Paper Citation Record · LEDGER

LLbezpeky: Leveraging Large Language Models for Vulnerability Detection

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2401.01269.

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

pith.paper-citation-record.v1
2401.01269 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:44:54.509120Z

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

11
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 88edbe16-2436-4a83-8e2c-76de19ae7767 · inbound

Direction for Detection: A Survey of Automated Vulnerability Detection and all of its Pain Points cites this paper.

Direction for Detection: A Survey of Automated Vulnerability Detection and all of its Pain Points LLbezpeky: Leveraging Large Language Models for Vulnerability Detection

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-23T06:52:39.669608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 31ba1f17-2cbc-46ef-a642-98dbe976ddc7 · inbound

Combining GPT and Code-Based Similarity Checking for Effective Smart Contract Vulnerability Detection cites this paper.

Combining GPT and Code-Based Similarity Checking for Effective Smart Contract Vulnerability Detection LLbezpeky: Leveraging Large Language Models for Vulnerability Detection

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-11T04:59:42.517243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ab3ab3f-c434-4820-90c1-fe8240e8a36e · inbound

LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights cites this paper.

LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights LLbezpeky: Leveraging Large Language Models for Vulnerability Detection

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-08T13:58:14.011152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c57bc83-78d5-451d-826c-34d49749b39d · inbound

A Contemporary Survey of Large Language Model Assisted Program Analysis cites this paper.

A Contemporary Survey of Large Language Model Assisted Program Analysis LLbezpeky: Leveraging Large Language Models for Vulnerability Detection

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-09T05:32:23.511479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56e035c0-bce0-4cdc-9e56-241141adeb3d · inbound

Leveraging LLM to Strengthen ML-Based Cross-Site Scripting Detection cites this paper.

Leveraging LLM to Strengthen ML-Based Cross-Site Scripting Detection LLbezpeky: Leveraging Large Language Models for Vulnerability Detection

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T05:44:54.509120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 633504f4-21d3-40b5-a11b-6dd56bed4251 · inbound

Do Fine-Tuned LLMs Understand Vulnerabilities? An Investigation into the Semantic Trap cites this paper.

Do Fine-Tuned LLMs Understand Vulnerabilities? An Investigation into the Semantic Trap LLbezpeky: Leveraging Large Language Models for Vulnerability Detection

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-22T11:41:30.205287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.