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Paper Citation Record · LEDGER

Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2409.07587.

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

pith.paper-citation-record.v1
2409.07587 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 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 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:50:07.922462Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T04:19:33.747087Z

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 9112907d-c0a1-4a38-8367-2b0d118db64c · inbound

Benchmarking LLMs in an Embodied Environment for Blue Team Threat Hunting cites this paper.

Benchmarking LLMs in an Embodied Environment for Blue Team Threat Hunting Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-15T20:50:07.922462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:50:07.922462Z digest=sha256:afb3f088523b64fef8ce7e85da5fb605b3ef9ad1fc76d88ffa389b619910c1fe

Observation 0cc2087f-46c7-410a-a717-16c3e7481581 · inbound

Forewarned is Forearmed: A Survey on Large Language Model-based Agents in Autonomous Cyberattacks cites this paper.

Forewarned is Forearmed: A Survey on Large Language Model-based Agents in Autonomous Cyberattacks Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-15T20:28:57.316371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:28:57.316371Z digest=sha256:4c45871bfe8bbd402d52068506fa1aa147bed6a7997eca8b120942317aa6c59c

Observation b988814d-deff-4e12-b1e0-681ec3e5cb7f · inbound

MGC: A Compiler Framework Exploiting Compositional Blindness in Aligned LLMs for Malware Generation cites this paper.

MGC: A Compiler Framework Exploiting Compositional Blindness in Aligned LLMs for Malware Generation Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T20:45:53.237300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:45:53.237300Z digest=sha256:22b6b6fe563862dd57e154e4664aa8eb910cf5d7fb748a36a3165802bc1e1b26

Observation 06a329de-618e-4063-b0ee-c47458e45284 · inbound

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection cites this paper.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T11:21:38.177587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:21:38.177587Z digest=sha256:2c775f061b57991f389b9ae1a10e488abeac13d850c022be612237d97f8940a3

Observation d283d974-b311-473f-9913-0cbf0943b459 · inbound

TraceRAG: A LLM-Based Framework for Explainable Android Malware Detection and Behavior Analysis cites this paper.

TraceRAG: A LLM-Based Framework for Explainable Android Malware Detection and Behavior Analysis Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-04T20:52:33.124418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T20:52:33.124418Z digest=sha256:c1aa9f8ea5829af5ed76a0988f2524db476b8b40caf9078b37d5cfa4cf1925b1

Observation ed287650-8aae-4599-8832-05804fc4637c · inbound

Benchmarking LLM-Assisted Blue Teaming via Standardized Threat Hunting cites this paper.

Benchmarking LLM-Assisted Blue Teaming via Standardized Threat Hunting Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-04T14:42:55.997378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T14:42:55.997378Z digest=sha256:30eba954543f56a693aa0f910cba09af0eb42995d9b09ffbdd01764e33d4ce4f

Observation 9087399d-dd4c-4bdf-946d-bbf2ac2d6306 · inbound

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? cites this paper.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-03T22:04:09.943222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:04:09.943222Z digest=sha256:4f9ccee4e4601e01cb4a25630b062554d3034362a868166349ca75e9e2f65e7e

Observation cd7ef27d-703b-45ca-ae00-7a70a11ee9b0 · inbound

Efficient Code Analysis via Graph Representation Learning-Guided Large Language Models cites this paper.

Efficient Code Analysis via Graph Representation Learning-Guided Large Language Models Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-03T09:44:53.702805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:44:53.702805Z digest=sha256:21c04f9a8c2357674fa4125693fa401acae2db0bb8d82b9d7e9b1f4f7a30cf54

Observation 0dc9d4a2-3869-4bab-bf9d-b368c578e526 · inbound

LCC-LLM: Leveraging Code-Centric Large Language Models for Malware Attribution cites this paper.

LCC-LLM: Leveraging Code-Centric Large Language Models for Malware Attribution Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-11T20:21:08.554813Z

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.

source=pdf_text observed=2026-05-08T09:34:17.991210Z digest=sha256:fd5ad02563efff76d813e11defaca597ab8391966d566a6c9e01f3d5d18e575c

Observation 9aa936aa-da7b-465c-94c6-4297e3cf5bff · inbound

Benchmarking Large Language Models for IoC Recovery under Adversarial Code Obfuscation and Encryption cites this paper.

Benchmarking Large Language Models for IoC Recovery under Adversarial Code Obfuscation and Encryption Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-11T04:50:58.042358Z

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.

source=pdf_text observed=2026-05-11T01:01:15.586948Z digest=sha256:29c1a9ae42efd1a19fb50c568fe7fd650eaff73cbd36849e44811bc4654fbb85

Observation 81e5f715-78c8-49a5-88fa-7f13393ab60d · inbound

A Large Language Model Approach to Generating Bypass Rules for Malware Evasion in Analysis Sandbox cites this paper.

A Large Language Model Approach to Generating Bypass Rules for Malware Evasion in Analysis Sandbox Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-22T08:31:16.951226Z

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.

source=pdf_text observed=2026-05-22T08:27:41.061397Z digest=sha256:b2d1ced1db0f837ddde5aab676f3129453c5dd849db64d8ac58851932cacacf9

Observation d1b926f8-8163-4b60-8f87-8c839c92abfe · inbound

Multi-View Decompilation for LLM-Based Malware Classification cites this paper.

Multi-View Decompilation for LLM-Based Malware Classification Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-07-04T04:19:33.749759Z

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.

source=arxiv_source observed=2026-06-26T17:10:44.510497Z digest=sha256:57190663bc53e525caa9ebd8a5a243070ba9dfe7a8694714e3e3fe37a0e16df5

Observation ae8e0396-234c-4d22-ba98-6560949a8ed1 · inbound

Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysis cites this paper.

Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysis Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-01T10:30:41.262977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T10:30:41.262977Z digest=sha256:503546fd71b973293df12110bc53bbc2909dc8f15e33ec8a8d89012de4313e57