Paper Citation Record · LEDGER

Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2310.02059.

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

pith.paper-citation-record.v1
2310.02059 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:30:24.961869Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-05T01:40:34.397804Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation eed53ce1-39c8-440b-b4a3-9697d043d79f · inbound

One Size Does Not Fit All: Investigating Efficacy of Perplexity in Detecting LLM-Generated Code cites this paper.

One Size Does Not Fit All: Investigating Efficacy of Perplexity in Detecting LLM-Generated Code Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T10:32:47.730759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 099ebb7d-b0e6-4a4b-a15b-4f8c77969fde · inbound

Correctness Assessment of Code Generated by Large Language Models Using Internal Representations cites this paper.

Correctness Assessment of Code Generated by Large Language Models Using Internal Representations Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-10T16:41:31.211096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d6dd596-0f46-4873-b478-2d286e71d9e6 · inbound

Benchmarking Prompt Engineering Techniques for Secure Code Generation with GPT Models cites this paper.

Benchmarking Prompt Engineering Techniques for Secure Code Generation with GPT Models Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T17:00:30.448721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9359158b-b2e7-4c79-92d0-ddcf78e5c49b · inbound

The Hidden Risks of LLM-Generated Web Application Code: A Security-Centric Evaluation of Code Generation Capabilities in Large Language Models cites this paper.

The Hidden Risks of LLM-Generated Web Application Code: A Security-Centric Evaluation of Code Generation Capabilities in Large Language Models Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T05:30:24.961869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d66c2f71-9511-433f-883c-a0847ab1fcfd · 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 Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T13:55:15.068058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5e9f135-da56-4594-b5bb-ac672c4f9e28 · inbound

ReCatcher: Towards LLMs Regression Testing for Code Generation cites this paper.

ReCatcher: Towards LLMs Regression Testing for Code Generation Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T17:57:05.800875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2985fa38-bf33-4e7f-b48f-13bd3d6b00ea · inbound

Human-Written vs. AI-Generated Code: A Large-Scale Study of Defects, Vulnerabilities, and Complexity cites this paper.

Human-Written vs. AI-Generated Code: A Large-Scale Study of Defects, Vulnerabilities, and Complexity Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T14:09:55.369454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a887214-ef51-45b2-8693-60a269296051 · inbound

A Quasi-Experimental Developer Study of Security Training in LLM-Assisted Web Application Development cites this paper.

A Quasi-Experimental Developer Study of Security Training in LLM-Assisted Web Application Development Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:28:39.218726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 15df1dd8-c126-4246-9343-3c98fd1f3b79 · inbound

SecureForge: Finding and Preventing Vulnerabilities in LLM-Generated Code via Prompt Optimization cites this paper.

SecureForge: Finding and Preventing Vulnerabilities in LLM-Generated Code via Prompt Optimization Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:26:24.626270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 150a70db-ade1-4e33-bf92-2c3323d9470f · inbound

Enhancing Reliability in LLM-Based Secure Code Generation cites this paper.

Enhancing Reliability in LLM-Based Secure Code Generation Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-06-30T15:14:46.816613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0eb551e0-70fc-4c00-8002-90df0c4faea3 · inbound

Short-Term Gain, Long-Term Fragility: AI Labor Substitution and the Erosion of Sustainable Capability cites this paper.

Short-Term Gain, Long-Term Fragility: AI Labor Substitution and the Erosion of Sustainable Capability Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-05T01:40:34.400611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7a99d616-2e08-4f21-ad82-ca6df9c50f71 · inbound

Insecure Coding Preferences in Long-Term Memory: Security Risks for LLM-based Code Generation cites this paper.

Insecure Coding Preferences in Long-Term Memory: Security Risks for LLM-based Code Generation Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T17:31:51.584796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b25a36a-1c4a-4f9f-b882-3d478bf15978 · inbound

Can AI Write Compliant Code, and to What Extent? Evaluating SOC 2 Compliance of Claude Fable 5, Claude Opus 4.8, and Claude Opus 5 Across Four Use Cases cites this paper.

Can AI Write Compliant Code, and to What Extent? Evaluating SOC 2 Compliance of Claude Fable 5, Claude Opus 4.8, and Claude Opus 5 Across Four Use Cases Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T04:17:02.689121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8a703fb-1ed9-4748-a764-fe27d6e6e62a · inbound

Activation Probes Surface Code-Security Signals that the Model's Output Misses cites this paper.

Activation Probes Surface Code-Security Signals that the Model's Output Misses Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T13:35:03.860136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.