Paper Citation Record · LEDGER

Learning to Poison Large Language Models for Downstream Manipulation

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2402.13459.

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

pith.paper-citation-record.v1
2402.13459 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:24:12.741477Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T04:42:33.856578Z

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 e8ac5296-cbea-4474-9949-b41fa795a601 · inbound

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models cites this paper.

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Learning to Poison Large Language Models for Downstream Manipulation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-12T13:56:36.652527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bebc58f5-0fab-40a1-a6c5-74861db3b877 · inbound

NLSR: Neuron-Level Safety Realignment of Large Language Models Against Harmful Fine-Tuning cites this paper.

NLSR: Neuron-Level Safety Realignment of Large Language Models Against Harmful Fine-Tuning Learning to Poison Large Language Models for Downstream Manipulation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T14:06:48.176729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08f42068-a52b-4aff-9fcf-1abaf8148950 · inbound

Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety cites this paper.

Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety Learning to Poison Large Language Models for Downstream Manipulation

Reference 151

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:42:33.859648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 275e3fdb-5a3a-48e3-8ff7-f0998800db91 · 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 Learning to Poison Large Language Models for Downstream Manipulation

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-09T00:50:00.286273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39535b40-b09a-4dd9-895b-0d8f4ff9a177 · inbound

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment cites this paper.

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment Learning to Poison Large Language Models for Downstream Manipulation

Reference 244

Resolution: unresolved; no resolver link, observed 2026-08-16T11:24:12.741477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4e7d98d-3312-4184-84f6-3b4e887c5fd3 · inbound

Automatic Calibration for Membership Inference Attack on Large Language Models cites this paper.

Automatic Calibration for Membership Inference Attack on Large Language Models Learning to Poison Large Language Models for Downstream Manipulation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T23:59:23.318046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e4b953e-9c90-4912-b65b-5dc0915554e8 · inbound

A Systematic Review of Poisoning Attacks Against Large Language Models cites this paper.

A Systematic Review of Poisoning Attacks Against Large Language Models Learning to Poison Large Language Models for Downstream Manipulation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T05:59:32.918399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48ce344e-7e62-45bd-8d86-18374023e328 · inbound

Prompt Injection 2.0: Hybrid AI Threats cites this paper.

Prompt Injection 2.0: Hybrid AI Threats Learning to Poison Large Language Models for Downstream Manipulation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:04.598552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0818a466-1fd4-440d-9da6-ff7c57c19383 · inbound

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation cites this paper.

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation Learning to Poison Large Language Models for Downstream Manipulation

Reference 178

Resolution: unresolved; no resolver link, observed 2026-08-05T20:31:50.052062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac8b7e90-5ad0-47ad-9fc9-ef0b23088d0b · inbound

Breaking to Build: A Threat Model of Prompt-Based Attacks for Securing LLMs cites this paper.

Breaking to Build: A Threat Model of Prompt-Based Attacks for Securing LLMs Learning to Poison Large Language Models for Downstream Manipulation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T05:59:28.677245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7df2670b-84c2-462a-ad8a-6f489e28d981 · inbound

Transferable Direct Prompt Injection via Activation-Guided MCMC Sampling cites this paper.

Transferable Direct Prompt Injection via Activation-Guided MCMC Sampling Learning to Poison Large Language Models for Downstream Manipulation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T22:01:57.897642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.