Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-12T09:10:11.585499Z
Paper Citation Record · LEDGER
As of 10 August 2026, this Paper Citation Record lists 100 of 145 outbound references and 1 inbound Pith citation observation for arXiv:2607.02605.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-12T09:10:11.585499Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T04:19:18.909239Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-08T04:19:19.261715Z
100 of 145 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 210fd98f-ca49-46b3-b294-6e11877e3ae1 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Service grid fed- eration architecture for heterogeneous domains,
Reference 1
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Observation aa5a10c5-7a25-474a-8a5b-7689c1cd5256 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Adaptive energy-aware computation offloading for cloud of things systems,
Reference 2
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Observation 14f8630f-b46a-400b-b917-1aee3ec82fd1 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Dynamic service invocation control in service composition environments,
Reference 3
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Observation 746a63a0-06d5-47fe-88ce-73976ae9637c · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Fine-grained two- factor access control for web-based cloud computing services,
Reference 4
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Observation 6a265dc0-411b-472f-9b50-cae8eec7bc2b · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Trust-based access control for secure cloud computing,
Reference 5
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Observation bd9b68e5-cc83-4c24-b65c-d0f83be8eeb5 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Weidman,Penetration Testing: A Hands-On Introduction to Hack- ing
Reference 6
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Observation 16791e6a-4846-4c2f-b339-04e653c29a3d · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Security and privacy challenges in cloud computing environments,
Reference 7
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Observation 64f4989a-9296-4306-b6d5-5a9ce36d0edf · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Dynamic security risk manage- ment using bayesian attack graphs,
Reference 8
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Observation 8bf6ee13-283f-459f-9fac-80d159266c96 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Two-factor data security protection mechanism for cloud storage system,
Reference 9
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Observation 3b3c516e-d9da-4166-b135-c23ac125a6db · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Engebretson,The basics of hacking and penetration testing: ethical hacking and penetration testing made easy
Reference 10
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Observation 75906ac1-11f8-48d5-8c00-97f805433ab6 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Examining penetration tester behavior in the collegiate penetration testing competition,
Reference 11
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Observation df531fa1-58ae-4872-b057-9d5fee0a8e34 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Penetration testing–reconnaissance with nmap tool,
Reference 12
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Observation bb35d593-0ab9-41c3-baab-0ef8822002f0 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Toss a fault to your witcher: Applying grey-box coverage-guided mutational fuzzing to detect sql and command injection vulnerabilities,
Reference 13
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Observation 8da05c22-b554-4a6f-b3e0-ef17449affc6 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges {FUGIO}: Automatic exploit generation for{PHP}object injection vulnerabilities,
Reference 14
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Observation 2ad030cb-5c8f-45df-ae90-fe6783ef50cf · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges {ChainReactor}: Automated privilege es- calation chain discovery via{AI}planning,
Reference 15
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Observation d748b416-21b0-4f2a-a84e-792d00440a53 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges A comprehensive detection method for the lateral movement stage of apt attacks,
Reference 16
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Observation 63d8d5b6-2503-425d-8047-0124bf642b1e · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges A comprehensive overview of large language models,
Reference 17
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Observation f667df0f-4b32-4971-97f7-54be8d515cba · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Large language models for cyber security: A systematic literature review,
Reference 18
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Observation c743e59d-b0ec-478b-a0e3-156f12764254 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges {PentestGPT}: Evaluating and harnessing large language models for automated penetration testing,
Reference 19
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Observation 05906e27-e947-4daf-a7a9-e86e6156c3fd · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Expel: Llm agents are experiential learners,
Reference 20
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Observation c1c82a77-5e6e-4a14-96c4-9bf0c06497f2 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Autotool: Efficient tool selection for large language model agents,
Reference 21
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Observation 731d5387-d37a-41dc-a752-606544d8b4ae · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges A survey on the feedback mechanism of llm- based ai agents,
Reference 22
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Observation a7f54027-a8b6-4368-81df-6fe9368e9ef3 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Spaiware: Uncovering a novel artificial intelligence attack vector through persistent memory in llm applications and agents,
Reference 23
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Observation 85194bb2-bd3c-4ce1-89f6-ae447e0820fd · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Exe- cutable code actions elicit better llm agents,
Reference 24
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Observation 0d16d82f-9d02-4712-af8c-61a1619fc6e7 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges VulnBot: Autonomous Penetration Testing for A Multi-Agent Collaborative Framework
Reference 25
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Observation 2e3e7b8b-fb0b-4e6b-bf09-efde705691e2 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Automated penetration testing with llm agents and classical planning,
Reference 26
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Observation af477695-0790-4068-bbbb-98c06d38af12 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Pentest-r1: Towards autonomous penetration testing reasoning optimized via two-stage reinforcement learning,
Reference 27
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Observation 02c34b39-37b5-4fb7-bfaf-ccfaf52fb370 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Cyber- zero: Training cybersecurity agents without runtime,
Reference 28
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Observation 5a93a239-8d0c-4381-ad78-5626f6cf1eed · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Cybench: A framework for evaluating cybersecurity capabilities and risks of language models,
Reference 29
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Observation 5f3952f4-5d59-4ede-b3e7-05fa6bf75dd6 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Autopenbench: A vulnerability testing benchmark for generative agents,
Reference 30
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Observation c3992502-f160-4a23-9de0-28499f2165a9 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Nyu ctf bench: A scalable open-source benchmark dataset for evaluating llms in offensive security,
Reference 31
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Observation 080f2a37-45a0-44bf-860b-630447ce2d8d · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Towards effective offensive security llm agents: Hyperparameter tuning, llm as a judge, and a lightweight ctf benchmark,
Reference 32
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Observation 0d9976af-13a6-4820-ac4e-73807e93a6bc · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Enigma: Interactive tools substantially assist lm agents in finding security vulnerabilities,
Reference 33
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Observation 0e72e55a-846a-42f6-bff8-eb744e3cd7d1 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Chimera: Harnessing multi- agent llms for automatic insider threat simulation,
Reference 34
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Observation c351cd2a-c334-45e1-9ece-eb6d29f591d5 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Awe: Adaptive agents for dynamic web penetration testing,
Reference 35
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Observation d27b3552-3f0d-48be-862c-3c013b62a9e5 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Hackers or Hallucinators? A Comprehensive Analysis of LLM-Based Automated Penetration Testing
Reference 36
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Observation 5371b2c9-2f19-4d1c-b521-5fb7f4011829 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Benchmarking Practices in LLM-driven Offensive Security: Testbeds, Metrics, and Experiment Design
Reference 37
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Observation a471ea7d-910a-43e2-ac91-5381519797e2 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges On the Surprising Efficacy of LLMs for Penetration-Testing
Reference 38
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Observation 3100b41b-dfe7-489c-a06d-4ef7dcc5e6c7 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges A unified modeling framework for automated penetration testing,
Reference 39
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Observation 027175b6-5a64-49e2-8404-ea9e96fe8d24 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Automated penetration testing: Formal- ization and realization,
Reference 40
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Observation db8f7259-7110-4b10-9818-1822490f4a19 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges {CTF}:{State-of-the-Art}and building the next generation,
Reference 41
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Observation b452e875-d806-452f-bec5-9f4c1e7a6a3e · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Cve-bench: A benchmark for ai agents’ ability to exploit real-worldweb application vulnerabilities,
Reference 42
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Observation c59add8c-1475-4f25-b80e-d9b977a154c6 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Guidelines for performing systematic literature reviews in software engineering,
Reference 43
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Observation adfa7a0f-770b-4e09-af85-092056ae07bb · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Intelligent penetration testing through integrated knowledge graph and historical decision enhancement,
Reference 44
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Observation c2543969-2970-403d-b724-85015210bb5d · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Intercode: Stan- dardizing and benchmarking interactive coding with execution feed- back,
Reference 45
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Observation 5e28fc9a-d73d-478b-a8d2-eb69709113e8 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges LLM Agents can Autonomously Exploit One-day Vulnerabilities
Reference 46
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Observation 8d49293a-1eb0-41b9-ada0-0ab3a11d48fb · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Got Root? A Linux Priv-Esc Benchmark
Reference 47
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Observation 449cdf82-bf1b-40fa-b15d-776a14b71bb5 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges HackSynth: LLM Agent and Evaluation Framework for Autonomous Penetration Testing
Reference 48
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Observation 6f3341c2-a8fd-46a0-b129-cf9e57cf8280 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges An Empirical Evaluation of LLMs for Solving Offensive Security Challenges
Reference 49
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Observation 2b9ceb88-5530-4cbf-83ce-e80d7511db39 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Catastrophic cyber capabilities benchmark (3cb): Robustly evaluating llm agent cyber offense capabilities,
Reference 50
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Observation 4b997c96-2099-4abb-a3d1-0b4b3a37c1cf · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Towards automated penetration testing: Introducing llm benchmark, analysis, and improve- ments,
Reference 51
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Observation 103f6b67-dd11-4fb7-9d37-93627dbd24c4 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Pen- testeval: Benchmarking llm-based penetration testing with modular and stage-level design,
Reference 52
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Observation 2be6268f-5191-41f5-8aa9-0bf34e110253 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Cybergym: Evaluating ai agents’ real-world cybersecurity capabilities at scale,
Reference 53
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Observation b7d26508-ee65-49ad-a541-effc16e84bde · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Hackworld: Evaluating computer-use agents on exploit- ing web application vulnerabilities,
Reference 54
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Observation 5a47ac48-02bf-4fbe-b5a7-465833cbf79e · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Pacebench: A framework for evaluating practical ai cyber-exploitation capabilities,
Reference 55
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Observation 29c5fa5b-1d1a-4ee2-a75e-c3f3368c8d93 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges CTFusion: A CTF-based Benchmark for LLM Agent Evaluation
Reference 56
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Observation b4732774-2ba4-49c4-8ca7-af1be4307ff3 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges How Reliable Are AI Attackers Against a Fixed Vulnerable Target? A 400-Run Empirical Study of LLM Penetration Testing Consistency
Reference 57
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Observation 2d5e2d96-e0eb-45df-9ae2-539a51d8200d · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges CyberGym-E2E: Scalable Real-World Benchmark for AI Agents' End-to-End Cybersecurity Capabilities
Reference 58
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Observation 29e2d4b9-282d-48c0-bdcf-9bd3f2509b6f · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?
Reference 59
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Observation 9a3eb5c2-626a-43b1-bcf9-1ea1b20f0200 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Penheal: A two-stage llm framework for automated pentesting and optimal remediation,
Reference 60
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Observation 0f64a90a-baee-4c66-b62c-38515c759812 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks
Reference 61
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Observation fa369260-2f15-4ff5-a111-5fcccf3e9bec · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Pentest-ai, an llm-powered multi- agents framework for penetration testing automation leveraging mitre attack,
Reference 62
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Observation f313c0ce-8f83-47a4-b297-9b721728f5cd · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Pentestagent: Incorporating llm agents to automated penetration testing,
Reference 63
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Observation f1d55eba-7b9d-4104-90df-da6d403b2c2f · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Autopentester: An llm agent-based framework for automated pentesting,
Reference 64
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Observation 0a3d033b-75d4-471a-87e1-827e61d1b54c · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges PenTest++: Elevating Ethical Hacking with AI and Automation
Reference 65
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Observation fa266cf3-5f06-4f6a-ab9a-8ca169bd82c5 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Rapidpen: Fully automated ip-to-shell penetration testing with llm-based agents,
Reference 66
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Observation f23085bf-13b3-4e39-bba4-d03b957eb832 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Guided reasoning in llm-driven penetration testing using structured attack trees,
Reference 67
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Observation 3635cbc8-0410-4ede-b126-1e066601d176 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges BreachSeek: A Multi-Agent Automated Penetration Tester
Reference 68
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Observation 2848a064-1259-4149-a7da-c349c96efe7e · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Controller makes pentesting better: An improved multi-agent auto- mated penetration testing framework,
Reference 69
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Observation eb4c7851-be73-4bfa-a7f7-a93bb8a540e5 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Shell or nothing: Real-world benchmarks and memory- activated agents for automated penetration testing,
Reference 70
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Observation a529eebf-bae3-4054-8b2d-1225ebe2467f · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Refpentester: A knowledge- informed self-reflective penetration testing framework based on large language models,
Reference 71
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Observation 3d413c04-9332-4b6d-b902-ee2b6ccb284d · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Ptfusion: Llm- driven context-aware knowledge fusion for web penetration testing,
Reference 72
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Observation 98b41d4b-c539-4034-a614-b84d265cb790 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Pentestmcp: Llm and mcp based multi-agent framework for automated penetration testing,
Reference 73
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Observation 5d800317-7377-490f-af9c-882f2e149a6d · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges xoffense: An ai-driven autonomous penetration testing framework with offensive knowledge-enhanced llms and multi agent systems,
Reference 74
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Observation 78960ca3-6a94-40bb-b0aa-baf686c3e03b · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges CAI: An Open, Bug Bounty-Ready Cybersecurity AI
Reference 75
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Observation f0b5d879-7476-44fd-b3f0-434a6e37a026 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Redteamllm: an agentic ai framework for offensive security,
Reference 76
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Observation 91414e80-c0a7-4176-b89a-748a6efd9007 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Red-mirror: Agentic llm-based autonomous penetration testing with reflective verification and knowledge-augmented interac- tion,
Reference 77
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Observation 2c088a8b-63c1-4f2e-b69b-b2d3abe6d00a · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges What makes a good llm agent for real-world penetration testing?
Reference 78
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Observation 16f0dc1a-57cb-4c6e-8605-560147f5281e · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Incalmo: An autonomous llm-assisted system for red teaming multi- host networks,
Reference 79
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Observation 2cf6539a-6cb5-4e4d-90f1-10965943e0b9 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Cipher: Cybersecurity intelligent penetration- testing helper for ethical researcher,
Reference 80
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Observation 31ecfd0c-d832-4929-8ca0-b4154b6c0e7d · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Pen-Strategist: A Reasoning Framework for Penetration Testing Strategy Formation and Analysis
Reference 81
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Observation 10ff4b56-ac0d-4aa9-ab28-55a72fa44e00 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges From intent to invocation: A reasoning-first framework for natural language to pen- etration testing commands,
Reference 82
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Observation 70ff2b12-b7b1-4391-adcf-f750b30954a5 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges LLM Agents can Autonomously Hack Websites
Reference 83
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Observation 2f0d08fa-93a9-4122-93f2-a8d10879c8ca · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agents
Reference 84
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Observation 96aef585-e1f7-422e-b536-1e18a0396d6e · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Automated tactics planning for cyber attack and defense based on large language model agents,
Reference 85
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Observation 94154dbd-a531-48dc-a0f4-4b7c04a314ab · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges From capabilities to performance: Evaluating key functional properties of llm architectures in penetration testing,
Reference 86
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Observation e9a648b7-6a46-4593-b160-55fa59a4ce80 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Penforge: On-the-fly expert agent construction for automated penetration testing,
Reference 87
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Observation 17035994-6e59-4683-a7e6-65929d33f127 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Perses: Unlocking privilege escalation for small llms via extensible heterogeneity,
Reference 88
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Observation 83ab6bc7-cefc-42bf-8263-7aab9d5039d4 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Towards Reliable Local Security Agents: Verifiable Post-Training for Linux Privilege Escalation
Reference 89
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Observation f1ba5da7-160d-4928-85cb-99ecc5e676dc · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Can llms hack enterprise networks? autonomous assumed breach penetration-testing active directory networks,
Reference 90
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Observation 71464fe0-cc6d-41cf-9872-bf253de0b665 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Multi-Agent Penetration Testing AI for the Web
Reference 91
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Observation 5b87ce12-3fcd-4f1c-9c6a-d3c90ea08304 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges ARACNE: An LLM-Based Autonomous Shell Pentesting Agent
Reference 92
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Observation 1bdbd712-9394-4429-aeb9-f7544ec9b9e3 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Wifipentester: Towards governed genai-assisted wireless pentesting,
Reference 93
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Observation c4d1d695-363c-439a-bd24-acedbda5d58b · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Ai-driven penetration testing for arm systems: Experimental evaluation and deployment framework across four paradigms,
Reference 94
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Observation d9070392-1f83-4ca8-ba8d-897911781525 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Getting pwn’d by ai: Penetration testing with large language models,
Reference 95
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Observation d0a70912-4a6c-422a-8326-482f6fa986c6 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges AutoPT: How Far Are We from the End2End Automated Web Penetration Testing?
Reference 96
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Observation 960b9023-4926-4141-9fbf-a86ff7787b6b · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Llms as hackers: Autonomous linux privilege escalation attacks,
Reference 97
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Observation 85a0061c-b2d1-4985-87a7-df0adff7c83d · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges D-CIPHER: Dynamic Collaborative Intelligent Multi-Agent System with Planner and Heterogeneous Executors for Offensive Security
Reference 98
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Observation c949ec0a-e045-4586-928f-35b22e340274 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Measuring and augmenting large language models for solving capture-the-flag challenges,
Reference 99
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Observation 3602dbb9-74c9-42a7-85ee-00ed9d5398a4 · outbound
A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges Ctfagent: An llm-powered agent for ctf challenge solving,
Reference 100
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Observation 4ae7e36f-590f-4925-8495-66c7521dbca7 · inbound
Tiny Enough to Break In: Agentic Remote Access Trojans Powered by Small Language Models A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges
Reference 18
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