Paper Citation Record · LEDGER

Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2402.12168.

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

pith.paper-citation-record.v1
2402.12168 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:50:00.774159Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T21:46:35.204845Z

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 c2089771-eff6-4feb-84fc-d2532b7c7a5f · inbound

A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks, Defenses, and Evaluations cites this paper.

A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks, Defenses, and Evaluations Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning

Reference 179

Resolution: unresolved; no resolver link, observed 2026-08-09T00:50:00.774159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9909c6b8-19a9-45bd-92c7-d908ade9326e · inbound

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks cites this paper.

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-08T17:43:24.844859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd53e15a-c0e7-48ba-97b1-c0bff164a097 · inbound

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution cites this paper.

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-05T14:42:31.558204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a9e253d-44cc-47f1-ba57-954dc7b0063a · inbound

Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates cites this paper.

Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:35:52.782863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 528ff828-b1f3-439b-a2b1-8ca036da4e72 · inbound

Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates cites this paper.

Reference 45

Resolution: unresolved; no resolver link, observed 2026-07-13T09:41:56.414542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 995d8c02-d0ec-4ad6-9ed3-e87462ddcb7a · inbound

Unveiling the Backdoor Mechanism Hidden Behind Catastrophic Overfitting in Fast Adversarial Training cites this paper.

Unveiling the Backdoor Mechanism Hidden Behind Catastrophic Overfitting in Fast Adversarial Training Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning

Reference 97

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:46:35.222998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.