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Paper Citation Record · LEDGER

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

As of 10 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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-23T20:58:16.237327Z digest=sha256:613b455a15efb3228a900c0536c890a1eccd2951b60a79c4d482b229f76c5dc9

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-23T04:37:59.969975Z digest=sha256:04d414e2002f2ab60b919ace4bb2b71a87a0e410227ef3e74e31659736410f47

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-23T02:35:35.639750Z digest=sha256:e0e14bd6d38cffab9d90d692d48dbfdafee22ee802aa2bc77fc849ee14d93a40

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-18T05:24:25.622071Z digest=sha256:b7fd807e5b8b9c6c37c927281fb786aed04c8f532388d7a0deab7b94b9642988

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-08T18:25:05.586464Z digest=sha256:aa7fc8c05f51d0004092fedfd1618dfc9968efefd5e0186a9b33de64df6d773a

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-07-01T05:42:10.497856Z digest=sha256:a3bfe2a12ee78b8fe76fcf2ab49a9475e2d0185bd059f7a34bc9fb71855f79bb