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Paper Citation Record · LEDGER

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection

As of 12 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 2 inbound Pith citation observations for arXiv:2412.12039.

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

pith.paper-citation-record.v1
2412.12039 v3

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:24:49.805679Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T09:16:56.388348Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T09:18:10.083886Z

Reference resolution

42 of 42 outbound references displayed

  • verified exact4
  • verified fuzzy18
  • unresolved20
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1cb62418-1586-41a8-90d6-2c76bf7b7d5c · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection LLaMA: Open and Efficient Foundation Language Models

Reference 1

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no resolver link, observed 2026-08-11T14:24:49.665116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.665116Z digest=sha256:99123d5ba5b541aae652ef4d599b645f16e16b941a2ba8619d977a5b9abf6581

Observation 0e88c866-d5b1-49af-8539-9864eba1f117 · outbound

This paper cites StarCoder: may the source be with you!.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection StarCoder: may the source be with you!

Reference 2

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no resolver link, observed 2026-08-11T14:24:49.669673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.669673Z digest=sha256:0765c0eb47e906e048bd327d20f5c789f83017c92d01df88e65d20261b412eaf

Observation 25b4460a-92ae-49eb-8dfc-11d7d0fd785f · outbound

This paper cites Deep learning based vulnerability detection: Are we there yet?.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Deep learning based vulnerability detection: Are we there yet?

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:24:50.314174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.673669Z digest=sha256:e2ef114490d0d6de3ce9e4e8c706181c80ec2c20c4f44000dbb96609e34cca87

Observation dc905f1b-a651-4e73-9c2e-57b80f2eb255 · outbound

This paper cites Detecting false alarms from automatic static analysis tools: How far are we?.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Detecting false alarms from automatic static analysis tools: How far are we?

Reference 4

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unresolved
no resolver link, observed 2026-08-11T14:24:49.677494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.677494Z digest=sha256:b2faa0a6b183c85140372aebe05d00d8b3952accae22b04a9e8757afd563fa0c

Observation 6164e076-ac50-4c1e-987f-9065bd3ae431 · outbound

This paper cites Stack Overflow in Github: Any Snippets There?.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Stack Overflow in Github: Any Snippets There?

Reference 5

Resolution
verified exact
local_arxiv, observed 2026-08-11T14:24:49.984773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.681145Z digest=sha256:d7dd5a41f9a8873321aa3b6074b4345398877f7761a68cfe535b657d9bbdbd65

Observation 70c84686-01a6-4d39-b869-4b8b9d497037 · outbound

This paper cites Beyond dependencies: The role of copy-based reuse in open source software development,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Beyond dependencies: The role of copy-based reuse in open source software development,

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-11T14:24:49.684886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.684886Z digest=sha256:c0b80452504829ee276b29e157fba59f073ba88afa53dcdb0ead7d2e049a9fb1

Observation 6b8a1292-5389-4cff-8fef-c56d31edb535 · outbound

This paper cites How do developers reuse stackoverflow answers in their github projects?.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection How do developers reuse stackoverflow answers in their github projects?

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:24:50.304692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.688835Z digest=sha256:49f8b08e756beea83339a0d325ef846b21c312d1370cd5c32119e74d74c03788

Observation 2cbef76b-4913-44f7-9b0d-5fc730ed2fda · outbound

This paper cites Amazon Nova AI Challenge -- Trusted AI: Advancing secure, AI-assisted software development.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Amazon Nova AI Challenge -- Trusted AI: Advancing secure, AI-assisted software development

Reference 8

Resolution
verified exact
local_arxiv, observed 2026-08-11T14:24:49.970594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.692181Z digest=sha256:3d6aa6e0af2c22e8df3e8437066769c22fad60b2332b00d2b2224c85ded4ab5b

Observation 0f9fd3d4-053a-41f2-aba3-4f609f129dd2 · outbound

This paper cites AI is already writing about 30% of code at Microsoft and Google-here’s what it means for software engineers,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection AI is already writing about 30% of code at Microsoft and Google-here’s what it means for software engineers,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:24:50.294840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.695698Z digest=sha256:568269b2337d60f1d60bda341d10a1e3ca2382fdb8f67d31503b1be25cd871df

Observation d848ba02-d270-4de6-93be-8ea75915c335 · outbound

This paper cites Vulnerabilities in ai code generators: Exploring targeted data poisoning attacks,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Vulnerabilities in ai code generators: Exploring targeted data poisoning attacks,

Reference 10

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verified fuzzy
raw_fallback, observed 2026-08-11T14:24:50.284415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.699192Z digest=sha256:a081bf965a4eedcce2a66a790fd53deb31752251228df67d760397b926efe25b

Observation 39ddc62f-7b51-4193-afab-2236bfb40cc0 · outbound

This paper cites Just another copy and paste? comparing the security vulnerabilities of chatgpt generated code and stackoverflow answers,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Just another copy and paste? comparing the security vulnerabilities of chatgpt generated code and stackoverflow answers,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:24:50.273457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.702539Z digest=sha256:71d0badc8b770d505bb6e9e36645bcfab59df811d7e7687f4a4406e5c93f2f88

Observation 65f1472d-d6c1-4fa2-8286-fa099af61c1f · outbound

This paper cites How secure is code generated by chatgpt?.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection How secure is code generated by chatgpt?

Reference 12

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unresolved
no resolver link, observed 2026-08-11T14:24:49.706200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.706200Z digest=sha256:f7fa1bcbf210aa4cbe03df34b090d0e246291af0740e001ee9ce64328db59075

Observation b5a9a65b-701a-4ca1-bcf4-4058846cd07f · outbound

This paper cites Asleep at the keyboard? assessing the security of github copilot’s code con- tributions,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Asleep at the keyboard? assessing the security of github copilot’s code con- tributions,

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-11T14:24:49.709703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.709703Z digest=sha256:82c2ea97f2085e1ee58508ce4ae6b36388b6de63bc8fbf7a10e3365238ecee81

Observation 941680e2-a134-4097-8ca3-d854f43293ef · outbound

This paper cites Devign: Effective vul- nerability identification by learning comprehensive program semantics via graph neural networks,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Devign: Effective vul- nerability identification by learning comprehensive program semantics via graph neural networks,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:24:50.250385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.712860Z digest=sha256:68bc2f57d0c92d16c826312b55179cc7a65e7b762232293746ba87ab944f333b

Observation 136c73a4-48e0-4406-91fc-dce2a31cff06 · outbound

This paper cites Vulnerability detection with fine- grained interpretations,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Vulnerability detection with fine- grained interpretations,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:24:50.238853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.716290Z digest=sha256:2c1a017544762b766e044e12b5feef3c4a9bd41c12a09fe463091ccb46b9ecee

Observation a368eb70-3500-4255-a674-522d7ebbf83c · outbound

This paper cites Linevd: Statement-level vulnerability detection using graph neural networks,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Linevd: Statement-level vulnerability detection using graph neural networks,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:24:50.227330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.719581Z digest=sha256:f97b525f4963a0f4269e364c4af93acfddbc8a15095d4c7aaaa02679b88383e2

Observation d73e4309-26aa-4990-bccc-08d1bcd12a7d · outbound

This paper cites To Err is Machine: Vulnerability Detection Challenges LLM Reasoning.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection To Err is Machine: Vulnerability Detection Challenges LLM Reasoning

Reference 17

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no resolver link, observed 2026-08-11T14:24:49.722561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.722561Z digest=sha256:2e54c630bffcc25350ab3d15278759d8cae330d4ca49ad10889c163a868e0392

Observation 5b71ed45-6191-486a-9c33-4acc9e3c59aa · outbound

This paper cites Vulnerability Detection with Code Language Models: How Far Are We?.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Vulnerability Detection with Code Language Models: How Far Are We?

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-11T14:24:49.725996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.725996Z digest=sha256:b56c91719738229cf7985a1d4196281c2a4599809d27610ce431e69973026a97

Observation c8cbb3b5-2afe-478b-b80f-3b743501ddf7 · outbound

This paper cites Llms cannot reliably identify and reason about security vulnerabilities (yet?): A comprehensive evaluation, framework, and benchmarks,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Llms cannot reliably identify and reason about security vulnerabilities (yet?): A comprehensive evaluation, framework, and benchmarks,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:24:50.216810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.729268Z digest=sha256:0e7bc1f6e31203e82316c088e4b13bfb8e012b4ba873e93d46b52db57036c8ea

Observation 39d2be9d-14b1-4016-959c-61aad804ca2b · outbound

This paper cites Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 20

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unresolved
no resolver link, observed 2026-08-11T14:24:49.732274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.732274Z digest=sha256:4f488b2d4a9a2dd74ddd1e0b958204e83982cffd2553c4d17117bc133a3f6c85

Observation 0590884d-5aae-42cb-b7ad-811d15e0039b · outbound

This paper cites Finetuning large language models for vulnerability detection,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Finetuning large language models for vulnerability detection,

Reference 21

Resolution
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no resolver link, observed 2026-08-11T14:24:49.735296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.735296Z digest=sha256:2649fd185c7b971e5da05378684bdeec7d21f27fe0030005e884f43d4a58a2f4

Observation b572e7d9-3330-48f4-8a19-a592654abb19 · outbound

This paper cites Grace: Empowering llm-based software vulnerability detection with graph structure and in- context learning,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Grace: Empowering llm-based software vulnerability detection with graph structure and in- context learning,

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-11T14:24:49.738555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.738555Z digest=sha256:2dbdbfa5c78dbc728ba0e2dee53b0520f126408b0d7fbb4d9760d708d5ca6c94

Observation 51483521-370a-4512-a833-66ffbc961fd3 · outbound

This paper cites Large language models for code: Security hardening and adversarial testing,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Large language models for code: Security hardening and adversarial testing,

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-11T14:24:49.741675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.741675Z digest=sha256:3a218eb3edfa2854dee2bb668ce395fd61bf22bf5f7f3c404ac3f83e2bee4be8

Observation b8f5d453-263b-4f0c-a111-816fb09b0452 · outbound

This paper cites Cvefixes: Automated collec- tion of vulnerabilities and their fixes from open-source software,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Cvefixes: Automated collec- tion of vulnerabilities and their fixes from open-source software,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:24:50.188402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.744799Z digest=sha256:bf45efac61ad3cffd7168ab25d7633e902fad416f9a852f868241352b0990721

Observation 8d3adf70-bd65-489d-bd2b-452f637b30db · outbound

This paper cites Symbolic Security Predicates: Hunt Program Weaknesses.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Symbolic Security Predicates: Hunt Program Weaknesses

Reference 25

Resolution
verified exact
local_arxiv, observed 2026-08-11T14:24:49.930932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.747949Z digest=sha256:c15144ac669342b4fee2e90ae51da8e789d8908afb7c12adc3cb8807af96eb51

Observation b26e67e1-133a-4ca1-b4af-fe6f74bb26e6 · outbound

This paper cites Amazon codeguru security,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Amazon codeguru security,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:24:50.178191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.751545Z digest=sha256:70924c95489e12545d44a80dfe24ba1f55aa7adef36759ade0bc0dd0f0abbfcd

Observation 152ab3c8-3fac-4b03-9bbc-3c62a53781df · outbound

This paper cites Semgrep: Lightweight static analysis for many languages,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Semgrep: Lightweight static analysis for many languages,

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:24:50.169070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.754666Z digest=sha256:50faa3c6ba1fcc6841a90d96257d4088b3b8727ce194ac775b22a8f3f6be9b5e

Observation 34aed1ea-a918-409e-adc5-3c9f224764e7 · outbound

This paper cites Codeql: Semantic code analysis engine,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Codeql: Semantic code analysis engine,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:24:50.159256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.758066Z digest=sha256:769c8f3fa488c2ae793274a3dd3ff7a75a28cce48b54b86f3a5fc5905f31a10d

Observation aa9452ba-2ef8-4900-9803-8983d32d2e9a · outbound

This paper cites Sonarqube: Continuous inspection of code quality,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Sonarqube: Continuous inspection of code quality,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:24:50.148126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.761146Z digest=sha256:46579cc1c90390627eb04c9527d51bbe5868bb0a0a712bc34cf945fcdaf0ff14

Observation a3b05baf-57ee-4e02-89ec-27e7472c74ef · outbound

This paper cites Automated vulnerability detection in source code using deep representation learning,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Automated vulnerability detection in source code using deep representation learning,

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:24:50.137755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.764103Z digest=sha256:f8f58cf026f797d1252744c7921c5e9c72e520f085b8d73de628f3b0d6735eca

Observation 21d42c4b-7220-4de9-ba75-3ae78d894f14 · outbound

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

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection CodeBERT: A Pre-Trained Model for Programming and Natural Languages

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-11T14:24:49.767235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.767235Z digest=sha256:c88a8d775f68d8572a53eaa1cd0c99f017fbda9b7828f4f7f8c371b445372b88

Observation b1d44815-798e-49ce-931b-b746e4b66d55 · outbound

This paper cites Linevul: A transformer-based line- level vulnerability prediction,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Linevul: A transformer-based line- level vulnerability prediction,

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:24:50.127254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.770917Z digest=sha256:e3e2ce66d4c2a557cf3b8d23f2c279388947092855dced51e1ab2ac38b19dda7

Observation 49690c62-6202-40ea-a8bc-879467cf70cf · outbound

This paper cites UniXcoder: Unified Cross-Modal Pre-training for Code Representation.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection UniXcoder: Unified Cross-Modal Pre-training for Code Representation

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-11T14:24:49.774198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.774198Z digest=sha256:cf5daa4d3bb3dcd14e5622c7bec2a2631e6a10c7073e80584b8f12bb96b16b58

Observation 2a52a07c-591e-49f6-a931-4ed8ec2f655e · outbound

This paper cites Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-11T14:24:49.777691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.777691Z digest=sha256:e403398a305487af9a6a558e737f3dec8721175779557d04ff8418e41db04611

Observation 7ee68ffd-a58a-48d4-b596-ce3e53517c18 · outbound

This paper cites LLMxCPG: Context-Aware Vulnerability Detection Through Code Property Graph-Guided Large Language Models.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection LLMxCPG: Context-Aware Vulnerability Detection Through Code Property Graph-Guided Large Language Models

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-11T14:24:49.781374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.781374Z digest=sha256:151cb0b9e5731cd5a0c9cb3033e59fdec9701152d3ba0fe0988f07ee5127adc7

Observation a78bd7c0-830e-4deb-8ada-16ce2d116f35 · outbound

This paper cites How Far Have We Gone in Vulnerability Detection Using Large Language Models.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection How Far Have We Gone in Vulnerability Detection Using Large Language Models

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-11T14:24:49.785110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.785110Z digest=sha256:1580c8d2547289659e0cbcc3cb1101876375b702461ddfc3967ff3caabe766a0

Observation aefbd5c0-60a9-4e0a-b518-e56c7d3a0480 · outbound

This paper cites Realvul: Can we detect vulnerabilities in web applications with llm?.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Realvul: Can we detect vulnerabilities in web applications with llm?

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:24:50.116806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.788767Z digest=sha256:00f429509b19d365ee06582ed9c03b2a70168247b67bd29b247a1449fb6a69cc

Observation 93874a9b-9515-45a6-9221-8517a24ee8c1 · outbound

This paper cites From Generalist to Specialist: Exploring CWE-Specific Vulnerability Detection.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection From Generalist to Specialist: Exploring CWE-Specific Vulnerability Detection

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-11T14:24:49.791985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.791985Z digest=sha256:d568a78d200d406f376ac1dfabbd22bec3bdb3d5f87baf8fe6b39b5c32be54af

Observation c91d2b9e-41a7-4e9d-9bd3-4973959d5872 · outbound

This paper cites One-for-all does not work! enhancing vulnerability detection by mixture-of-experts (moe),.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection One-for-all does not work! enhancing vulnerability detection by mixture-of-experts (moe),

Reference 39

Resolution
verified exact
doi, observed 2026-08-11T14:24:49.837627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.795538Z digest=sha256:09e8828b79fa1eca5ec90a4c1c0003bfa9fc321733b4a7093a0a0476c8cef526

Observation a5aad842-0efa-431d-8532-e29130a94a81 · outbound

This paper cites Applying contrastive learning to code vulnerability type classification,.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection Applying contrastive learning to code vulnerability type classification,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:24:50.105422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T14:24:49.798692Z digest=sha256:eec2c252ea3ef3b837b147440e88bcf42a087fbed945fd1c626167348a9316ba

Observation fa327631-129a-4709-a099-99edea1293c4 · outbound

This paper cites ChatGPT for Vulnerability Detection, Classification, and Repair: How Far Are We?.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection ChatGPT for Vulnerability Detection, Classification, and Repair: How Far Are We?

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-11T14:24:49.801977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.801977Z digest=sha256:e4fb1bfb32c08421c76e0c17f6146788311497710915d43f932930ae4f0a4918

Observation ad4a2401-54b0-4d59-beac-48fe3494e813 · outbound

This paper cites IRIS: LLM-Assisted Static Analysis for Detecting Security Vulnerabilities.

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection IRIS: LLM-Assisted Static Analysis for Detecting Security Vulnerabilities

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-11T14:24:49.805679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:24:49.805679Z digest=sha256:a7f21798b8b58c2060a432c37f7ecf0ba1e67d70f791234ed8f5879932720150

Pith citing papers

Observation 0c489996-2647-4d31-917a-f57c6fbe1ad1 · inbound

Do Skill Descriptions Tell the Truth? Detecting Undisclosed Security Behaviors in Code-Backed LLM Skills cites this paper.

Do Skill Descriptions Tell the Truth? Detecting Undisclosed Security Behaviors in Code-Backed LLM Skills Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-14T19:07:51.305289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-14T19:06:00.332789Z digest=sha256:00b00a92cc3d768918cf01e60604665fd62e830f91c3075febba7b759f5e4f2a

Observation b085b9dc-e912-49aa-8b60-69044b3b5b37 · inbound

Three Heads Are Better Than One: A Multi-perspective Reasoning Framework for Enhanced Vulnerability Detection cites this paper.

Three Heads Are Better Than One: A Multi-perspective Reasoning Framework for Enhanced Vulnerability Detection Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-20T09:18:10.085520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-20T09:16:56.388348Z digest=sha256:ed51a6ed614472dfefbc3cb1374b22093dbc38530921048adacda980ee0c1fd4