large Language Models for Code Using Syntactic Structures | *arXiv* | 2023.08.07 | [Paper Link](https://arxiv.org/abs/2308.03873)
17. Understanding Programs by Exploiting (Fuzzing) Test Cases | *ACL Findings* | 2023.01.12 | [Paper Link](https://arxiv.org/abs/2305.13592)
18. Large Language Models for Code Analysis: Do LLMs Really Do Their Job? | *USENIX* | 2024.03.05 | [Paper Link](https://arxiv.org/abs/2310.12357)
19. LLM4Decompile: Decompiling Binary Code with Large Language Models | *EMNLP* | 2024.03.08 | [Paper Link](https://arxiv.org/abs/2403.05286)
20. Pop Quiz! Can a Large Language Model Help With Reverse Engineering? | *arXiv* | 2022.02.02 | [Paper Link](https://arxiv.org/abs/2202.01142)
21. Large Language Models for Code: Security Hardening and Adversarial Testing | *ACM SIGSAC Conference on Computer and Communications Security* | 2023.09.29 | [Paper Link](https://arxiv.org/abs/2302.05319)
22. How Secure is Code Generated by ChatGPT? | *SMC* | 2023.04.19 | [Paper Link](https://arxiv.org/abs/2304.09655)
23. A Comparative Study of Code Generation using ChatGPT 3.5 across 10 Programming Languages | *arXiv* | 2023.08.08 | [Paper Link](https://arxiv.org/abs/2308.04477)
24. Can Large Language Models Identify And Reason About Security Vulnerabilities? Not Yet | *arXiv* | 2023.12.19 | [Paper Link](https://arxiv.org/abs/2312.12575)
25. Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation | *NeurIPS* | 2023.10.30 | [Paper Link](https://arxiv.org/abs/2305.01210)
26. Generate and Pray: Using SALLMS to Evaluate the Security of LLM Generated Code | *arXiv* | 2023.11.01 | [Paper Link](https://arxiv.org/abs/2311.00889)
27. No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPT | *IEEE Trans. Software Eng.* | 2023.08.09 | [Paper Link](https://arxiv.org/abs/2308.04838)
28. The Effectiveness of Large Language Models (ChatGPT and CodeBERT) for Security-Oriented Code Analysis | *arXiv* | 2023.08.29 | [Paper Link](https://arxiv.org/abs/2307.12488)
29. Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions | *S&P* | 2021.12.16 | [Paper Link](https://arxiv.org/abs/2108.09293)
30. Bugs in Large Language Models Generated Code | *arXiv* | 2024.03.18 | [Paper Link](https://arxiv.org/abs/2403.08937)
31. Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants | *USENIX* | 2023.02.27 | [Paper Link](https://arxiv.org/abs/2208.09727)
#### LLM Assisted Defense
1. Towards a Cognitive-Support Tool for Threat Hunters | *arxiv* | 2026.01.31 | [Paper Link](https://arxiv.org/pdf/2602.00432)
2. AEGIS: White-Box Attack Path Generation using LLMs and Training Effectiveness Evaluation for Large-Scale Cyber Defence Exercises | *arxiv* | 2026.01.30 | [Paper Link](https://arxiv.org/pdf/2601.22720)
3. User-Centric Phishing Detection: A RAG and LLM-Based Approach | *arxiv* | 2026.01.29 | [Paper Link](https://arxiv.org/pdf/2601.21261)
4. Proactively Detecting Threats: A Novel Approach Using LLMs | *arxiv* | 2026.01.14 | [Paper Link](https://arxiv.org/pdf/2601.09029)
5. A Decompilation-Driven Framework for Malware Detection with Large Language Models | *arxiv* | 2026.01.14 | [Paper Link](https://arxiv.org/pdf/2601.09035)
6. SecureCAI: Injection-Resilient LLM Assistants for Cybersecurity Operations | *arxiv* | 2026.01.12 | [Paper Link](https://arxiv.org/pdf/2601.07835)
7. LLM-PEA: Leveraging Large Language Models Against Phishing Email Attacks | *arxiv* | 2025.12.10 | [Paper Link](https://arxiv.org/pdf/2512.10104)
8. Improving Phishing Resilience with AI-Generated Training: Evidence on Prompting, Personalization, and Duration | *arxiv* | 2025.12.01 | [Paper Link](https://arxiv.org/pdf/2512.01893)
9. Can MLLMs Detect Phishing? A Comprehensive Security Benchmark Suite Focusing on Dynamic Threats and Multimodal Evaluation in Academic Environments | *arxiv* | 2025.11.22 | [Paper Link](https://arxiv.org/pdf/2511.15165)
10. Large Language Model-Based Reward Design for Deep Reinforcement Learning-Driven Autonomous Cyber Defense | *arxiv* | 2025.11.20 | [Paper Link](https://arxiv.org/pdf/2511.16483)
11. How Can We Effectively Use LLMs for Phishing Detection?: Evaluating the Effectiveness of Large Language Model-based Phishing Detection Models | *arxiv* | 2025.11.14 | [Paper Link](https://arxiv.org/pdf/2511.09606)
12. MalCVE: Malware Detection and CVE Association Using Large Language Models | *arxiv* | 2025.10.17 | [Paper Link](https://arxiv.org/pdf/2510.15567)
13. RHINO: Guided Reasoning for Mapping Network Logs to Adversarial Tactics and Techniques with Large Language Models | *arxiv* | 2025.10.16 | [Paper Link](https://arxiv.org/pdf/2510.14233)
14. A Systematic Study on Generating Web Vulnerability Proof-of-Concepts Using Large Language Models | *arxiv* | 2025.10.11 | [Paper Link](https://arxiv.org/pdf/2510.10148)
15. VelLMes: A high-interaction AI-based deception framework | *arxiv* | 2025.10.08 | [Paper Link](https://arxiv.org/pdf/2510.06975)
16. "Leveraging Large Language Models for Cybersecurity Risk Assessment -- A Case from Forestry Cyber-Physical Systems
17. " | *arxiv* | 2025.10.07 | [Paper Link](https://arxiv.org/pdf/2510.06343)
18. Memory-Augmented Log Analysis with Phi-4-mini: Enhancing Threat Detection in Structured Security Logs | *arxiv* | 2025.10.01 | [Paper Link](https://arxiv.org/pdf/2510.00529)
19. Benchmarking LLM-Assisted Blue Teaming via Standardized Threat Hunting | *arxiv* | 2025.10.01 | [Paper Link](https://arxiv.org/pdf/2509.23571)
20. Evaluating LLM Generated Detection Rules in Cybersecurity | *arxiv* | 2025.09.20 | [Paper Link](https://arxiv.org/pdf/2509.16749)
21. ATLANTIS: AI-driven Threat Localization, Analysis, and Triage Intelligence System | *arxiv* | 2025.09.18 | [Paper Link](https://arxiv.org/pdf/2509.14589)
22. BEACON: Behavioral Malware Classification with Large Language Model Embeddings and Deep Learning | *arxiv* | 2025.09.18 | [Paper Link](https://arxiv.org/pdf/2509.14519)
23. RationAnomaly: Log Anomaly Detection with Rationality via Chain-of-Thought and Reinforcement Learning | *arxiv* | 2025.09.18 | [Paper Link](https://arxiv.org/pdf/2509.14693)
24. Beyond Classification: Evaluating LLMs for Fine-Grained Automatic Malware Behavior Auditin | *arxiv* | 2025.09.17 | [Paper Link](https://arxiv.org/pdf/2509.14335)
25. TraceRAG: A LLM-Based Framework for Explainable Android Malware Detection and Behavior Analysis | *arxiv* | 2025.09.10 | [Paper Link](https://arxiv.org/pdf/2509.08865)
26. AgentSentinel: An End-to-End and Real-Time Security Defense Framework for Computer-Use Agents | *arxiv* | 2025.09.09 | [Paper Link](https://arxiv.org/pdf/2509.07764)
27. LLM-driven Provenance Forensics for Threat Investigation and Detection | *arxiv* | 2025.08.29 | [Paper Link](https://arxiv.org/pdf/2508.21323)
28. FALCON: Autonomous Cyber Threat Intelligence Mining with LLMs for IDS Rule Generation | *arxiv* | 2025.08.26 | [Paper Link](https://arxiv.org/pdf/2508.18684)
29. Chimera: Harnessing Multi-Agent LLMs for Automatic Insider Threat Simulation | *arxiv* | 2025.08.11 | [Paper Link](https://arxiv.org/pdf/2508.07745)
30. Think Broad, Act Narrow: CWE Identification with Multi-Agent Large Language Models | *arxiv* | 2025.08.02 | [Paper Link](https://arxiv.org/pdf/2508.01451)
31. OFCnetLLM: Large Language Model for Network Monitoring and Alertness | *arxiv* | 2025.07.30 | [Paper Link](https://arxiv.org/pdf/2507.22711)
32. Large Language Model-Based Framework for Explainable Cyberattack Detection in Automatic Generation Control Systems | *arxiv* | 2025.07.29 | [Paper Link](https://arxiv.org/pdf/2507.22239)
33. From Alerts to Intelligence: A Novel LLM-Aided Framework for Host-based Intrusion Detection | *arxiv* | 2025.07.15 | [Paper Link](https://arxiv.org/pdf/2507.10873)
34. Can Large Language Models Improve Phishing Defense? A Large-Scale Controlled Experiment on Warning Dialogue Explanations | *arxiv* | 2025.07.10 | [Paper Link](https://arxiv.org/pdf/2507.07916)
35. Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions | *arxiv* | 2025.07.07 | [Paper Link](https://arxiv.org/pdf/2507.04752)
36. Adaptive Linguistic Prompting (ALP) Enhances Phishing Webpage Detection in Multimodal Large Language Models | *arxiv* | 2025.06.29 | [Paper Link](https://arxiv.org/pdf/2507.13357)
37. Leveraging Large Language Model for Intelligent Log Processing and Autonomous Debugging in Cloud AI Platforms | *arxiv* | 2025.06.22 | [Paper Link](https://arxiv.org/pdf/2506.17900)
38. SmartGuard: Leveraging Large Language Models for Network Attack Detection through Audit Log Analysis and Summarization | *arxiv* | 2025.06.20 | [Paper Link](https://arxiv.org/pdf/2506.16981)
39. PhishDebate: An LLM-Based Multi-Agent Framework for Phishing Website Detection | *arxiv* | 2025.06.18 | [Paper Link](https://arxiv.org/pdf/2506.15656)
40. LLM-Powered Intent-Based Categorization of Phishing Emails | *arxiv* | 2025.06.17 | [Paper Link](https://arxiv.org/pdf/2506.14337)
41. Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability | *arxiv* | 2025.06.16 | [Paper Link](https://arxiv.org/pdf/2506.13746)
42. Training RL Agents for Multi-Objective Network Defense Tasks | *arxiv* | 2025.06.13 | [Paper Link](https://arxiv.org/pdf/2505.22531)
43. A Unified Framework for Human AI Collaboration in Security Operations Centers with Trusted Autonomy | *arxiv* | 2025.06.01 | [Paper Link](https://arxiv.org/pdf/2505.23397)
44. MultiPhishGuard: An LLM-based Multi-Agent System for Phishing Email Detection | *arxiv* | 2025.05.27 | [Paper Link](https://arxiv.org/pdf/2505.23803)
45. IRCopilot: Automated Incident Response with Large Language Models | *arxiv* | 2025.05.27 | [Paper Link](https://arxiv.org/pdf/2505.20945)
46. LLM-Driven APT Detection for 6G Wireless Networks: A Systematic Review and Taxonomy | *arxiv* | 2025.05.24 | [Paper Link](https://arxiv.org/pdf/2505.18846)
47. Benchmarking LLMs in an Embodied Environment for Blue Team Threat Hlunting | *arxiv* | 2025.05.17 | [Paper Link](https://arxiv.org/pdf/2505.11901)
48. Automating Security Audit Using Large Language Model based Agent: An Exploration Experiment | *arxiv* | 2025.05.16 | [Paper Link](https://arxiv.org/pdf/2505.10732)
49. On Technique Identification and Threat-Actor Attribution using LLMs and Embedding Models | *arxiv* | 2025.05.15 | [Paper Link](https://arxiv.org/pdf/2505.11547)
50. Towards AI-Driven Human-Machine Co-Teaming for Adaptive and Agile Cyber Security Operation Centers | *arxiv* | 2025.05.09 | [Paper Link](https://arxiv.org/pdf/2505.06394)
51. Large Language Models are Autonomous Cyber Defenders | *arxiv* | 2025.05.08 | [Paper Link](https://arxiv.org/pdf/2505.04843)
52. Bridging Expertise Gaps: The Role of LLMs in Human-AI Collaboration for Cybersecurity | *arxiv* | 2025.05.06 | [Paper Link](https://arxiv.org/pdf/2505.03179)
53. LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems | *arxiv* | 2025.05.01 | [Paper Link](https://arxiv.org/pdf/2505.00240)
54. Improving Phishing Email Detection Performance of Small Large Language Models | *arxiv* | 2025.04.29 | [Paper Link](https://arxiv.org/pdf/2505.00034)
55. AnomalyGen: An Automated Semantic Log Sequence Generation Framework with LLM for Anomaly Detection | *arxiv* | 2025.04.16 | [Paper Link](https://arxiv.org/pdf/2504.12250)
56. Investigating cybersecurity incidents using large language models in latest-generation wireless networks | *arxiv* | 2025.04.14 | [Paper Link](https://arxiv.org/pdf/2504.13196)
57. SoK: LLM-based Log Parsing | *arxiv* | 2025.04.07 | [Paper Link](https://arxiv.org/pdf/2504.04877)
58. Knowledge Transfer from LLMs to Provenance Analysis: A Semantic-Augmented Method for APT Detection | *arxiv* | 2025.03.24 | [Paper Link](https://arxiv.org/pdf/2503.18316)
59. Large Language Models powered Network Attack Detection: Architecture, Opportunities and Case Study | *arxiv* | 2025.03.24 | [Paper Link](https://arxiv.org/pdf/2503.18487)
60. Payload-Aware Intrusion Detection with CMAE and Large Language Models | *arxiv* | 2025.03.23 | [Paper Link](https://arxiv.org/pdf/2503.20798)
61. RedChronos: A Large Language Model-Based Log Analysis System for Insider Threat Detection in Enterprises | *arxiv* | 2025.03.05 | [Paper Link](https://arxiv.org/pdf/2503.02702)
62. Enhancing Cybersecurity in Critical Infrastructure with LLM-Assisted Explainable IoT Systems | *arxiv* | 2025.03.05 | [Paper Link](https://arxiv.org/pdf/2503.03180)
63. Transforming Cyber Defense: Harnessing Agentic and Frontier AI for Proactive, Ethical Threat Intelligence | *arxiv* | 2025.02.28 | [Paper Link](https://arxiv.org/pdf/2503.00164)
64. Cyber Defense Reinvented: Large Language Models as Threat Intelligence Copilots | *arXiv* | 2025.02.28 | [Paper Link](https://arxiv.org/pdf/2502.20791)
65. Design and implementation of a distributed security threat detection system integrating federated learning and multimodal LLM | *arXiv* | 2025.02.28 | [Paper Link](https://arxiv.org/pdf/2502.17763)
66. LAMD: Context-driven Android Malware Detection and Classification with LLMs | *arXiv* | 2025.02.18 | [Paper Link](https://arxiv.org/pdf/2502.13055)
67. APT-LLM: Embedding-Based Anomaly Detection of Cyber Advanced Persistent Threats Using Large Language Models | *arXiv* | 2025.02.13 | [Paper Link](https://arxiv.org/pdf/2502.09385)
68. AdaPhish: AI-Powered Adaptive Defense and Education Resource Against Deceptive Emails | *arXiv* | 2025.02.05 | [Paper Link](https://arxiv.org/pdf/2502.03622)
69. SHIELD: APT Detection and Intelligent Explanation Using LLM | *arXiv* | 2025.02.04 | [Paper Link](https://arxiv.org/pdf/2502.02342)
70. LLM-based event log analysis techniques: A survey | *arXiv* | 2025.02.02 | [Paper Link](https://arxiv.org/pdf/2502.00677)
71. TORCHLIGHT: Shedding LIGHT on Real-World Attacks on Cloudless IoT Devices Concealed within the Tor Network | *arXiv* | 2025.01.28 | [Paper Link](https://arxiv.org/pdf/2501.16784)
72. Confront Insider Threat: Precise Anomaly Detection in Behavior Logs Based on LLM Fine-Tuning | *COLING* | 2024 | [Paper Link](https://aclanthology.org/2025.coling-main.574/)
73. Exploring Large Language Models for Semantic Analysis and Categorization of Android Malware | *arXiv* | 2025.01.08 | [Paper Link](https://arxiv.org/pdf/2501.04848)
74. Large Multimodal Agents for Accurate Phishing Detection with Enhanced Token Optimization and Cost Reduction | *arXiv* | 2024.12.03 | [Paper Link](https://arxiv.org/pdf/2412.02301)
75. LogLM: From Task-based to Instruction-based Automated Log Analysis | *arXiv* | 2024.10.12 | [Paper Link](https://arxiv.org/pdf/2410.09352)
76. LogLLM: Log-based Anomaly Detection Using Large Language Models | *arXiv* | 2024.11.13 | [Paper Link](https://arxiv.org/pdf/2411.08561)
77. Using Large Language Models for Template Detection from Security Event Logs | *arXiv* | 2024.09.08 | [Paper Link](https://arxiv.org/pdf/2409.05045)
78. A Comparative Study on Large Language Models for Log Parsing | *arXiv* | 2024.09.04 | [Paper Link](https://arxiv.org/pdf/2409.02474)
79. LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models | *arXiv* | 2024.09.03 | [Paper Link](https://arxiv.org/pdf/2409.01909)
80. XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language Model | *arXiv* | 2024.08.27 | [Paper Link](https://arxiv.org/pdf/2408.16021)
81. LogParser-LLM: Advancing Efficient Log Parsing with Large Language Models | *arXiv* | 2024.08.25 | [Paper Link](https://arxiv.org/pdf/2408.13727)
82. Automated Phishing Detection Using URLs and Webpages | *arXiv* | 2024.08.16 | [Paper Link](https://arxiv.org/pdf/2408.01667)
83. Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey | *arXiv* | 2024.08.14 | [Paper Link](https://arxiv.org/pdf/2408.07583)
84. Multimodal Large Language Models for Phishing Webpage Detection and Identification | *arXiv* | 2024.08.12 | [Paper Link](https://arxiv.org/pdf/2408.05941)
85. Utilizing Large Language Models to Optimize the Detection and Explainability of Phishing Websites | *arXiv* | 2024.08.11 | [Paper Link](https://arxiv.org/pdf/2408.05667)
86. Towards Explainable Network Intrusion Detection using Large Language Models | *arXiv* | 2024.08.08 | [Paper Link](https://arxiv.org/pdf/2408.04342)
87. Audit-LLM: Multi-Agent Collaboration for Log-based Insider Threat Detection | *arXiv* | 2024.07.12 | [Paper Link](https://arxiv.org/pdf/2408.08902)
88. LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log Analysis | *arXiv* | 2024.07.02 | [Paper Link](https://arxiv.org/pdf/2407.01896)
89. Defending Against Social Engineering Attacks in the Age of LLMs | *EMNLP* | 2024.06.18 | [Paper Link](https://arxiv.org/pdf/2406.12263)
90. Anomaly Detection on Unstable Logs with GPT Models | *arXiv* | 2024.06.11 | [Paper Link](https://arxiv.org/pdf/2406.07467)
91. ULog: Unsupervised Log Parsing with Large Language Models through Log Contrastive Units | *arXiv* | 2024.06.11 | [Paper Link](https://arxiv.org/pdf/2406.07174)
92. Generative AI-in-the-loop: Integrating LLMs and GPTs into the Next Generation Networks | *arXiv* | 2024.06.06 | [Paper Link](https://arxiv.org/pdf/2406.04276)
93. Log Parsing with Self-Generated In-Context Learning and Self-Correction | *arXiv* | 2024.06.05 | [Paper Link](https://arxiv.org/pdf/2406.03376)
94. Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection | *arXiv* | 2024.05.17 | [Paper Link](https://arxiv.org/pdf/2405.11002)
95. DoLLM: How Large Language Models Understanding Network Flow Data to Detect Carpet Bombing DDoS | *arXiv* | 2024.05.12 | [Paper Link](https://arxiv.org/pdf/2405.07638)
96. LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing | *ICSE* | 2024.04.27 | [Paper Link](https://arxiv.org/pdf/2404.18001)
97. Large Language Models Spot Phishing Emails with Surprising Accuracy: A Comparative Analysis of Performance | *arXiv* | 2024.04.23 | [Paper Link](http://arxiv.org/abs/2404.15485)
98. ChatGPT for digital forensic investigation: The good, the bad, and the unknown | *Forensic Science International: Digital Investigation* | 2023.07.10 | [Paper Link](https://arxiv.org/abs/2307.10195)
99. HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs) | *arXiv* | 2023.09.27 | [Paper Link](https://arxiv.org/abs/2309.16021)
100. Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices | *IEEE Access* | 2024.02.08 | [Paper Link](https://ieeexplore.ieee.org/document/10423646)
101. Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection | *arXiv* | 2023.10.30 | [Paper Link](https://arxiv.org/abs/2310.19658)
102. Devising and Detecting Phishing: Large Language Models vs. Smaller Human Models | *arXiv* | 2023.11.30 | [Paper Link](https://arxiv.org/abs/2308.12287)
103. Prompted Contextual Vectors for Spear-Phishing Detection | *arXiv* | 2024.02.14 | [Paper Link](https://arxiv.org/abs/2402.08309)
104. Evaluating the Performance of ChatGPT for Spam Email Detection | *arXiv* | 2024.02.23 | [Paper Link](https://arxiv.org/abs/2402.15537)
105. An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approach | *arXiv* | 2023.11.12 | [Paper Link](https://arxiv.org/abs/2311.04913)
106. Application of Large Language Models to DDoS Attack Detection | *International Conference on Security and Privacy in Cyber-Physical Systems and Smart Vehicles* | 2024.02.05 | [Paper Link](https://link.springer.com/chapter/10.1007/978-3-031-51630-6_6)
107. Web Content Filtering through knowledge distillation of Large Language Models | *WI-IAT* | 2023.05.10 | [Paper Link](https://arxiv.org/abs/2305.05027)
108. Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging | *arXiv* | 2024.03.02 | [Paper Link](https://arxiv.org/abs/2402.18205)
109. Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies | *ICPC* | 2024.01.26 | [Paper Link](https://arxiv.org/abs/2308.07610)
110. LogGPT: Log Anomaly Detection via GPT | *BigData* | 2023.12.11 | [Paper Link](https://arxiv.org/abs/2309.14482)
111. LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection | *HPCC/DSS/SmartCity/DependSys* | 2023.09.14 | [Paper Link](https://arxiv.org/abs/2309.01189)
112. Log-based Anomaly Detection based on EVT Theory with feedback | *arXiv* | 2023.09.30 | [Paper Link](https://arxiv.org/abs/2306.05032)
113. Benchmarking Large Language Models for Log Analysis, Security, and Interpretation | *J. Netw. Syst. Manag.* | 2023.11.24 | [Paper Link](https://arxiv.org/abs/2311.14519)
#### LLM Assisted Attack
1. Lightweight LLMs for Network Attack Detection in IoT Networks | *arxiv* | 2026.01.21 | [Paper Link](https://arxiv.org/pdf/2601.15269)
2. When Bots Take the Bait: Exposing and Mitigating the Emerging Social Engineering Attack in Web Automation Agent | *arxiv* | 2026.01.12 | [Paper Link](https://arxiv.org/pdf/2601.07263)
3. PenForge: On-the-Fly Expert Agent Construction for Automated Penetration Testing | *arxiv* | 2026.01.11 | [Paper Link](https://arxiv.org/pdf/2601.06910)
4. Cybersecurity AI: A Game-Theoretic AI for Guiding Attack and Defense | *arxiv* | 2026.01.09 | [Paper Link](https://arxiv.org/pdf/2601.05887)
5. PentestEval: Benchmarking LLM-based Penetration Testing with Modular and Stage-Level Design | *arxiv* | 2025.12.16 | [Paper Link](https://arxiv.org/pdf/2512.14233)
6. The Role of AI in Modern Penetration Testing | *arxiv* | 2025.12.13 | [Paper Link](https://arxiv.org/pdf/2512.12326)
7. Automated Penetration Testing with LLM Agents and Classical Planning | *arxiv* | 2025.12.11 | [Paper Link](https://arxiv.org/pdf/2512.11143)
8. Comparing AI Agents to Cybersecurity Professionals in Real-World Penetration Testing | *arxiv* | 2025.12.10 | [Paper Link](https://arxiv.org/pdf/2512.09882)
9. Comparing AI Agents to Cybersecurity Professionals in Real-World Penetration Testing | *arxiv* | 2025.12.10 | [Paper Link](https://arxiv.org/abs/2512.09882)
10. Genesis: Evolving Attack Strategies for LLM Web Agent Red-Teaming | *arxiv* | 2025.10.21 | [Paper Link](https://arxiv.org/pdf/2510.18314)
11. AutoPentester: An LLM Agent-based Framework for Automated Pentesting | *arxiv* | 2025.10.07 | [Paper Link](https://arxiv.org/pdf/2510.05605)
12. SoK: Potentials and Challenges of Large Language Models for Reverse Engineering | *arxiv* | 2025.09.26 | [Paper Link](https://arxiv.org/pdf/2509.21821)
13. From Capabilities to Performance: Evaluating Key Functional Properties of LLM Architectures in Penetration Testing | *arxiv* | 2025.09.16 | [Paper Link](https://arxiv.org/pdf/2509.14289)
14. From Firewalls to Frontiers: AI Red-Teaming is a Domain-Specific Evolution of Cyber Red-Teaming | *arxiv* | 2025.09.14 | [Paper Link](https://arxiv.org/pdf/2509.11398)
15. Guided Reasoning in LLM-Driven Penetration Testing Using Structured Attack Trees | *arxiv* | 2025.09.09 | [Paper Link](https://arxiv.org/pdf/2509.07939)
16. Cybersecurity AI: Hacking the AI Hackers via Prompt Injection | *arxiv* | 2025.09.01 | [Paper Link](https://arxiv.org/pdf/2508.21669)
17. An Automated Attack Investigation Approach Leveraging Threat-Knowledge-Augmented Large Language Models | *arxiv* | 2025.09.01 | [Paper Link](https://arxiv.org/pdf/2509.01271)
18. An Automated Attack Investigation Approach Leveraging Threat-Knowledge-Augmented Large Language Models | *arxiv* | 2025.09.01 | [Paper Link](https://arxiv.org/pdf/2509.01271)
19. Cybersecurity AI: Hacking the AI Hackers via Prompt Injection | *arxiv* | 2025.09.01 | [Paper Link](https://arxiv.org/pdf/2508.21669)
20. SoK: Large Language Model-Generated Textual Phishing Campaigns End-to-End Analysis of Generation, Characteristics, and Detection | *arxiv* | 2025.08.29 | [Paper Link](https://arxiv.org/pdf/2508.21457)
21. Pentest-R1: Towards Autonomous Penetration Testing Reasoning Optimized via Two-Stage Reinforcement Learning | *arxiv* | 2025.08.10 | [Paper Link](https://arxiv.org/pdf/2508.07382)
22. PenTest2.0: Towards Autonomous Privilege Escalation Using GenAI | *arxiv* | 2025.08.09 | [Paper Link](https://arxiv.org/pdf/2507.06742)
23. Prompt to Pwn: Automated Exploit Generation for Smart Contracts | *arxiv* | 2025.08.02 | [Paper Link](https://arxiv.org/pdf/2508.01371)
24. Can We End the Cat-and-Mouse Game? Simulating Self-Evolving Phishing Attacks with LLMs and Genetic Algorithms | *arxiv* | 2025.07.29 | [Paper Link](https://arxiv.org/pdf/2507.21538)
25. Exploiting Jailbreaking Vulnerabilities in Generative AI to Bypass Ethical Safeguards for Facilitating Phishing Attacks | *arxiv* | 2025.07.16 | [Paper Link](https://arxiv.org/pdf/2507.12185)
26. LLMalMorph: On The Feasibility of Generating Variant Malware using Large-Language-Models | *arxiv* | 2025.07.13 | [Paper Link](https://arxiv.org/pdf/2507.09411)
27. On the Surprising Efficacy of LLMs for Penetration-Testing | *arxiv* | 2025.07.01 | [Paper Link](https://arxiv.org/pdf/2507.00829)
28. From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs | *arxiv* | 2025.06.16 | [Paper Link](https://arxiv.org/pdf/2506.13434)
29. On the Ethics of Using LLMs for Offensive Security | *arxiv* | 2025.06.10 | [Paper Link](https://arxiv.org/pdf/2506.08693)
30. ReCopilot: Reverse Engineering Copilot in Binary Analysis | *arxiv* | 2025.05.22 | [Paper Link](https://arxiv.org/pdf/2505.16366)
31. LLMs unlock new paths to monetizing exploits | *arxiv* | 2025.05.16 | [Paper Link](https://arxiv.org/pdf/2505.11449)
32. AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agents | *arxiv* | 2025.05.15 | [Paper Link](https://arxiv.org/pdf/2505.10321)
33. Offensive Security for AI Systems: Concepts, Practices, and Applications | *arxiv* | 2025.05.09 | [Paper Link](https://arxiv.org/pdf/2505.06380)
34. Weaponizing Language Models for Cybersecurity Offensive Operations: Automating Vulnerability Assessment Report Validation; A Review Paper | *arxiv* | 2025.05.07 | [Paper Link](https://arxiv.org/pdf/2505.04265)
35. PwnGPT: Automatic Exploit Generation Based on Large Language Models | *ACL* | 2025.04 | [Paper Link](https://aclanthology.org/2025.acl-long.562.pdf)
36. On the Feasibility of Using MultiModal LLMs to Execute AR Social Engineering Attacks | *arxiv* | 2025.04.16 | [Paper Link](https://arxiv.org/pdf/2504.13209)
37. Benchmarking Practices in LLM-driven Offensive Security: Testbeds, Metrics, and Experiment Design | *arxiv* | 2025.04.14 | [Paper Link](https://arxiv.org/pdf/2504.10112)
38. Red Teaming with Artificial Intelligence-Driven Cyberattacks: A Scoping Review | *arxiv* | 2025.03.25 | [Paper Link](https://arxiv.org/pdf/2503.19626)
39. A Framework for Evaluating Emerging Cyberattack Capabilities of AI | *arxiv* | 2025.03.15 | [Paper Link](https://arxiv.org/pdf/2503.11917)
40. Jailbreaking Generative AI: Empowering Novices to Conduct Phishing Attacks | *arxiv* | 2025.03.03 | [Paper Link](https://arxiv.org/pdf/2503.01395)
41. CAI: An Open, Bug Bounty-Ready Cybersecurity AI | *arXiv* | 2025.04.15 | [Paper Link](https://arxiv.org/abs/2504.06017)
42. RapidPen: Fully Automated IP-to-Shell Penetration Testing with LLM-based Agents | *arXiv* | 2025.02.23 | [Paper Link](https://arxiv.org/pdf/2502.16730)
43. Construction and Evaluation of LLM-based agents for Semi-Autonomous penetration testing | *arXiv* | 2025.02.21 | [Paper Link](https://arxiv.org/pdf/2502.15506)
44. OCCULT: Evaluating Large Language Models for Offensive Cyber Operation Capabilities | *arXiv* | 2025.02.18 | [Paper Link](https://arxiv.org/pdf/2502.15797)
45. PenTest++: Elevating Ethical Hacking with AI and Automation | *arXiv* | 2025.02.13 | [Paper Link](https://arxiv.org/pdf/2502.09484)
46. Can LLMs Hack Enterprise Networks? Autonomous Assumed Breach Penetration-Testing Active Directory Networks | *arXiv* | 2025.02.06 | [Paper Link](https://arxiv.org/pdf/2502.04227)
47. On the Feasibility of Using LLMs to Execute Multistage Network Attacks | *arXiv* | 2025.01.27 | [Paper Link](https://arxiv.org/pdf/2501.16466)
48. HackSynth: LLM Agent and Evaluation Framework for Autonomous Penetration Testing | *arXiv* | 2024.12.02 | [Paper Link](https://arxiv.org/pdf/2412.01778)
49. Hacking CTFs with Plain Agents | *arXiv* | 2024.12.03 | [Paper Link](https://arxiv.org/pdf/2412.02776)
50. Evaluating and Improving the Robustness of Security Attack Detectors Generated by LLMs | *arXiv* | 2024.11.27 | [Paper Link](https://arxiv.org/pdf/2411.18216)
51. AI-Augmented Ethical Hacking: A Practical Examination of Manual Exploitation and Privilege Escalation in Linux Environments | *arXiv* | 2024.11.26 | [Paper Link](https://arxiv.org/pdf/2411.17539)
52. Next-Generation Phishing: How LLM Agents Empower Cyber Attackers | *arXiv* | 2024.11.22 | [Paper Link](https://arxiv.org/pdf/2411.13874)
53. Adapting to Cyber Threats: A Phishing Evolution Network (PEN) Framework for Phishing Generation and Analyzing Evolution Patterns using Large Language Models | *arXiv* | 2024.11.18 | [Paper Link](https://arxiv.org/pdf/2411.11389)
54. Hacking Back the AI-Hacker: Prompt Injection as a Defense Against LLM-driven Cyberattacks | *arXiv* | 2024.11.18 | [Paper Link](https://arxiv.org/pdf/2410.20911)
55. PentestAgent: Incorporating LLM Agents to Automated Penetration Testing | *arXiv* | 2024.11.07 | [Paper Link](https://arxiv.org/pdf/2411.05185)
56. AutoPT: How Far Are We from the End2End Automated Web Penetration Testing? | *arXiv* | 2024.11.02 | [Paper Link](https://arxiv.org/pdf/2411.01236)
57. AutoPenBench: Benchmarking Generative Agents for Penetration Testing | *arXiv* | 2024.10.28 | [Paper Link](https://arxiv.org/pdf/2410.03225)
58. Towards Automated Penetration Testing: Introducing LLM Benchmark, Analysis, and Improvements | *arXiv* | 2024.10.25 | [Paper Link](https://arxiv.org/pdf/2410.17141)
59. On the Feasibility of Fully AI-automated Vishing Attacks | *arXiv* | 2024.09.20 | [Paper Link](https://arxiv.org/pdf/2409.13793)
60. Hacking, The Lazy Way: LLM Augmented Pentesting | *arXiv* | 2024.09.14 | [Paper Link](https://arxiv.org/pdf/2409.09493)
61. Is Generative AI the Next Tactical Cyber Weapon For Threat Actors? Unforeseen Implications of AI Generated Cyber Attacks | *arXiv* | 2024.08.23 | [Paper Link](https://arxiv.org/pdf/2408.12806)
62. CIPHER: Cybersecurity Intelligent Penetration-testing Helper for Ethical Researcher | *Sensors* | 2024.08.21 | [Paper Link](https://arxiv.org/pdf/2408.11650)
63. Using Retriever Augmented Large Language Models for Attack Graph Generation | *arXiv* | 2024.08.11 | [Paper Link](https://arxiv.org/pdf/2408.05855)
64. Practical Attacks against Black-box Code Completion Engines | *arXiv* | 2024.08.05 | [Paper Link](https://arxiv.org/pdf/2408.02509)
65. PenHeal: A Two-Stage LLM Framework for Automated Pentesting and Optimal Remediation | *Proceedings of the Workshop on Autonomous Cybersecurity* | 2024.07.25 | [Paper Link](https://arxiv.org/pdf/2407.17788)
66. From Sands to Mansions: Enabling Automatic Full-Life-Cycle Cyberattack Construction with LLM | *arXiv* | 2024.07.24 | [Paper Link](https://arxiv.org/pdf/2407.16928)
67. The Shadow of Fraud: The Emerging Danger of AI-powered Social Engineering and its Possible Cure | *arXiv* | 2024.07.22 | [Paper Link](https://arxiv.org/pdf/2407.15912)
68. Tactics, Techniques, and Procedures (TTPs) in Interpreted Malware: A Zero-Shot Generation with Large Language Models | *arXiv* | 2024.07.11 | [Paper Link](https://arxiv.org/pdf/2407.08532)
69. Assessing AI vs Human-Authored Spear Phishing SMS Attacks: An Empirical Study Using the TRAPD Method | *arXiv* | 2024.06.18 | [Paper Link](https://arxiv.org/pdf/2406.13049)
70. Getting pwn’d by AI: Penetration Testing with Large Language Models | *ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering* | 2023.08.17 | [Paper Link](https://arxiv.org/abs/2308.00121)
71. RatGPT: Turning online LLMs into Proxies for Malware Attacks | *arXiv* | 2023.09.07 | [Paper Link](https://arxiv.org/abs/2308.09183)
72. AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks | *arXiv* | 2024.03.02 | [Paper Link](https://arxiv.org/abs/2403.01038)
73. PentestGPT: An LLM-empowered Automatic Penetration Testing Tool | *USENIX* | 2023.08.13 | [Paper Link](https://arxiv.org/abs/2308.06782)
74. From Text to MITRE Techniques: Exploring the Malicious Use of Large Language Models for Generating Cyber Attack Payloads | *arXiv* | 2023.05.24 | [Paper Link](https://arxiv.org/abs/2305.15336)
75. From Chatbots to PhishBots? - Preventing Phishing scams created using ChatGPT, Google Bard and Claude | *arXiv* | 2024.03.10 | [Paper Link](https://arxiv.org/abs/2310.19181)
76. Exploring the Dark Side of AI: Advanced Phishing Attack Design and Deployment Using ChatGPT | *CNS* | 2023.09.19 | [Paper Link](https://arxiv.org/abs/2309.10463)
77. Using Large Language Models for Cybersecurity Capture-The-Flag Challenges and Certification Questions | *arXiv* | 2023.08.21 | [Paper Link](https://arxiv.org/abs/2308.10443)
78. Evaluating LLMs for Privilege-Escalation Scenarios | *arXiv* | 2023.10.23 | [Paper Link](https://arxiv.org/abs/2310.11409)
79. Malla: Demystifying Real-world Large Language Model Integrated Malicious Services | *USENIX* | 2024.01.06 | [Paper Link](https://arxiv.org/abs/2401.03315)
80. LLMs Killed the Script Kiddie: How Agents Supported by Large Language Models Change the Landscape of Network Threat Testing | *arXiv* | 2023.10.10 | [Paper Link](https://arxiv.org/abs/2310.06936)
81. From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy | *IEEE Access* | 2023.07.03 | [Paper Link](https://arxiv.org/abs/2307.00691)
82. Impact of Big Data Analytics and ChatGPT on Cybersecurity | *I3CS* | 2023.05.22 | [Paper Link](https://ieeexplore.ieee.org/document/10127411)
83. Identifying and mitigating the security risks of generative ai | *Foundations and Trends in Privacy and Security* | 2023.12.29 | [Paper Link](https://arxiv.org/abs/2308.14840)
#### Others
1. Towards Cybersecurity Superintelligence: from AI-guided humans to human-guided AI | *arxiv* | 2026.01.21 | [Paper Link](https://arxiv.org/pdf/2601.14614)
2. A cybersecurity AI agent selection and decision support framework | *arxiv* | 2025.10.02 | [Paper Link](https://arxiv.org/pdf/2510.01751)
3. Large Language Models for Security Operations Centers: A Comprehensive Survey | *arxiv* | 2025.09.19 | [Paper Link](https://arxiv.org/pdf/2509.10858)
4. From Legacy to Standard: LLM-Assisted Transformation of Cybersecurity Playbooks into CACAO Format | *arxiv* | 2025.08.05 | [Paper Link](https://arxiv.org/pdf/2508.03342)
5. Information Security Based on LLM Approaches: A Review | *arxiv* | 2025.07.24 | [Paper Link](https://arxiv.org/pdf/2507.18215)
6. Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques | *arxiv* | 2025.07.18 | [Paper Link](https://arxiv.org/pdf/2507.13629)
7. Cybersecurity AI: The Dangerous Gap Between Automation and Autonomy | *arxiv* | 2025.06.30 | [Paper Link](https://arxiv.org/pdf/2506.23592)
8. Using LLMs for Security Advisory Investigations: How Far Are We? | *arxiv* | 2025.06.16 | [Paper Link](https://arxiv.org/pdf/2506.13161)
9. Exposing the Impact of GenAI for Cybercrime: An Investigation into the Dark Side | *arxiv* | 2025.05.29 | [Paper Link](https://arxiv.org/pdf/2505.23733)
10. Large Language Models for IT Automation Tasks: Are We There Yet? | *arxiv* | 2025.05.26 | [Paper Link](https://arxiv.org/pdf/2505.20505)
11. Mitigating Cyber Risk in the Age of Open-Weight LLMs: Policy Gaps and Technical Realities | *arxiv* | 2025.05.21 | [Paper Link](https://arxiv.org/pdf/2505.17109)
12. ACSE-Eval: Can LLMs threat model real-world cloud infrastructure? | *arxiv* | 2025.05.16 | [Paper Link](https://arxiv.org/pdf/2505.11565)
13. LLMs Suitability for Network Security: A Case Study of STRIDE Threat Modeling | *arxiv* | 2025.05.06 | [Paper Link](https://arxiv.org/pdf/2505.04101)
14. From Texts to Shields: Convergence of Large Language Models and Cybersecurity | *arxiv* | 2025.05.01 | [Paper Link](https://arxiv.org/pdf/2505.00841)
15. Automatically Generating Rules of Malicious Software Packages via Large Language Model | *arxiv* | 2025.04.24 | [Paper Link](https://arxiv.org/pdf/2504.17198)
16. Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey | *arxiv* | 2025.04.22 | [Paper Link](https://arxiv.org/pdf/2504.15622)
17. SoK: Frontier AIs Impact on the Cybersecurity Landscape | *arxiv* | 2025.04.07 | [Paper Link](https://arxiv.org/pdf/2504.05408)
18. Emerging Cyber Attack Risks of Medical AI Agents | *arxiv* | 2025.04.02 | [Paper Link](https://arxiv.org/pdf/2504.03759)
19. Inducing Personality in LLM-Based Honeypot Agents: Measuring the Effect on Human-Like Agenda Generation | *arxiv* | 2025.03.25 | [Paper Link](https://arxiv.org/pdf/2503.19752)
20. ChatIoT: Large Language Model-based Security Assistant for Internet of Things with Retrieval-Augmented Generation | *arXiv* | 2025.02.14 | [Paper Link](https://arxiv.org/pdf/2502.09896)
21. Empowering AIOps: Leveraging Large Language Models for IT Operations Management | *arXiv* | 2025.01.21 | [Paper Link](https://arxiv.org/pdf/2501.12461)
22. BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction | *arXiv* | 2025.01.03 | [Paper Link](https://arxiv.org/pdf/2501.01664)
23. Toward Intelligent and Secure Cloud: Large Language Model Empowered Proactive Defense | *arXiv* | 2024.12.30 | [Paper Link](https://arxiv.org/pdf/2412.21051)
24. Emerging Security Challenges of Large Language Models | *arXiv* | 2024.12.23 | [Paper Link](https://arxiv.org/pdf/2412.17614)
25. Ontology-Aware RAG for Improved Question-Answering in Cybersecurity Education | *arXiv* | 2024.12.10 | [Paper Link](https://arxiv.org/pdf/2412.14191)
26. Integrating Large Language Models with Internet of Things Applications | *arXiv* | 2024.10.25 | [Paper Link](https://arxiv.org/pdf/2410.19223)
27. CmdCaliper: A Semantic-Aware Command-Line Embedding Model and Dataset for Security Research | *EMNLP* | 2024.10.02 | [Paper Link]([link](https://aclanthology.org/2024.emnlp-main.1126.pdf))
28. Advancing Cyber Incident Timeline Analysis Through Rule Based AI and Large Language Models | *arXiv* | 2024.09.25 | [Paper Link](https://arxiv.org/pdf/2409.02572)
29. Contextualized AI for Cyber Defense: An Automated Survey using LLMs | *arXiv* | 2024.09.20 | [Paper Link](https://arxiv.org/pdf/2409.13524)
30. LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot Systems | *arXiv* | 2024.09.15 | [Paper Link](https://arxiv.org/pdf/2409.08234)
31. ScriptSmith: A Unified LLM Framework for Enhancing IT Operations via Automated Bash Script Generation, Assessment, and Refinement | *arXiv* | 2024.09.12 | [Paper Link](https://arxiv.org/pdf/2409.17166)
32. Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks | *arXiv* | 2024.08.26 | [Paper Link](https://arxiv.org/pdf/2408.14045)
33. MistralBSM: Leveraging Mistral-7B for Vehicular Networks Misbehavior Detection | *arXiv* | 2024.07.26 | [Paper Link](https://arxiv.org/pdf/2407.18462)
34. MoRSE: Bridging the Gap in Cybersecurity Expertise with Retrieval Augmented Generation | *arXiv* | 2024.07.22 | [Paper Link](https://arxiv.org/pdf/2407.15748)
35. Disassembling Obfuscated Executables with LLM | *arXiv* | 2024.07.12 | [Paper Link](https://arxiv.org/pdf/2407.08924)
36. On Large Language Models in National Security Applications | *arXiv* | 2024.07.03 | [Paper Link](https://arxiv.org/pdf/2407.03453)
37. Threat Modelling and Risk Analysis for Large Language Model (LLM)-Powered Applications | *arXiv* | 2024.06.16 | [Paper Link](https://arxiv.org/pdf/2406.11007)
38. Exploring the Efficacy of Large Language Models (GPT-4) in Binary Reverse Engineering | *arXiv* | 2024.06.09 | [Paper Link](https://arxiv.org/pdf/2406.06637)
39. A Comprehensive Overview of Large Language Models (LLMs) for Cyber Defences: Opportunities and Directions | *arXiv* | 2024.05.23 | [Paper Link](https://arxiv.org/pdf/2405.14487)
40. LLMPot: Automated LLM-based Industrial Protocol and Physical Process Emulation for ICS Honeypots | *arXiv* | 2024.05.10 | [Paper Link](https://arxiv.org/pdf/2405.05999)
41. Critical Infrastructure Protection: Generative AI, Challenges, and Opportunities | *arXiv* | 2024.05.08 | [Paper Link](https://arxiv.org/pdf/2405.04874)
42. Large Language Models for Cyber Security: A Systematic Literature Review | *arXiv* | 2024.05.08 | [Paper Link](https://arxiv.org/pdf/2405.04760)
43. AppPoet: Large Language Model based Android malware detection via multi-view prompt engineering | *arXiv* | 2024.04.29 | [Paper Link](https://arxiv.org/pdf/2404.18816)
44. Act as a Honeytoken Generator! An Investigation into Honeytoken Generation with Large Language Models | *arXiv* | 2024.04.24 | [Paper Link](https://arxiv.org/pdf/2404.16118)
45. How Far Have We Gone in Stripped Binary Code Understanding Using Large Language Models | *arXiv* | 2024.04.16 | [Paper Link](https://arxiv.org/pdf/2404.09836)
46. Is Stack Overflow Obsolete? An Empirical Study of the Characteristics of ChatGPT Answers to Stack Overflow Questions | *CHI* | 2024.02.07 | [Paper Link](https://arxiv.org/abs/2308.02312)
47. Prompting Is All You Need: Automated Android Bug Replay with Large Language Models | *ICSE* | 2023.07.18 | [Paper Link](https://arxiv.org/abs/2306.01987)
48. Enhancing Network Management Using Code Generated by Large Language Models | *Proceedings of the 22nd ACM Workshop on Hot Topics in Networks* | 2023.08.11 | [Paper Link] (https://arxiv.org/abs/2308.06261)
49. Employing LLMs for Incident Response Planning and Review | *arXiv* | 2024.03.02 | [Paper Link](https://arxiv.org/abs/2403.01271)
50. LLM in the Shell: Generative Honeypots | *EuroS&P Workshop* | 2024.02.09 | [Paper Link](https://arxiv.org/abs/2309.00155)
51. Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations | *arXiv* | 2023.12.07 | [Paper Link](https://arxiv.org/abs/2312.06674)
52. Harnessing the Power of LLM to Support Binary Taint Analysis | *arXiv* | 2023.10.12 | [Paper Link](https://arxiv.org/abs/2310.08275)
53. LLM for SoC Security: A Paradigm Shift | *IEEE Access* | 2023.10.09 | [Paper Link](https://arxiv.org/abs/2310.06046)
54. Just-in-Time Security Patch Detection -- LLM At the Rescue for Data Augmentation | *arXiv* | 2023.12.12 | [Paper Link](https://arxiv.org/abs/2312.01241)
55. Anatomy of an AI-powered malicious social botnet | *arXiv* | 2023.07.30 | [Paper Link](https://arxiv.org/abs/2307.16336)
56. An LLM-based Framework for Fingerprinting Internet-connected Devices | *ACM on Internet Measurement Conference* | 2023.10.24 | [Paper Link](https://dl.acm.org/doi/pdf/10.1145/3618257.3624845)
### 🤖 RQ3: What are further research directions about the application of LLMs in cybersecurity? *(56 papers)*
#### Further Research: Agent4Cybersecurity
1. A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-case Prototypes | *arxiv* | 2026.01.08 | [Paper Link](https://arxiv.org/pdf/2601.05293)
2. A Network Arena for Benchmarking AI Agents on Network Troubleshooting | *arxiv* | 2025.12.18 | [Paper Link](https://arxiv.org/pdf/2512.16381)
3. The Evolution of Agentic AI in Cybersecurity: From Single LLM Reasoners to Multi-Agent Systems and Autonomous Pipelines | *arxiv* | 2025.12.07 | [Paper Link](https://arxiv.org/pdf/2512.06659)
4. AgentCyTE: Leveraging Agentic AI to Generate Cybersecurity Training & Experimentation Scenarios | *arxiv* | 2025.10.29 | [Paper Link](https://arxiv.org/pdf/2510.25189)
5. Cybersecurity AI: Evaluating Agentic Cybersecurity in Attack/Defense CTFs | *arxiv* | 2025.10.20 | [Paper Link](https://arxiv.org/pdf/2510.17521)
6. Synthesizing Agentic Data for Web Agents with Progressive Difficulty Enhancement Mechanisms | *arxiv* | 2025.10.15 | [Paper Link](https://arxiv.org/pdf/2510.13913)
7. A Survey on Agentic Security: Applications, Threats and Defenses | *arxiv* | 2025.10.07 | [Paper Link](https://arxiv.org/pdf/2510.06445)
8. xOffense: An AI-driven autonomous penetration testing framework with offensive knowledge-enhanced LLMs and multi agent systems | *arxiv* | 2025.09.16 | [Paper Link](https://arxiv.org/pdf/2509.13021)
9. Shell or Nothing: Real-World Benchmarks and Memory-Activated Agents for Automated Penetration Testing | *arxiv* | 2025.09.15 | [Paper Link](https://arxiv.org/pdf/2509.09207)
10. From CVE Entries to Verifiable Exploits: An Automated Multi-Agent Framework for Reproducing CVEs | *arxiv* | 2025.09.02 | [Paper Link](https://arxiv.org/pdf/2509.01835)
11. Training Language Model Agents to Find Vulnerabilities with CTF-Dojo | *arxiv* | 2025.08.25 | [Paper Link](https://arxiv.org/pdf/2508.18370)
12. FaultLine: Automated Proof-of-Vulnerability Generation Using LLM Agents | *arxiv* | 2025.07.21 | [Paper Link](https://arxiv.org/pdf/2507.15241)
13. From CVE Entries to Verifiable Exploits: An Automated Multi-Agent Framework for Reproducing CVEs | *arxiv* | 2025.09.02 | [Paper Link](https://arxiv.org/pdf/2509.01835)
14. Multi-Agent Penetration Testing AI for the Web | *arxiv* | 2025.08.28 | [Paper Link](https://arxiv.org/pdf/2508.20816)
15. CyberSleuth: Autonomous Blue-Team LLM Agent for Web Attack Forensics | *arxiv* | 2025.08.28 | [Paper Link](https://arxiv.org/pdf/2508.20643)
16. BountyBench: Dollar Impact of AI Agent Attackers and Defenders on Real-World Cybersecurity Systems | *arxiv* | 2025.07.10 | [Paper Link](https://arxiv.org/pdf/2505.15216)
17. AIRTBench: Measuring Autonomous AI Red Teaming Capabilities in Language Models | *arxiv* | 2025.06.17 | [Paper Link](https://arxiv.org/pdf/2506.14682)
18. Measuring and Augmenting Large Language Models for Solving Capture-the-Flag Challenges | *arxiv* | 2025.06.21 | [Paper Link](https://arxiv.org/pdf/2506.17644)
19. Towards Effective Offensive Security LLM Agents: Hyperparameter Tuning, LLM as a Judge, and a Lightweight CTF Benchmark | *arxiv* | 2025.08.05 | [Paper Link](https://arxiv.org/pdf/2508.05674)
20. Autonomous Penetration Testing: Solving Capture-the-Flag Challenges with LLMs | *arxiv* | 2025.08.01 | [Paper Link](https://arxiv.org/pdf/2508.01054)
21. AURA: A Multi-Agent Intelligence Framework for Knowledge-Enhanced Cyber Threat Attribution | *arxiv* | 2025.06.11 | [Paper Link](https://arxiv.org/pdf/2506.10175)
22. Improving LLM Agents with Reinforcement Learning on Cryptographic CTF Challenges | *arxiv* | 2025.06.01 | [Paper Link](https://arxiv.org/pdf/2506.02048)
23. RefPentester: A Knowledge-Informed Self-Reflective Penetration Testing Framework Based on Large Language Models | *arxiv* | 2025.05.1agent\t1 | [Paper Link](https://arxiv.org/pdf/2505.07089)
24. RedTeamLLM: an Agentic AI framework for offensive security | *arxiv* | 2025.05.11 | [Paper Link](https://arxiv.org/pdf/2505.06913)
25. AutoPatch: Multi-Agent Framework for Patching Real-World CVE Vulnerabilities | *arxiv* | 2025.05.07 | [Paper Link](https://arxiv.org/pdf/2505.04195)
26. Agent That Debugs: Dynamic State-Guided Vulnerability Repair | *arxiv* | 2025.04.10 | [Paper Link](https://arxiv.org/pdf/2504.07634)
27. CAI: An Open, Bug Bounty-Ready Cybersecurity AI | *arxiv* | 2025.04.08 | [Paper Link](https://arxiv.org/pdf/2504.06017)
28. Agentic AI and the Cyber Arms Race | *arxiv* | 2025.03.10 | [Paper Link](https://arxiv.org/pdf/2503.04760)
29. VulnBot: Autonomous Penetration Testing for A Multi-Agent Collaborative Framework | *arXiv* | 2025.01.23 | [Paper Link](https://arxiv.org/pdf/2501.13411)
30. Multi-Agent Collaboration in Incident Response with Large Language Models | *arXiv* | 2024.12.03 | [Paper Link](https://arxiv.org/pdf/2412.00652)
31. LLM Agent Honeypot: Monitoring AI Hacking Agents in the Wild | *arXiv* | 2024.10.17 | [Paper Link](https://arxiv.org/pdf/2410.13919)
32. MarsCode Agent: AI-native Automated Bug Fixing | *arXiv* | 2024.09.04 | [Paper Link](https://arxiv.org/pdf/2409.00899)
33. BreachSeek: A Multi-Agent Automated Penetration Tester | *arXiv* | 2024.08.31 | [Paper Link](https://arxiv.org/pdf/2409.03789)
34. PhishAgent: A Robust Multimodal Agent for Phishing Webpage Detection | *arXiv* | 2024.08.20 | [Paper Link](https://arxiv.org/pdf/2408.10738)
35. Using LLMs to Automate Threat Intelligence Analysis Workflows in Security Operation Centers | *arXiv* | 2024.07.18 | [Paper Link](https://arxiv.org/pdf/2407.13093)
36. Teams of LLM Agents can Exploit Zero-Day Vulnerabilities | *arXiv* | 2024.06.02 | [Paper Link](https://arxiv.org/pdf/2406.01637)
37. Generative AI and Large Language Models for Cyber Security: All Insights You Need | *arXiv* | 2024.05.21 | [Paper Link](https://arxiv.org/pdf/2405.12750)
38. Generative AI in Cybersecurity | *arXiv* | 2024.05.02 | [Paper Link](https://arxiv.org/pdf/2405.01674)
39. Large Language Models for Networking: Workflow, Advances and Challenges | *arXiv* | 2024.04.29 | [Paper Link](https://arxiv.org/pdf/2404.12901)
40. LLM Agents can Autonomously Exploit One-day Vulnerabilities | *arXiv* | 2024.04.17 | [Paper Link](https://arxiv.org/pdf/2404.08144)
41. InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents | *ACL Findings* | 2024.03.25 | [Paper Link](https://arxiv.org/abs/2403.02691)
42. WIPI: A New Web Threat for LLM-Driven Web Agents | *arXiv* | 2024.02.26 | [Paper Link](https://arxiv.org/abs/2402.16965)
43. R-Judge: Benchmarking Safety Risk Awareness for LLM Agents | *EMNLP Findings* | 2024.02.18 | [Paper Link](https://web3.arxiv.org/abs/2401.10019)
44. Large Language Models for Networking: Applications, Enabling Techniques, and Challenges | *arXiv* | 2023.11.29 | [Paper Link](https://arxiv.org/abs/2311.17474)
45. TaskWeaver: A Code-First Agent Framework | *arXiv* | 2023.12.01 | [Paper Link](https://arxiv.org/abs/2311.17541)
46. If llm is the wizard, then code is the wand: A survey on how code empowers large language models to serve as intelligent agents. | *arXiv* | 2024.01.08 | [Paper Link](https://arxiv.org/abs/2401.00812)
47. From Summary to Action: Enhancing Large Language Models for Complex Tasks with Open World APIs | *arXiv* | 2024.02.28 | [Paper Link](https://arxiv.org/abs/2402.18157)
48. ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs | *ICLR* | 2023.10.03 | [Paper Link](https://arxiv.org/abs/2307.16789)
49. The Rise and Potential of Large Language Model Based Agents: A Survey | *arXiv* | 2023.09.19 | [Paper Link](https://arxiv.org/abs/2309.07864)
50. TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage | *arXiv* | 2023.11.07 | [Paper Link](https://arxiv.org/abs/2308.03427)
51. Nissist: An Incident Mitigation Copilot based on Troubleshooting Guides | *ECAI* | 2024.02.27 | [Paper Link](https://arxiv.org/abs/2402.17531v1)
52. Llm agents can autonomously hack websites. | *arXiv* | 2024.02.16 | [Paper Link](https://arxiv.org/abs/2402.06664v1)
53. Out of the Cage: How Stochastic Parrots Win in Cyber Security Environments | *ICAART* | 2023.08.28 | [Paper Link](https://arxiv.org/abs/2308.12086)
54. LLMind: Orchestrating AI and IoT with LLM for Complex Task Execution | *arXiv* | 2024.02.20 | [Paper Link](https://arxiv.org/abs/2312.09007)
55. A unified cybersecurity framework for complex environments | *Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists* | 2018.09.26 | [Paper Link](https://dl.acm.org/doi/10.1145/3278681.3278682)
56. Cybersecurity Issues and Challenges | *Handbook of research on cybersecurity issues and challenges for business and FinTech applications* | 2022.08 | [Paper Link](https://www.researchgate.net/publication/367250373_Cybersecurity_Issues_and_Challenges)