Paper Citation Record · LEDGER

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks

As of 20 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2507.10054.

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

pith.paper-citation-record.v1
2507.10054 v2

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:46:00.710652Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T13:56:46.667386Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

27 of 27 outbound references displayed

verified exact3
verified fuzzy7
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 97b13014-60c2-4fa5-8dd7-b88905e1a052 · outbound

This paper cites Large language models for software engineering: Sur- vey and open problems,.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Large language models for software engineering: Sur- vey and open problems,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T17:45:57.547463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbc3550e-0ecc-4e2b-8d84-f5bd4a920cf3 · outbound

This paper cites Large language models for software engi- neering: A systematic literature review,.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Large language models for software engi- neering: A systematic literature review,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T17:45:57.614923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcbe25da-159a-4f35-9866-bcf18d777c55 · outbound

This paper cites SOK: Exploring Hallucinations and Security Risks in AI-Assisted Software Development with Insights for LLM Deployment.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks SOK: Exploring Hallucinations and Security Risks in AI-Assisted Software Development with Insights for LLM Deployment

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:46:01.631348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4879e9c0-ad66-4162-aec1-b42e972c7b8f · outbound

This paper cites Do users write more insecure code with ai assistants?.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Do users write more insecure code with ai assistants?

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T17:45:57.952129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f73178d-e858-4621-b1b7-e47108862f7c · outbound

This paper cites How secure is ai-generated code: a large-scale comparison of large language models,.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks How secure is ai-generated code: a large-scale comparison of large language models,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:46:02.552664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0b1a0f62-d265-4249-aaee-8f67d32d49aa · outbound

This paper cites Large language model for vulnerability detection and repair: Literature review and the road ahead,.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Large language model for vulnerability detection and repair: Literature review and the road ahead,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:46:02.418623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 47108bc9-e6b0-48df-a5cc-ffe314623e52 · outbound

This paper cites PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T17:45:58.463121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e63b4ed-a605-42be-bee0-ef0f041c79d5 · outbound

This paper cites Codeattack: Code-based adversarial attacks for pre-trained programming language models,.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Codeattack: Code-based adversarial attacks for pre-trained programming language models,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:46:02.310124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dda1b033-61d2-4105-9240-05780f8dee01 · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T17:45:58.756338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7d53857-e651-4c73-91f9-6b669162d482 · outbound

This paper cites Code Red! On the Harmfulness of Applying Off-the-shelf Large Language Models to Programming Tasks.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Code Red! On the Harmfulness of Applying Off-the-shelf Large Language Models to Programming Tasks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T17:45:58.899317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7d19855-612e-47c8-b8cd-0439326844db · outbound

This paper cites Smoke and Mirrors: Jailbreaking LLM-based Code Generation via Implicit Malicious Prompts.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Smoke and Mirrors: Jailbreaking LLM-based Code Generation via Implicit Malicious Prompts

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T17:45:59.021956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1d43001-6362-4cc1-9ec4-29c41aeaf19d · outbound

This paper cites Rmcbench: Benchmarking large language models’ resistance to malicious code,.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Rmcbench: Benchmarking large language models’ resistance to malicious code,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:46:02.198386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2aea4d47-6aa5-4df7-a547-9410540f0c3e · outbound

This paper cites Prompting techniques for secure code generation: A systematic investigation,.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Prompting techniques for secure code generation: A systematic investigation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:46:02.098511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d0620f5d-573e-4fa2-8384-c8c813a13187 · outbound

This paper cites Insights and Current Gaps in Open-Source LLM Vulnerability Scanners: A Comparative Analysis.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Insights and Current Gaps in Open-Source LLM Vulnerability Scanners: A Comparative Analysis

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T17:45:59.497304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88ad85a3-e094-478c-bf6c-3caa9ea4752c · outbound

This paper cites Large language models and code security: A systematic literature review,.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Large language models and code security: A systematic literature review,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T17:45:59.630521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4bedc37-6cf1-44ed-a87d-0a05b3c1d9bd · outbound

This paper cites Do llms consider security? an empirical study on responses to programming questions,.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Do llms consider security? an empirical study on responses to programming questions,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:46:01.980103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ca50e69e-fccc-42c9-a28b-ab7b74e9831b · outbound

This paper cites Attribution-guided adversarial code prompt generation for code completion models,.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Attribution-guided adversarial code prompt generation for code completion models,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T17:46:00.138933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee72f87d-2f44-4743-9d6a-aef92a1fc866 · outbound

This paper cites Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T17:46:00.241746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b03a1b6b-245f-472e-b312-c9c4899b25b5 · outbound

This paper cites Available: https://doi.org/10.1007/s10664-025-10658-6.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Available: https://doi.org/10.1007/s10664-025-10658-6

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T17:45:59.988321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1729a7fb-0329-4771-a40a-779bb4668890 · outbound

This paper cites Secreevalbench: A multi-turned security resilience evaluation benchmark for large language models,.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Secreevalbench: A multi-turned security resilience evaluation benchmark for large language models,

Reference 21

Resolution: verified exact; raw_fallback, observed 2026-08-06T17:46:01.067787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a59aafa5-1cc9-476b-88ee-b521406cc136 · outbound

This paper cites Vulgen: Realistic vulnerability generation via pattern mining and deep learning,.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Vulgen: Realistic vulnerability generation via pattern mining and deep learning,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T17:46:00.468027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e20a69d-e557-461d-9906-d5289f9f9710 · outbound

This paper cites Analysing safety risks in llms fine-tuned with pseudo-malicious cyber security data,.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Analysing safety risks in llms fine-tuned with pseudo-malicious cyber security data,

Reference 23

Resolution: verified exact; raw_fallback, observed 2026-08-06T17:46:01.248884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 864eb6ec-b404-48a6-8c56-bf9254111e6f · outbound

This paper cites A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T17:46:00.640707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e8d2fb1-9dcd-42c4-a585-826548c7cdef · outbound

This paper cites ESBMC: Efficient SMT-Based Context-Bounded Model Checker,.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks ESBMC: Efficient SMT-Based Context-Bounded Model Checker,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:46:01.814643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 82624aa6-079b-4213-b2dd-ae1b3ef4298c · outbound

This paper cites Vgx: Large-scale sample generation for boosting learning-based software vul- nerability analyses,.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Vgx: Large-scale sample generation for boosting learning-based software vul- nerability analyses,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T17:46:00.555880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9031223d-eacd-4db8-9648-40cdeb73bc49 · outbound

This paper cites Available: https://doi.org/10.48550/arXiv.2412.15004.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Available: https://doi.org/10.48550/arXiv.2412.15004

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T17:45:59.781497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b38ae34a-54d3-4684-bc65-49a30c9ea40f · outbound

This paper cites Available: https://doi.org/10.1007/s10664-024-10590-1.

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks Available: https://doi.org/10.1007/s10664-024-10590-1

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T17:45:58.192599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 7e1e3bcf-f60f-4d81-a747-5e965f38afee · inbound

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference cites this paper.

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-02T13:56:46.667386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.