Paper Citation Record · LEDGER

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk

As of 9 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 3 inbound Pith citation observations for arXiv:2506.19453.

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

pith.paper-citation-record.v1
2506.19453 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:13:34.554413Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T14:49:04.136085Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-01T13:55:45.556935Z

Reference resolution

38 of 38 outbound references displayed

verified exact0
verified fuzzy28
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1316ca4c-e5e6-4b52-b110-44971591abf6 · outbound

This paper cites In: 2023 IEEE 9th International Women in En- gineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE).

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk In: 2023 IEEE 9th International Women in En- gineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:43.799230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8531301b-625d-4269-b1a1-350219fe43ea · outbound

This paper cites an unresolved cited work.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:13:43.507360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 86001457-6366-45bb-871b-d524c0122dc2 · outbound

This paper cites an unresolved cited work.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:13:43.340413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 12458923-1a0c-40c1-a459-32845d1fb447 · outbound

This paper cites In: Proceedings of the 19th ACM Asia Conference on Computer and Communications Se- curity.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk In: Proceedings of the 19th ACM Asia Conference on Computer and Communications Se- curity

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:43.018180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8533ff44-a121-4772-a142-0ca518069ace · outbound

This paper cites CodeBERT: A Pre-Trained Model for Programming and Natural Languages.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk CodeBERT: A Pre-Trained Model for Programming and Natural Languages

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T23:13:30.523327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c660a5c5-92e0-4ade-9240-e0471ef20358 · outbound

This paper cites In: Proceedings of the 19th International Conference on Mining Software Repositories.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk In: Proceedings of the 19th International Conference on Mining Software Repositories

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:42.886238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0d1fcfc3-155e-4a13-9242-e2f366b46ead · outbound

This paper cites GraphCodeBERT: Pre-training Code Representations with Data Flow.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk GraphCodeBERT: Pre-training Code Representations with Data Flow

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T23:13:30.726739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4fa07b3-eaaf-4a97-a8ad-071a479153ea · outbound

This paper cites In: European symposium on research in computer security.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk In: European symposium on research in computer security

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:42.671668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 92a2c42b-126e-4eed-88b6-1f003dfe73ba · outbound

This paper cites In: 2022 International joint conference on neural networks (IJCNN).

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk In: 2022 International joint conference on neural networks (IJCNN)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:42.395891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bd927deb-2fd2-4c64-8508-e2da57dd363c · outbound

This paper cites In: Proceedings of the 19th international conference on mining software repositories.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk In: Proceedings of the 19th international conference on mining software repositories

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:42.120090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4f40fbba-bc59-47f8-a4b2-c7b389584f1a · outbound

This paper cites In: 2023 IEEE 8th European Sym- posium on Security and Privacy (EuroS&P).

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk In: 2023 IEEE 8th European Sym- posium on Security and Privacy (EuroS&P)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:41.878736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation baa9c564-1137-4826-b876-1765b9f57b94 · outbound

This paper cites In: Proceedings of naacL-HLT.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk In: Proceedings of naacL-HLT

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:41.700525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8b62c49e-d378-4def-a0e8-84a385e576b2 · outbound

This paper cites In: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk In: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:41.437234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d37fad52-3827-4cb1-9597-c3180f3e8b26 · outbound

This paper cites IEEE Transactions on Dependable and Secure Computing 19(4), 2821–2837 (2021).

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk IEEE Transactions on Dependable and Secure Computing 19(4), 2821–2837 (2021)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:41.094818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ddbaa340-d397-41f7-b718-5f13db2a09aa · outbound

This paper cites In: Proceedings of the 32nd annual conference on computer security applications.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk In: Proceedings of the 32nd annual conference on computer security applications

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:40.883753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 48c02220-5350-4c61-835a-4d02bc84c508 · outbound

This paper cites VulDeePecker: A Deep Learning-Based System for Vulnerability Detection.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk VulDeePecker: A Deep Learning-Based System for Vulnerability Detection

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T23:13:32.024818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0140dcbb-5bcf-4ea8-a5cd-41ef4a675f0e · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T23:13:32.087203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f05d7fe-1833-4c95-97cc-98315e273ea6 · outbound

This paper cites Journal of Systems and Software 212, 112031 (2024).

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk Journal of Systems and Software 212, 112031 (2024)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:40.676591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 72256e87-a454-42ea-8e14-febd2de5f051 · outbound

This paper cites In: Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk In: Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:40.394751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 451f4afb-3239-4658-809b-489851032370 · outbound

This paper cites an unresolved cited work.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:13:39.037469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2ac8d498-c1d7-4a5e-813c-e1c88b493809 · outbound

This paper cites In: Pro- ceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Confer- ence and Symposium on the Foundations of Software Engineering.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk In: Pro- ceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Confer- ence and Symposium on the Foundations of Software Engineering

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:38.896750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 57a01b32-c3ac-4880-9694-b2fa38376521 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:13:32.800356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a230064-b73f-48db-9fb8-84b773b1a6c5 · outbound

This paper cites Future Generation Computer Systems163, 107504 (2025).

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk Future Generation Computer Systems163, 107504 (2025)

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:38.524959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ae260348-8106-437f-a2f2-e2cdb946cf3a · outbound

This paper cites an unresolved cited work.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:13:38.264916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5af05784-2e6c-4902-a0ee-24e58cc51074 · outbound

This paper cites an unresolved cited work.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:13:38.005106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4a1153a4-5aad-40bb-8c76-792eb2868fcd · outbound

This paper cites IEEE Transactions on Information Forensics and Security16, 1943–1958 (2020).

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk IEEE Transactions on Information Forensics and Security16, 1943–1958 (2020)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:37.801615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation af01e145-320e-4537-8e36-dc7c07997af9 · outbound

This paper cites Ap- plied Sciences10(6), 1943 (2020).

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk Ap- plied Sciences10(6), 1943 (2020)

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:37.484525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 49586868-1428-47b8-8f54-2fac2dcf5e98 · outbound

This paper cites In: Proceedings of the 2024 IEEE/ACM 46th International Con- ference on Software Engineering: Companion Proceedings.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk In: Proceedings of the 2024 IEEE/ACM 46th International Con- ference on Software Engineering: Companion Proceedings

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:37.179082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 84a5b772-d60b-47de-a154-8800902764b1 · outbound

This paper cites Information and Software Technology 144, 106809 (2022).

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk Information and Software Technology 144, 106809 (2022)

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:36.991626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d7f1e7e7-2017-4f33-aa47-0a68908bff62 · outbound

This paper cites Algorithms14(11), 335 (2021).

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk Algorithms14(11), 335 (2021)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:36.829798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 251d9c39-f857-4c65-a047-c66c1c245649 · outbound

This paper cites In: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE).

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk In: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:36.501640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e3ba99b6-371b-494b-aac8-4c50340ea632 · outbound

This paper cites In: Proceedings of the 44th International Conference on Software Engineering.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk In: Proceedings of the 44th International Conference on Software Engineering

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:36.317458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e8eaef9b-919f-4a85-b349-7a0076dec55a · outbound

This paper cites In: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE).

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk In: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:36.086943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b64ffd2e-9766-4378-a806-0b7ae5610777 · outbound

This paper cites Journal of Information Security and Applications81, 103718 (2024).

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk Journal of Information Security and Applications81, 103718 (2024)

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:35.806999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cec28795-6f76-4b72-96ad-324b60ac90e0 · outbound

This paper cites Advances in neural information processing systems32(2019) A Appendix A.1 Generic Code Conversion LLM Prompt Generic code chunks converts code chunks in generic format.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk Advances in neural information processing systems32(2019) A Appendix A.1 Generic Code Conversion LLM Prompt Generic code chunks converts code chunks in generic format

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:35.605429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 30e80d78-a63d-41c4-a269-0c9841e93e27 · outbound

This paper cites Return these lines in a list of string named asline code.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk Return these lines in a list of string named asline code

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:35.332132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 422cba74-21fe-481d-a2d2-0772655052cf · outbound

This paper cites Return these line numbers in a list of integer named asvul lines.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk Return these line numbers in a list of integer named asvul lines

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:35.162111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation effdd6e4-f0a3-4b4b-b224-6c1c8516e2ac · outbound

This paper cites Return these in a list of string named as vul category.

FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk Return these in a list of string named as vul category

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:13:34.968541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation 8401a129-16cd-41ae-93c7-38b9c63504f9 · inbound

Can LLMs Find Bugs in Code? An Evaluation from Beginner Errors to Security Vulnerabilities in Python and C++ cites this paper.

Can LLMs Find Bugs in Code? An Evaluation from Beginner Errors to Security Vulnerabilities in Python and C++ FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-18T21:41:51.550675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 970ba9b8-827f-4b4a-9e78-6623962351f4 · inbound

Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity cites this paper.

Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-04T14:49:04.136085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04beb161-d80c-4f95-843d-b9e20edf793f · inbound

SemChunk-C: Semantic Segmentation for C Code cites this paper.

SemChunk-C: Semantic Segmentation for C Code FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-01T13:55:45.560437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.