Paper Citation Record · LEDGER

Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2311.16169.

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

pith.paper-citation-record.v1
2311.16169 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T20:21:37.520539Z

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

12
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 0c4482a5-066c-420c-9c19-50f2fb464ea7 · 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 Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 156

Resolution: verified exact; arxiv_id, observed 2026-05-23T06:52:39.949213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6514ab27-09b1-419f-b5be-0b0e523c5b79 · inbound

MetaLint: Easy-to-Hard Generalization for Code Linting cites this paper.

MetaLint: Easy-to-Hard Generalization for Code Linting Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-19T04:12:02.943059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 60b453e2-e14f-49cf-8032-bffa27a979cf · inbound

Fine-Tuning Code Language Models to Detect Cross-Language Bugs cites this paper.

Fine-Tuning Code Language Models to Detect Cross-Language Bugs Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-19T02:42:56.400694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 55edd2cc-5d49-450c-8b67-711a85c6ab6a · inbound

QuiLL: An LLM-Based Vulnerability Assessment Framework for the Wild cites this paper.

QuiLL: An LLM-Based Vulnerability Assessment Framework for the Wild Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-18T11:01:17.221215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 69b53457-5785-4108-9a53-da2b8bc3f478 · inbound

RubberDuckBench: A Benchmark for AI Coding Assistants cites this paper.

RubberDuckBench: A Benchmark for AI Coding Assistants Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-16T12:17:51.990399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d540fea6-84a1-480e-8742-9e963f3e9783 · inbound

QASecClaw: A Multi-Agent LLM Approach for False Positive Reduction in Static Application Security Testing cites this paper.

QASecClaw: A Multi-Agent LLM Approach for False Positive Reduction in Static Application Security Testing Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:26:03.474985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e245f422-4b77-4039-b089-a2e0567c5d06 · inbound

Longitudinal Analyses of SAST Tools: A CodeQL Case Study cites this paper.

Longitudinal Analyses of SAST Tools: A CodeQL Case Study Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:35:55.330349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3566f947-7fd3-46fd-8472-f12780947e2b · inbound

OpenAnt: LLM-Powered Vulnerability Discovery Through Code Decomposition, Adversarial Verification, and Dynamic Testing cites this paper.

OpenAnt: LLM-Powered Vulnerability Discovery Through Code Decomposition, Adversarial Verification, and Dynamic Testing Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-07-04T01:29:22.422247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.