Paper Citation Record · LEDGER

Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2407.02395.

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

pith.paper-citation-record.v1
2407.02395 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:55:14.361719Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T19:56:10.801171Z

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 ec566a92-e435-4bc6-8eb7-23f91e20f791 · inbound

LLM Performance for Code Generation on Noisy Tasks cites this paper.

LLM Performance for Code Generation on Noisy Tasks Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:43.375530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d273f42-c329-43ba-bd73-61fc2f03955b · inbound

CodeMirage: A Multi-Lingual Benchmark for Detecting AI-Generated and Paraphrased Source Code from Production-Level LLMs cites this paper.

CodeMirage: A Multi-Lingual Benchmark for Detecting AI-Generated and Paraphrased Source Code from Production-Level LLMs Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-07T13:55:14.361719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c725ddf-2f77-46cf-8740-b74455af6572 · inbound

Guiding AI to Fix Its Own Flaws: An Empirical Study on LLM-Driven Secure Code Generation cites this paper.

Guiding AI to Fix Its Own Flaws: An Empirical Study on LLM-Driven Secure Code Generation Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T21:56:17.536958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cfa7f88-6cea-4a69-8188-34069b47c5d3 · inbound

When Prompts Go Wrong: Evaluating Code Model Robustness to Ambiguous, Contradictory, and Incomplete Task Descriptions cites this paper.

When Prompts Go Wrong: Evaluating Code Model Robustness to Ambiguous, Contradictory, and Incomplete Task Descriptions Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T13:38:52.828980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31ac015f-04da-469f-8178-46ff07505fa0 · inbound

Secure Code Generation at Scale with Reflexion cites this paper.

Secure Code Generation at Scale with Reflexion Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T23:50:49.625959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f292a6ca-192e-4192-b4e7-dc142ce8f940 · inbound

Break Me If You Can: Self-Jailbreaking of Aligned LLMs via Lexical Insertion Prompting cites this paper.

Break Me If You Can: Self-Jailbreaking of Aligned LLMs via Lexical Insertion Prompting Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T18:08:13.072442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3a468b82-4d6b-4f18-9857-85c3e7ea6785 · inbound

Extracting Recurring Vulnerabilities from Black-Box LLM-Generated Software cites this paper.

Extracting Recurring Vulnerabilities from Black-Box LLM-Generated Software Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T05:18:44.638936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 453e945b-dba5-472b-91ce-450fa6c4044c · inbound

A Large-Scale Comprehensive Measurement of AI-Generated Code in Real-World Repositories cites this paper.

A Large-Scale Comprehensive Measurement of AI-Generated Code in Real-World Repositories Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:49:33.951503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2bfa60fa-e387-4c62-80ef-dea2b0e8cba1 · inbound

PYTHALAB-MERA: Validation-Grounded Memory, Retrieval, and Acceptance Control for Frozen-LLM Coding Agents cites this paper.

PYTHALAB-MERA: Validation-Grounded Memory, Retrieval, and Acceptance Control for Frozen-LLM Coding Agents Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:26:23.021556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 026c2599-6751-47d2-8c72-78782ab782ae · inbound

Refusal Evaluation in Coding LLMs and Code Agents: A Systematic Review of Thirteen Malicious-Code Prompt Corpora (2023-2025) cites this paper.

Refusal Evaluation in Coding LLMs and Code Agents: A Systematic Review of Thirteen Malicious-Code Prompt Corpora (2023-2025) Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:39:48.512231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 092fec05-4353-4187-b4a8-4812da081a03 · inbound

What Breaks When LLMs Code? Characterizing Operational Safety Failures of Agentic Code Assistants cites this paper.

What Breaks When LLMs Code? Characterizing Operational Safety Failures of Agentic Code Assistants Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval

Reference 85

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:56:10.802989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.