Paper Citation Record · LEDGER

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain

As of 23 August 2026, this Paper Citation Record lists 93 of 93 outbound references and 2 inbound Pith citation observations for arXiv:2509.03787.

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

pith.paper-citation-record.v1
2509.03787 v1

Coverage vector

measured 93 of 93 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:44:10.652420Z

measured 95 of 95 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T05:29:41.946357Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T05:31:23.579211Z

Reference resolution

93 of 93 outbound references displayed

verified exact2
verified fuzzy2
unresolved89
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9d1efffb-72a7-4f3a-a84e-ad9311ab36a0 · outbound

This paper cites Phi-4 Technical Report.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Phi-4 Technical Report

Reference 1

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Observation d37dfe9d-3a38-4539-9b28-c2901e114761 · outbound

This paper cites an unresolved cited work.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

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Observation b8102041-b5ed-486b-a1a9-b473682b79f0 · outbound

This paper cites Embedding-based classifiers can detect prompt injection attacks.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Embedding-based classifiers can detect prompt injection attacks

Reference 3

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Observation 1329cf1f-9917-486b-a118-5ac62721170f · outbound

This paper cites an unresolved cited work.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 4

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Observation c53b43f2-503b-4582-802a-406e7eb5cd2d · outbound

This paper cites EMPRA: Embedding Perturbation Rank Attack against Neural Ranking Models.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain EMPRA: Embedding Perturbation Rank Attack against Neural Ranking Models

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Observation 08cfc278-7da6-4c6b-9e7c-187095a36d41 · outbound

This paper cites Adversarial Attacks against Neural Ranking Models via In-Context Learning.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Adversarial Attacks against Neural Ranking Models via In-Context Learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.378820Z

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Observation 6fc69d1a-04dc-4d07-9c5b-2233f71cc917 · outbound

This paper cites Defending Against Unforeseen Failure Modes with Latent Adversarial Training.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Defending Against Unforeseen Failure Modes with Latent Adversarial Training

Reference 7

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Observation c3ac7339-7a0a-4473-a297-c00baf8f4199 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 8

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Observation bcc23ca0-9379-480b-a6e2-06d423e697ce · outbound

This paper cites an unresolved cited work.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 9

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Observation 940262c2-4ed1-49da-8b0f-3e9611bde491 · outbound

This paper cites an unresolved cited work.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 10

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Observation 3759cf91-e76f-435b-a7b1-a60fb611481f · outbound

This paper cites Defending Against Prompt Injection With a Few DefensiveTokens.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Defending Against Prompt Injection With a Few DefensiveTokens

Reference 11

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Observation 16634dc5-cecf-4163-a0d7-286a5723c790 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain SecAlign: Defending Against Prompt Injection with Preference Optimization

Reference 12

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Observation a353e47c-88e4-438b-a794-eca3ab8aaa75 · outbound

This paper cites an unresolved cited work.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

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Observation d0f4a70f-bfa6-4f3c-8aef-140e65fcb8a6 · outbound

This paper cites Towards Imperceptible Document Manipulations against Neural Ranking Models.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Towards Imperceptible Document Manipulations against Neural Ranking Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.405623Z

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Observation 580c21fc-3a13-4587-a63b-4e0d02514d3f · outbound

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

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models

Reference 15

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Observation 4c93c43e-f461-4543-bdc9-b525bc71f5e1 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 16

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Observation 9e56de89-bc26-4953-bb1d-b8788837cc3e · outbound

This paper cites Typos that Broke the RAG's Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Typos that Broke the RAG's Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.414941Z

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Observation 2115510b-3d10-462c-a1bc-319ed78ac1e6 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

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Observation 98a3d487-10ad-48cd-94a4-f344f522e655 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

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Observation dcd5842f-a502-4ce1-9166-c459cd7ffec8 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Overview of the TREC 2020 deep learning track

Reference 20

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Observation 9b5b9f45-1b6a-4521-b304-062cf82abe40 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Overview of the TREC 2022 deep learning track

Reference 21

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Overview of the TREC 2019 deep learning track

Reference 22

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

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Observation 4a909c0b-6654-4cbe-aebf-7d55ade6bc19 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain MasterKey: Automated Jailbreak Across Multiple Large Language Model Chatbots

Reference 24

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

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Observation 8dfd1971-30db-4e6e-a3eb-8a55af771ad1 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain SmartRAG: Jointly Learn RAG-Related Tasks From the Environment Feedback

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Retrieval-Augmented Generation for Large Language Models: A Survey

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

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Observation ea0dc7f7-484d-4214-a895-9832b3ce4261 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Atlas: Few-shot Learning with Retrieval Augmented Language Models

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Baseline Defenses for Adversarial Attacks Against Aligned Language Models

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Certifying LLM Safety against Adversarial Prompting

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Multi-step Jailbreaking Privacy Attacks on ChatGPT

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Enhancing Retrieval-Augmented Generation: A Study of Best Practices

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Lost in the Middle: How Language Models Use Long Contexts

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Do prompt positions really matter?

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Document Ranking with a Pretrained Sequence-to-Sequence Model

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Entity Cloze By Date: What LMs Know About Unseen Entities

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain From Prompt Injections to SQL Injection Attacks: How Protected is Your LLM-Integrated Web Application?

Reference 56

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Investigating the Robustness of Retrieval-Augmented Generation at the Query Level

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.548768Z

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Observation b32b16f5-2f37-4314-a8e4-a7645bf8912d · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Ignore Previous Prompt: Attack Techniques For Language Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.551784Z

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Observation 4364f9d1-2877-4a4c-9788-806fc557f7b2 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.554735Z

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:44:11.438589Z

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Observation 1969eef6-59cb-4a85-af68-201ff2cf3879 · outbound

This paper cites an unresolved cited work.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:44:11.428892Z

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Observation b0289010-f263-4507-ab76-66de5934b18f · outbound

This paper cites an unresolved cited work.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:44:11.417272Z

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Observation 81cb42fd-32db-4390-a9e8-8454e905e854 · outbound

This paper cites Prompt Perturbation Consistency Learning for Robust Language Models.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Prompt Perturbation Consistency Learning for Robust Language Models

Reference 63

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Observation 81892dd7-ba3f-47a4-901c-54779842b347 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:44:11.406919Z

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Observation 7a23eb1c-0502-414f-9294-9075e306505d · outbound

This paper cites an unresolved cited work.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:44:11.385379Z

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Observation 581ffc31-b7da-4a44-9e1a-939b2134d496 · outbound

This paper cites SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.580434Z

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Observation ef7f8013-f8f3-4afa-8906-71479ce23a53 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:44:11.396487Z

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Observation c64504aa-1161-4f7a-bd6f-c116b8dbeabc · outbound

This paper cites do anything now.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain do anything now

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:44:11.374391Z

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Observation fb91bcd2-468d-4001-ba8d-da88f264442a · outbound

This paper cites an unresolved cited work.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:44:11.362247Z

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Observation 6077579c-8a79-4f05-bd11-b9290276d170 · outbound

This paper cites Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.583699Z

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.595505Z

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Observation 6be4a335-3a1b-4f6d-a855-12ed15ebb2b6 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:44:11.344046Z

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Observation c46614cc-2f71-42d5-94f1-28156b4a7284 · outbound

This paper cites PromptArmor: Simple yet Effective Prompt Injection Defenses.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain PromptArmor: Simple yet Effective Prompt Injection Defenses

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.592328Z

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:44:11.333686Z

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Observation cb59fae0-327d-4f7b-8a18-53892e51b031 · outbound

This paper cites an unresolved cited work.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:44:11.323499Z

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This paper cites DeRAG: Black-box Adversarial Attacks on Multiple Retrieval-Augmented Generation Applications via Prompt Injection.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain DeRAG: Black-box Adversarial Attacks on Multiple Retrieval-Augmented Generation Applications via Prompt Injection

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.601057Z

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 77

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:44:11.301691Z

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Observation 1899c61f-1715-441c-8035-66c8c6aad87c · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 78

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:44:11.291131Z

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Observation cd1baf12-17c8-4159-9966-cc94cf58e100 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:44:11.313177Z

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Observation 0698889b-9b55-4363-ba93-fe97c98b0fb0 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.621289Z

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.624358Z

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 82

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:44:11.281380Z

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Observation 5c342607-bb58-4b62-86ba-353bf506b0c8 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain How Language Model Hallucinations Can Snowball

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.630613Z

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:44:11.270624Z

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Observation 6a78fe27-3580-4bdf-95e6-6f4d72bcf790 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.627459Z

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain HijackRAG: Hijacking Attacks against Retrieval-Augmented Large Language Models

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.643154Z

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain DocPrompting: Generating Code by Retrieving the Docs

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.646252Z

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 88

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:44:11.260224Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fda83275-8882-44aa-b99b-3e65f871a595 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain JBShield: Defending Large Language Models from Jailbreak Attacks through Activated Concept Analysis and Manipulation

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.639804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e9b4736-8bc7-48de-a651-d088b13a28c2 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.649233Z

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain Unresolved cited work

Reference 93

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:44:11.249126Z

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Observation bb9e0039-7807-47cb-a936-43b03388ae7f · outbound

This paper cites In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval.

Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:10.436793Z

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Unavailable: canonical work link unavailable.

Observation 7d419938-5213-4c46-9de1-f0e065058555 · outbound

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Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain ACM Transactions on Intelligent Systems and Technology 16, 5 (2025), 1–72

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:44:11.468193Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation 9f1ad7ee-8fca-429a-89e2-8c70be8b58f0 · inbound

Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation cites this paper.

Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:46:11.573012Z

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Observation a30b22e0-4cfa-4eb4-98ff-74d712f2e562 · inbound

Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation cites this paper.

Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:31:23.581877Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.