Pith. sign in

Paper Citation Record · LEDGER

Using Large Language Models for Cybersecurity Capture-The-Flag Challenges and Certification Questions

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2308.10443.

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

pith.paper-citation-record.v1
2308.10443 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:23:52.023681Z

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

7
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 349dffc6-f783-43e7-8e06-d78707229650 · inbound

CRAKEN: Cybersecurity LLM Agent with Knowledge-Based Execution cites this paper.

CRAKEN: Cybersecurity LLM Agent with Knowledge-Based Execution Using Large Language Models for Cybersecurity Capture-The-Flag Challenges and Certification Questions

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-07T15:23:52.023681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:23:52.023681Z digest=sha256:22f08d69a5e91e0b5d8df9a9e5710a57ee7b9e77a92a139cbc234ab1659f114a

Observation a650f72c-7905-4486-9246-e85a5588b093 · inbound

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

Improving LLM Agents with Reinforcement Learning on Cryptographic CTF Challenges Using Large Language Models for Cybersecurity Capture-The-Flag Challenges and Certification Questions

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-07T12:02:35.418518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:02:35.418518Z digest=sha256:06bc329f2c4913922418f8724b35f4b386c79a625c78b59b682f38ab4e53b435

Observation 53081299-40d5-450f-ba3a-f98341845241 · 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 Using Large Language Models for Cybersecurity Capture-The-Flag Challenges and Certification Questions

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-06T23:35:05.945362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:35:05.945362Z digest=sha256:cefdf8fdcdda9bb10424333ada76570cf8db76310072fd990ab07cbbf1a2e4b6

Observation 14d5a522-2838-4b3f-9ec9-95119e5a4eff · inbound

CyberCertBench: Evaluating LLMs in Cybersecurity Certification Knowledge cites this paper.

CyberCertBench: Evaluating LLMs in Cybersecurity Certification Knowledge Using Large Language Models for Cybersecurity Capture-The-Flag Challenges and Certification Questions

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-10T00:34:47.016403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T00:32:59.120345Z digest=sha256:ca8257d6c0c0c6837cc13bd5c2c9dae6a1df1819292728ef071ce84677e221cc