Paper Citation Record · LEDGER

PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2410.08811.

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

pith.paper-citation-record.v1
2410.08811 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:24:12.420623Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T14:59:54.708937Z

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 9b274567-d45c-4fa0-863a-e2c07d94652b · inbound

Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation cites this paper.

Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-09T00:39:02.309959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84c72cda-a02d-43aa-b02f-dd2342677813 · 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 PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning

Reference 149

Resolution: unresolved; no resolver link, observed 2026-08-16T11:24:12.420623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 195a3ecc-af48-47ed-b019-3c02b3b79a90 · inbound

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead cites this paper.

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:29.676305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fca70983-ea4d-4e35-9b7a-b07eeb09401d · inbound

LLM Hypnosis: Exploiting User Feedback for Unauthorized Knowledge Injection to All Users cites this paper.

LLM Hypnosis: Exploiting User Feedback for Unauthorized Knowledge Injection to All Users PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-19T06:02:07.810301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d13cfdac-bde0-4bf9-b4ea-e227a2d5ba2b · inbound

LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems cites this paper.

LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-04T17:46:17.800744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1ebc7a2-c75f-4e95-bc1c-acc5bdca919c · inbound

SafeTune: Mitigating Data Poisoning in LLM Fine-Tuning for RTL Code Generation cites this paper.

SafeTune: Mitigating Data Poisoning in LLM Fine-Tuning for RTL Code Generation PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T09:36:26.575265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4617f6c5-db4b-46cd-98eb-fca9caaf0729 · inbound

Efficient Preference Poisoning Attack on Offline RLHF cites this paper.

Efficient Preference Poisoning Attack on Offline RLHF PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning

Reference 139

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T05:50:27.117761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7884badc-28e9-4edd-b089-56613bc33fef · inbound

BackFlush: Knowledge-Free Backdoor Detection and Elimination with Watermark Preservation in Large Language Models cites this paper.

BackFlush: Knowledge-Free Backdoor Detection and Elimination with Watermark Preservation in Large Language Models PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T21:02:58.867033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b4f2d092-324a-4e17-ad18-99c4bc23e4d5 · inbound

Be Kind, Rewrite: Benign Projections via Rewriting Defend Against LLM Data Poisoning Attacks cites this paper.

Be Kind, Rewrite: Benign Projections via Rewriting Defend Against LLM Data Poisoning Attacks PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-20T08:58:10.448234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 408b2067-284f-42a6-ab78-1527d5bf42e0 · inbound

AI Integrity: Defending Against Backdoors and Secret Loyalties cites this paper.

AI Integrity: Defending Against Backdoors and Secret Loyalties PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-04T14:59:54.712176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.