Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Concealed Data Poisoning Attacks on NLP Models
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2010.12563.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T05:59:33.565741Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T19:50:11.208012Z
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 1b68074a-fca0-4a99-996a-a679b9349ffc · inbound
A Systematic Review of Poisoning Attacks Against Large Language Models cites this paper.
A Systematic Review of Poisoning Attacks Against Large Language Models Concealed Data Poisoning Attacks on NLP Models
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:59:33.565741Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2e404a11-8591-431f-802d-ed89bcb30782 · inbound
A Survey on Data Security in Large Language Models cites this paper.
A Survey on Data Security in Large Language Models Concealed Data Poisoning Attacks on NLP Models
Reference 66
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T05:05:02.876605Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0c8be5a4-0139-462a-95bb-a8e3025f9afb · inbound
RapidUn: Influence-Driven Parameter Reweighting for Efficient Large Language Model Unlearning cites this paper.
RapidUn: Influence-Driven Parameter Reweighting for Efficient Large Language Model Unlearning Concealed Data Poisoning Attacks on NLP Models
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T17:34:17.377307Z
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 8f499758-16ba-4934-ae5f-d694e449d34a · 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 Concealed Data Poisoning Attacks on NLP Models
Reference 31
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-04T19:50:11.209459Z
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 94e3bf5e-1d55-4c90-9ca9-e505211f1e21 · 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 Concealed Data Poisoning Attacks on NLP Models
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T10:16:38.248004Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e269dfcf-dbbd-42b0-b2e5-223ba479aa7c · inbound
Pretraining Data Can Be Poisoned through Computational Propaganda cites this paper.
Pretraining Data Can Be Poisoned through Computational Propaganda Concealed Data Poisoning Attacks on NLP Models
Reference 2021
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T23:44:46.193089Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.