Paper Citation Record · LEDGER

Weight Poisoning Attacks on Pre-trained Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2004.06660.

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

pith.paper-citation-record.v1
2004.06660 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 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 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:50:00.151000Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T19:50:11.061646Z

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 6ba8a3e5-4073-4db9-b86a-b37652bb2915 · inbound

Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models cites this paper.

Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models Weight Poisoning Attacks on Pre-trained Models

Reference 150

Resolution: verified exact; arxiv_id, observed 2026-05-17T14:43:30.183744Z

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.

Observation 73974b64-3012-44fc-9c45-0b589fcdda11 · 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 Weight Poisoning Attacks on Pre-trained Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-09T00:50:00.151000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c90c598b-7aab-4022-900f-9d8a214daf62 · inbound

Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques cites this paper.

Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques Weight Poisoning Attacks on Pre-trained Models

Reference 160

Resolution: unresolved; no resolver link, observed 2026-08-06T16:24:30.419753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 204a80ca-88e5-49b0-8b27-aad8b3a8087c · inbound

MOCHA: Are Code Language Models Robust Against Multi-Turn Malicious Coding Prompts? cites this paper.

MOCHA: Are Code Language Models Robust Against Multi-Turn Malicious Coding Prompts? Weight Poisoning Attacks on Pre-trained Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T14:17:34.028517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8591055-0a00-4a95-9bed-03007b7941cb · inbound

A Survey on Data Security in Large Language Models cites this paper.

A Survey on Data Security in Large Language Models Weight Poisoning Attacks on Pre-trained Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T05:05:02.298693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50c8f818-a136-43e6-941d-e832e78c06fb · inbound

SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models cites this paper.

SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models Weight Poisoning Attacks on Pre-trained Models

Reference 36

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T23:28:40.705090Z

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.

Observation 868621a2-cd07-4e0a-a4d2-f025a4895770 · inbound

Follow My Eyes: Backdoor Attacks on Goal-Directed Scanpath Prediction cites this paper.

Follow My Eyes: Backdoor Attacks on Goal-Directed Scanpath Prediction Weight Poisoning Attacks on Pre-trained Models

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:45:58.607550Z

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.

Observation a64fc9ff-bfd9-4bfb-9cdd-c9fe29a931fc · inbound

Follow My Eyes: Backdoor Attacks on Goal-Directed Scanpath Prediction cites this paper.

Follow My Eyes: Backdoor Attacks on Goal-Directed Scanpath Prediction Weight Poisoning Attacks on Pre-trained Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-12T23:44:14.956966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ea8a53c-a01d-482a-8a91-9f36d326e222 · 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 Weight Poisoning Attacks on Pre-trained Models

Reference 99

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T21:46:35.182780Z

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.

Observation df8d92c3-d496-4e11-9278-3978feda422c · inbound

The Invitation Trap: Proactive Availability Backdoor in LLMs via Conversational Induction cites this paper.

The Invitation Trap: Proactive Availability Backdoor in LLMs via Conversational Induction Weight Poisoning Attacks on Pre-trained Models

Reference 44

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T20:22:37.842877Z

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.

Observation e9956566-8cb4-43ad-9d49-4d6567db9874 · inbound

A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation cites this paper.

A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation Weight Poisoning Attacks on Pre-trained Models

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:50:11.063148Z

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.

Observation 50668df4-5f0e-47c3-af87-7a030b3e9d48 · inbound

A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation cites this paper.

A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation Weight Poisoning Attacks on Pre-trained Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-02T10:16:38.047621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.