Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2312.15838.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T12:01:31.400840Z
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
6
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 10405172-29ff-4486-a308-df9ed79badbc · inbound
Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models cites this paper.
Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T14:04:52.203849Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1cd9c795-c2bb-4a71-b747-d15da02a1135 · inbound
SecBench: A Comprehensive Multi-Dimensional Benchmarking Dataset for LLMs in Cybersecurity cites this paper.
SecBench: A Comprehensive Multi-Dimensional Benchmarking Dataset for LLMs in Cybersecurity SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T23:13:15.165989Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 89da18fe-18d3-4128-ab03-f233e45f5cc0 · inbound
MEQA: A Meta-Evaluation Framework for Question & Answer LLM Benchmarks cites this paper.
MEQA: A Meta-Evaluation Framework for Question & Answer LLM Benchmarks SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T12:01:31.400840Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dbfc55e4-2577-4141-81ec-b92f64bdc393 · inbound
Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey cites this paper.
Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:23:50.777683Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3597b74e-e587-4101-a538-bd4390941d14 · inbound
Forewarned is Forearmed: A Survey on Large Language Model-based Agents in Autonomous Cyberattacks cites this paper.
Forewarned is Forearmed: A Survey on Large Language Model-based Agents in Autonomous Cyberattacks SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
Reference 121
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:28:59.288060Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7130c725-7c73-4f30-9864-7d3c421d4335 · inbound
ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat Investigation cites this paper.
ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat Investigation SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T04:42:04.694141Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 5913bd84-b7ff-46dd-aac6-ee4ba7a18f80 · inbound
Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report cites this paper.
Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T05:57:29.331417Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2bd3e3ef-55c9-4993-abdd-9251d43e8ad1 · inbound
Capture the Flags: Family-Based Evaluation of Agentic LLMs via Semantics-Preserving Transformations cites this paper.
Capture the Flags: Family-Based Evaluation of Agentic LLMs via Semantics-Preserving Transformations SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
Reference 30
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T07:27:31.451825Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 5b176ddf-901b-4c1a-8fdd-0351d472a0c9 · inbound
SIR-Bench: Evaluating Investigation Depth in Security Incident Response Agents cites this paper.
SIR-Bench: Evaluating Investigation Depth in Security Incident Response Agents SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T10:41:07.590068Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 585ba0f4-05ab-4356-b50d-bb184d0fc785 · inbound
CyberCertBench: Evaluating LLMs in Cybersecurity Certification Knowledge cites this paper.
CyberCertBench: Evaluating LLMs in Cybersecurity Certification Knowledge SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T00:34:47.052589Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 977dc510-6605-4c46-bb01-3e8cf97cce6c · inbound
CyberMaskQA: A Privacy-Aware Benchmark for Evaluating Large Language Models in Cybersecurity Question Answering cites this paper.
CyberMaskQA: A Privacy-Aware Benchmark for Evaluating Large Language Models in Cybersecurity Question Answering SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T12:54:40.159155Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation ddaa661a-6792-4b0d-a236-3006f9ddfed2 · inbound
Cybersecurity AI (CAI) Dataset cites this paper.
Cybersecurity AI (CAI) Dataset SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T12:13:26.847667Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 03fe296a-249b-4b85-82d3-75e0fc0838fc · inbound
Domyn-Small: A European 10B Reasoning Language Model cites this paper.
Domyn-Small: A European 10B Reasoning Language Model SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
Reference 75
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T14:07:59.461037Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.