Paper Citation Record · LEDGER

NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2406.05590.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2406.05590 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T23:10:11.091917Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

3
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 95d04523-eaaf-47c8-9261-b400cd3c580f · inbound

Can LLMs Hack Enterprise Networks? Autonomous Assumed Breach Penetration-Testing Active Directory Networks cites this paper.

Can LLMs Hack Enterprise Networks? Autonomous Assumed Breach Penetration-Testing Active Directory Networks NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-08T23:10:11.091917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d163595e-7ebc-4faf-a91d-e25a6264d473 · inbound

Improving LLM Agents with Reinforcement Learning on Cryptographic CTF Challenges cites this paper.

Improving LLM Agents with Reinforcement Learning on Cryptographic CTF Challenges NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:35.404055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 425b3577-9f55-4a6f-9635-ccaf8025aa32 · inbound

Measuring and Augmenting Large Language Models for Solving Capture-the-Flag Challenges cites this paper.

Measuring and Augmenting Large Language Models for Solving Capture-the-Flag Challenges NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T23:35:05.819135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c5271e7-fee5-4224-bf41-1af1cef21a82 · inbound

Running in CIRCLE? A Simple Benchmark for LLM Code Interpreter Security cites this paper.

Running in CIRCLE? A Simple Benchmark for LLM Code Interpreter Security NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T14:25:15.835767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 465b323a-806e-4e04-8059-d42cec2a3e14 · inbound

Systematic Capability Benchmarking of Frontier Large Language Models for Offensive Cyber Tasks cites this paper.

Systematic Capability Benchmarking of Frontier Large Language Models for Offensive Cyber Tasks NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:06:19.098028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d0e9112a-cc10-45ad-9357-1898e1e3df85 · inbound

Cyber Defense Benchmark: Agentic Threat Hunting Evaluation for LLMs in SecOps cites this paper.

Cyber Defense Benchmark: Agentic Threat Hunting Evaluation for LLMs in SecOps NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:06:02.871221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 495deee8-79ee-4084-8dc3-77be9f21d781 · inbound

Synthesizing Multi-Agent Harnesses for Vulnerability Discovery cites this paper.

Synthesizing Multi-Agent Harnesses for Vulnerability Discovery NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:19:46.433919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 96ce393e-2f4d-4192-a244-09cc28ad4275 · inbound

Dynamic Cyber Ranges cites this paper.

Dynamic Cyber Ranges NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T22:16:52.890773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fd25a401-3a2c-4e9a-8080-d2b893e40381 · inbound

Are Frontier LLMs Ready for Cybersecurity? Evidence for Vertical Foundation Models from Dual-Mode Vulnerability Benchmarks cites this paper.

Are Frontier LLMs Ready for Cybersecurity? Evidence for Vertical Foundation Models from Dual-Mode Vulnerability Benchmarks NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-25T04:30:20.213891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5d93b70a-bdf3-4a88-b9f4-25f4fbb1f84f · inbound

Are Frontier LLMs Ready for Cybersecurity? Evidence for Vertical Foundation Models from Dual-Mode Vulnerability Benchmarks cites this paper.

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-06-30T16:35:12.937455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 916b8740-ad9a-4ba6-8d4b-655ba5d65ae2 · inbound

CyberEvolver: Structured Self-Evolution for Cybersecurity Agents On the Fly cites this paper.

CyberEvolver: Structured Self-Evolution for Cybersecurity Agents On the Fly NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-06-29T21:23:58.993128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 38612fa6-7e8f-427d-859d-f1d3a5b99f5f · inbound

unix-ctf: Procedural Environments for Unix-Competence Reinforcement Learning cites this paper.

unix-ctf: Procedural Environments for Unix-Competence Reinforcement Learning NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-06-29T11:23:20.894545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 58921728-1b32-4b4e-a8a8-94aedc70557c · inbound

Mastermind: Strategy-grounded Learning for Repository-Scale Vulnerability Reproduction cites this paper.

Mastermind: Strategy-grounded Learning for Repository-Scale Vulnerability Reproduction NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:08:21.515900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8d20a8c4-390a-4650-a315-0bcba00456e7 · inbound

Beyond Success Rate: Cost-Aware Evaluation of Offensive and Defensive Security Agents cites this paper.

Beyond Success Rate: Cost-Aware Evaluation of Offensive and Defensive Security Agents NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T23:44:36.919216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56e77957-a8b6-4938-9fce-98f4ecdf0eee · inbound

The Disruptive Impact of Large Language Models on Capture the Flag Competitions and the Path Toward Fair Play cites this paper.

The Disruptive Impact of Large Language Models on Capture the Flag Competitions and the Path Toward Fair Play NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T02:31:25.876707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff095c14-b3bc-4ac3-910d-20329497c5a8 · inbound

Open Security Benchmark: Towards Autonomous Enterprise Cyber Defense cites this paper.

Open Security Benchmark: Towards Autonomous Enterprise Cyber Defense NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T10:23:19.237266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b62f91e6-9de3-4e1c-8460-edc37fea8a3c · inbound

Antares: Foundation Models for Agentic Vulnerability Localization cites this paper.

Antares: Foundation Models for Agentic Vulnerability Localization NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-04T07:50:50.682477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.