Paper Citation Record · LEDGER

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation

As of 20 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 4 inbound Pith citation observations for arXiv:2505.12574.

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

pith.paper-citation-record.v1
2505.12574 v5

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:38:21.015474Z

measured 44 of 44 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:20:40.017938Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T11:31:00.505172Z

Reference resolution

40 of 40 outbound references displayed

verified exact1
verified fuzzy18
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c3393d47-63c5-4185-b47b-171cc891314d · outbound

This paper cites Survey of hallucination in natural language generation.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Survey of hallucination in natural language generation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:21.923237Z

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 90618f71-a669-44df-81e8-7e324e33d04c · outbound

This paper cites Dense passage retrieval for open-domain question answering.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Dense passage retrieval for open-domain question answering

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:21.864214Z

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 3092b301-71e5-4816-8e5d-d5e79e2381f6 · outbound

This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks.Advances in Neural Information Processing Systems, 33:9459–9474, 2020.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Retrieval-augmented generation for knowledge-intensive nlp tasks.Advances in Neural Information Processing Systems, 33:9459–9474, 2020

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:20.810962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70b35c45-f951-4a1e-a407-11eac577603d · outbound

This paper cites Improving language models by retrieving from trillions of tokens.International Conference on Machine Learning, 2022.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Improving language models by retrieving from trillions of tokens.International Conference on Machine Learning, 2022

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:21.801320Z

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 0e86f4bd-55fe-465b-9f20-3198be86bcbb · outbound

This paper cites Thoppilan, D.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Thoppilan, D

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:21.787153Z

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 b8b008b2-c65f-4848-871d-9e7cb7878d3c · outbound

This paper cites Generative ai in search: Let google do the searching for you.Google blogs, 2024.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Generative ai in search: Let google do the searching for you.Google blogs, 2024

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:21.700417Z

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 0749b720-77e1-4095-b96d-91f91058989c · outbound

This paper cites Introducing deep research.OpenAI blogs, 2025.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Introducing deep research.OpenAI blogs, 2025

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:21.656100Z

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 8d59d408-9f0d-432b-8c6e-d209d7a54ecc · outbound

This paper cites Grok 3 beta — the age of reasoning agents.xAI blogs, 2025.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Grok 3 beta — the age of reasoning agents.xAI blogs, 2025

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:21.633979Z

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 bd87f562-e224-41f6-b512-6abb0d1fb4bf · outbound

This paper cites Al Ghadban, H.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Al Ghadban, H

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:21.619628Z

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 95c44021-f437-4573-a4e9-ff72b3884f2c · outbound

This paper cites Loukas, I.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Loukas, I

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:21.605954Z

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 a747083b-9f27-4c3b-8d12-0b8e801afc51 · outbound

This paper cites an unresolved cited work.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:38:21.591058Z

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 73243c16-5f75-4f16-a18d-32ef68407395 · outbound

This paper cites Poisonedrag: Knowledge corruption attacks to retrieval-augmented generation of large language models.USENIX Security, 2024.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Poisonedrag: Knowledge corruption attacks to retrieval-augmented generation of large language models.USENIX Security, 2024

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:21.564752Z

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 546d763f-71e8-4c7a-bc68-39fa1abf8dcc · outbound

This paper cites TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:20.852978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd5e23e2-a5fb-49ba-a253-4a6892cf0162 · outbound

This paper cites Agentpoison: Red-teaming llm agents via poisoning memory or knowledge bases.The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Agentpoison: Red-teaming llm agents via poisoning memory or knowledge bases.The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:21.538678Z

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 14dca4c1-16fc-4290-94f7-3d0f81cd80ef · outbound

This paper cites Gasliteing the retrieval: Exploring vulnerabilities in dense embedding-based search.arXiv preprint, arXiv:2412.20953, 2024.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Gasliteing the retrieval: Exploring vulnerabilities in dense embedding-based search.arXiv preprint, arXiv:2412.20953, 2024

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:20.861367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b160339-11d5-47ab-a159-f0a3dd40399e · outbound

This paper cites Adversarial Decoding: Generating Readable Documents for Adversarial Objectives.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Adversarial Decoding: Generating Readable Documents for Adversarial Objectives

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:20.865182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1075a889-03b6-40ce-919b-a1b28272ca85 · outbound

This paper cites "Glue pizza and eat rocks" -- Exploiting Vulnerabilities in Retrieval-Augmented Generative Models.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation "Glue pizza and eat rocks" -- Exploiting Vulnerabilities in Retrieval-Augmented Generative Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:20.870550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c37f133a-a233-403c-9a9d-be4a5a26a91f · outbound

This paper cites an unresolved cited work.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:38:21.525772Z

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 dd548734-1a5e-434f-9be4-8407e2161f1a · outbound

This paper cites Poisoning Retrieval Corpora by Injecting Adversarial Passages.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Poisoning Retrieval Corpora by Injecting Adversarial Passages

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:20.879477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60abf020-0fde-48a4-8970-bc245348f71b · outbound

This paper cites Human- imperceptible retrieval poisoning attacks in llm-powered applications.Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering, 2024.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Human- imperceptible retrieval poisoning attacks in llm-powered applications.Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering, 2024

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:21.512884Z

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 83413f08-369e-4d97-9342-e05a8e9c83dd · outbound

This paper cites an unresolved cited work.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Unresolved cited work

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:20.889803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e0a2965-78f8-4920-9ff9-6a555b85fe00 · outbound

This paper cites Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:21.488757Z

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 803ff6c2-7b06-4ea7-a0a6-f452885bd497 · outbound

This paper cites Nguyen, M.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Nguyen, M

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:21.475237Z

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 51aaf60c-d747-4399-b073-512dafd6b0ac · outbound

This paper cites Benchmarking large language mod- els in retrieval-augmented generation.In Proceedings of the AAAI Conference on Artificial Intelligence, 38:17754–17762, 2024.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Benchmarking large language mod- els in retrieval-augmented generation.In Proceedings of the AAAI Conference on Artificial Intelligence, 38:17754–17762, 2024

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:21.462307Z

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 48ce0db4-dae5-4277-9b6f-42dc665f049e · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:20.907826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 069c59a9-f1d3-48a7-8894-b1a5ac173475 · outbound

This paper cites ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:20.911987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 648bb214-e2e5-4443-aaf6-bd3e2bab2826 · outbound

This paper cites Prompt Perturbation in Retrieval-Augmented Generation based Large Language Models.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Prompt Perturbation in Retrieval-Augmented Generation based Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:20.916935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9035abfe-95a7-48ee-8521-bf8513d6bd1c · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:20.921165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f7e637f-a08e-4849-a1e7-00a76162169a · outbound

This paper cites The Llama 3 Herd of Models.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation The Llama 3 Herd of Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:20.924921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50598670-1598-4e27-981a-03b0e8d1ba98 · outbound

This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:21.448705Z

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 f86970e0-cad1-4b68-9b5c-c155b7e7ab28 · outbound

This paper cites Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:20.938905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29370180-694c-42ce-b884-f9d590376fe6 · outbound

This paper cites Language models are few-shot learners.Advances in Neural Information Processing Systems, 2020.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Language models are few-shot learners.Advances in Neural Information Processing Systems, 2020

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:20.944392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e65adca-60a8-4398-a22a-6cf467136e55 · outbound

This paper cites GPT-4 Technical Report.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation GPT-4 Technical Report

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:20.950755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19d77032-c828-4488-95df-d911ff80197a · outbound

This paper cites Izacard, M.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Izacard, M

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:21.426658Z

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 faadb9e6-2b9c-4fb8-91b4-452aacdaddd9 · outbound

This paper cites an unresolved cited work.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:38:21.413752Z

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 851bdc2f-3d38-4d1e-a964-9c843a315184 · outbound

This paper cites Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:20.975806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d64a7612-6c17-4229-8c8b-7236083cae25 · outbound

This paper cites A Survey on LLM-as-a-Judge.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation A Survey on LLM-as-a-Judge

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:20.985391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 457c593f-d43d-465d-b2fc-341b6ff6e14b · outbound

This paper cites InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:20.997538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca8e3303-ae69-4dbf-af2d-1cda937f906d · outbound

This paper cites incorrect_answers.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation incorrect_answers

Reference 39

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:38:21.122598Z

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 fb27bc4c-0fe1-4acb-af5f-464f30cd7f8f · outbound

This paper cites strong attack.

Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation strong attack

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:21.401088Z

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.

Pith citing papers

Observation 5ba96162-4742-4409-aa94-8f4e54f11f65 · inbound

Needle-in-RAG: Prompt-Conditioned Character-Level Traceback of Poisoned Spans in Retrieved Evidence cites this paper.

Needle-in-RAG: Prompt-Conditioned Character-Level Traceback of Poisoned Spans in Retrieved Evidence Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-03T02:05:41.017232Z

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 ae3cf485-87db-4cab-9ebd-c1d71dfc7305 · inbound

A Failure-Mode Benchmark for Polymorphic Sybil Poisoning in RAG cites this paper.

A Failure-Mode Benchmark for Polymorphic Sybil Poisoning in RAG Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-12T00:17:10.365724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f57e93b-fe0f-4810-972a-332c68e591f3 · inbound

Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability cites this paper.

Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-14T12:01:22.824663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f9c5233-f1cd-4007-ac5c-5aa324bfcd40 · inbound

EviSD: Evidence-Conditioned Self-Distillation for Search-Augmented Agents cites this paper.

EviSD: Evidence-Conditioned Self-Distillation for Search-Augmented Agents Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T00:20:40.017938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.