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Paper Citation Record · LEDGER

A Survey of Recent Backdoor Attacks and Defenses in Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2406.06852.

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

pith.paper-citation-record.v1
2406.06852 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:47:15.427626Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T10:15:44.407671Z

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 ddaba08f-8856-4763-90b4-45e359951436 · inbound

Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey cites this paper.

Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey A Survey of Recent Backdoor Attacks and Defenses in Large Language Models

Reference 180

Resolution
verified exact
arxiv_id, observed 2026-05-23T20:58:26.433054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T20:58:16.237327Z digest=sha256:c0f76416bc7c0208f45fdb133562c2d8e8d04e4f0be6b6f2e33149529c7b8791

Observation 92908b84-f482-4baf-bd3d-479823319aa0 · inbound

Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation cites this paper.

Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation A Survey of Recent Backdoor Attacks and Defenses in Large Language Models

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-23T04:42:35.090883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T04:37:59.969975Z digest=sha256:cf80a29341687f2d245c0ad861a084e60c98cddedb9121f8c14131d5fca2bba1

Observation c52482b9-2f28-4048-80f8-85433775e0de · inbound

Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities cites this paper.

Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities A Survey of Recent Backdoor Attacks and Defenses in Large Language Models

Reference 89

Resolution
unresolved
no resolver link, observed 2026-08-09T14:47:15.427626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T14:47:15.427626Z digest=sha256:ccb7d4496a1b2cb7a93c8323a4c9ad9e6a3aafb94669fec9205b7fb9db0ae439

Observation d4c59593-2428-4189-a8e9-350587cd2a94 · inbound

Gungnir: Exploiting Stylistic Features in Images for Backdoor Attacks on Diffusion Models cites this paper.

Gungnir: Exploiting Stylistic Features in Images for Backdoor Attacks on Diffusion Models A Survey of Recent Backdoor Attacks and Defenses in Large Language Models

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-23T02:37:25.687974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T02:35:35.639750Z digest=sha256:2615b85e4925cf9eb834cf70be879a486ae4bbe013d20dbacc9c2f380333fa68

Observation 5b27d4bc-ccb3-4bcc-90b6-2298c7efe159 · inbound

MEraser: An Effective Fingerprint Erasure Approach for Large Language Models cites this paper.

MEraser: An Effective Fingerprint Erasure Approach for Large Language Models A Survey of Recent Backdoor Attacks and Defenses in Large Language Models

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-07T00:51:20.499535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:51:20.499535Z digest=sha256:10922029f6dde5307a7bb8e821e433d5f1563200cfa3161115f188c312c51fee

Observation 19220c8f-b5dc-4c40-9f04-7ffa7dae0097 · inbound

Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption cites this paper.

Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption A Survey of Recent Backdoor Attacks and Defenses in Large Language Models

Reference 71

Resolution
verified exact
arxiv_id, observed 2026-05-18T05:25:54.532819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T05:24:25.622071Z digest=sha256:a14cfc831a8c21f229914d3d38522583e60f1e4462feee0a515c997432bb0294

Observation b9f4da72-1894-4442-8aa8-efa60b306167 · inbound

On the Privacy of LLMs: An Ablation Study cites this paper.

On the Privacy of LLMs: An Ablation Study A Survey of Recent Backdoor Attacks and Defenses in Large Language Models

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-09T06:25:48.860158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T18:25:05.586464Z digest=sha256:16999cfd1080ed3b7afbac1a3538b258187730cbc1a014c97def7f5ac7aba27e

Observation 67611283-6140-4380-9b7e-5ee0e0bd1b5f · inbound

CSO-LLM: Class Subspace Orthogonalization for Post-Training Backdoor Detection and Trigger Inversion in LLMs cites this paper.

CSO-LLM: Class Subspace Orthogonalization for Post-Training Backdoor Detection and Trigger Inversion in LLMs A Survey of Recent Backdoor Attacks and Defenses in Large Language Models

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-07-01T10:15:44.408974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-07-01T05:42:10.497856Z digest=sha256:bf0378f6680bd9ae7abc75e4094bea8c62a1574d223632320c476ec20dfe3327