Paper Citation Record · LEDGER

Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 18 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 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 18 of 18 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:42:57.593130Z

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 2026-08-10T06:31:04.303077+00:00.

Observation 59ff3d9f-7b10-4908-8533-61f25a722984 · inbound

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows cites this paper.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.593130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ed50a8f-dcae-4b88-b776-d9bf35956b90 · 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 Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-08T13:58:13.887315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5793f6b-7db9-4fb4-8eb2-6c11fed80625 · inbound

Adaptive Plan-Execute Framework for Smart Contract Security Auditing cites this paper.

Adaptive Plan-Execute Framework for Smart Contract Security Auditing Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:14.456635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebc4ddca-2b52-4d85-91cd-f4c1b35ea6a7 · inbound

SV-TrustEval-C: Evaluating Structure and Semantic Reasoning in Large Language Models for Source Code Vulnerability Analysis cites this paper.

SV-TrustEval-C: Evaluating Structure and Semantic Reasoning in Large Language Models for Source Code Vulnerability Analysis Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T13:55:14.910504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51360553-e5fb-4062-9b05-de98435193e7 · inbound

Mono: Is Your "Clean" Vulnerability Dataset Really Solvable? Exposing and Trapping Undecidable Patches and Beyond cites this paper.

Mono: Is Your "Clean" Vulnerability Dataset Really Solvable? Exposing and Trapping Undecidable Patches and Beyond Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:16.122549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 427ec583-1bf0-4f8e-ae87-6e6ead532661 · inbound

DCE-LLM: Dead Code Elimination with Large Language Models cites this paper.

DCE-LLM: Dead Code Elimination with Large Language Models Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:55.882508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d85352a8-7bf2-4f5b-bec6-c701cb2fb5be · inbound

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis cites this paper.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T04:19:10.099050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89402b97-58a7-49fc-a238-510ff92fd30d · 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 Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T23:35:04.178937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af50e917-27b9-45f7-a918-8cd64258dbb5 · inbound

A Mixture of Linear Corrections Generates Secure Code cites this paper.

A Mixture of Linear Corrections Generates Secure Code Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T17:59:03.805856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-10T06:31:04.303077+00:00.

Observation c103a05d-e794-4ebd-a470-f2ab9f768484 · inbound

Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques cites this paper.

Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T16:24:29.165973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+00:00.