The Ripple Effect: On Unforeseen Complications of Backdoor Attacks | Paper Citation Record

Observation b995f518-fb2b-4eec-a8fb-1b69087fea0e · outbound

This paper cites an unresolved cited work.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:55:44.993112Z

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Observation 230797c0-0240-4a48-9f63-d05d0aa42906 · outbound

This paper cites an unresolved cited work.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:55:44.977945Z

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Observation 56bd9cc5-383c-45f2-adf1-3b05b956e1e5 · outbound

This paper cites an unresolved cited work.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:55:44.963269Z

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Observation aba76b24-1ce7-4a50-b259-86073c810440 · outbound

This paper cites Detecting opin- ion spams and fake news using text classification.Security and Privacy, 2018.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Detecting opin- ion spams and fake news using text classification.Security and Privacy, 2018

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.948935Z

Source-reported events for the cited work

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Observation 6eb27ab3-bdd7-4c30-a4cd-cc55897f80ff · outbound

This paper cites Data poisoning attacks against autoregressive models.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Data poisoning attacks against autoregressive models

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.934906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 95dd07ba-7966-45cf-a99e-7d2f8cbca0e7 · outbound

This paper cites Contributions to the study of sms spam filtering: new collection and results.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Contributions to the study of sms spam filtering: new collection and results

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.921572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dbf651c7-f4c7-42e0-89c5-c621bfe989bd · outbound

This paper cites Spinning Lan- guage Models: Risks of Propaganda-As-A-Service and Coun- termeasures.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Spinning Lan- guage Models: Risks of Propaganda-As-A-Service and Coun- termeasures

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.907988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4fe414c6-156f-4629-8f56-00e531ebcf1f · outbound

This paper cites an unresolved cited work.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:55:44.894501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d55280a2-d423-406d-9d3a-6ec752f66373 · outbound

This paper cites Poisoning and Back- dooring Contrastive Learning.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Poisoning and Back- dooring Contrastive Learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.881464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 155abd5d-4fd0-4d3d-b739-c24f76e3c1cf · outbound

This paper cites BadPre: Task- agnostic Backdoor Attacks to Pre-trained NLP Foundation Models.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks BadPre: Task- agnostic Backdoor Attacks to Pre-trained NLP Foundation Models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.867568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 04a174d3-d8a3-4576-bee5-a8e3330a0649 · outbound

This paper cites BadNL: Back- door Attacks Against NLP Models with Semantic-preserving Improvements.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks BadNL: Back- door Attacks Against NLP Models with Semantic-preserving Improvements

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.853812Z

Source-reported events for the cited work

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Observation 11444656-d4b8-4013-b3cb-281280313ab1 · outbound

This paper cites Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:43.382102Z

Source-reported events for the cited work

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Observation 1a8eb0bc-b816-455d-929d-50418b128bd4 · outbound

This paper cites Amplifying Membership Exposure via Data Poison- ing.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Amplifying Membership Exposure via Data Poison- ing

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.838875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation da24e9ba-5f86-480d-a335-9f122597bfbb · outbound

This paper cites Lotus: Evasive and resilient backdoor attacks through sub-partitioning.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Lotus: Evasive and resilient backdoor attacks through sub-partitioning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.815434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 30cdd309-0a9b-4b37-8164-0c8330795d5f · outbound

This paper cites Automated hate speech detection and the prob- lem of offensive language.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Automated hate speech detection and the prob- lem of offensive language

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.801210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 87100f4a-85e9-4ca0-bbdf-3f286768a0dc · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Trans- formers for Language Understanding.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks BERT: Pre-training of Deep Bidirectional Trans- formers for Language Understanding

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.786905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e6b8da8a-3a84-4aae-abfe-4dacff917129 · outbound

This paper cites Multi-dimensional gender bias classification.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Multi-dimensional gender bias classification

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.771448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d2912cae-18c6-4fec-8901-66606a387fcf · outbound

This paper cites Adversarial Examples Make Strong Poisons.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Adversarial Examples Make Strong Poisons

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.755238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4eadba74-0404-4d69-80f1-d8cc042b8338 · outbound

This paper cites E-commerce text dataset (version - 2).https://do i.org/10.5281/zenodo.3355823, 2019.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks E-commerce text dataset (version - 2).https://do i.org/10.5281/zenodo.3355823, 2019

Reference 19

Resolution: verified exact; doi, observed 2026-08-15T20:55:43.682841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1df4d919-f211-4881-8e9a-2d90704d710e · outbound

This paper cites Practical solutions to the problem of diagonal dominance in kernel document clustering.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Practical solutions to the problem of diagonal dominance in kernel document clustering

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.741338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 88454457-3205-48f5-a320-a17b1aaa2d03 · outbound

This paper cites BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:43.427891Z

Source-reported events for the cited work

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Observation d86bfa95-a2fc-43f5-b8d0-66e2ae01858a · outbound

This paper cites Threats to Pre-trained Language Models: Survey and Taxonomy.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Threats to Pre-trained Language Models: Survey and Taxonomy

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:43.432911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 402f40e1-f5e8-4f72-baeb-8faa7759757f · outbound

This paper cites Composite backdoor attacks against large lan- guage models.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Composite backdoor attacks against large lan- guage models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.727551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 946dd0d9-7ef2-47c9-af58-b04973b7e9fe · outbound

This paper cites BadEn- coder: Backdoor Attacks to Pre-trained Encoders in Self- Supervised Learning.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks BadEn- coder: Backdoor Attacks to Pre-trained Encoders in Self- Supervised Learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.713880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5c5f0ab4-71de-48ed-bf83-b8788b1d06d5 · outbound

This paper cites AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:43.447139Z

Source-reported events for the cited work

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Observation 484b4bda-b17b-4f65-837f-49c83d533d30 · outbound

This paper cites Aliasing backdoor attacks on pre-trained models.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Aliasing backdoor attacks on pre-trained models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.700154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b2e9153f-0219-4ade-968d-a123c0f5a909 · outbound

This paper cites BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:43.456225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5484b6e5-371f-4e81-8865-8738558a71db · outbound

This paper cites Hidden backdoors in human-centric language models.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Hidden backdoors in human-centric language models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.676730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b56524ae-615c-490c-9401-f2da2828b6c6 · outbound

This paper cites Backdoor Learning: A Survey.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Backdoor Learning: A Survey

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:43.466498Z

Source-reported events for the cited work

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Observation 1084ae62-9f4d-40b9-a3fc-d022ff588b22 · outbound

This paper cites Invisible Backdoor Attack with Sample- Specific Triggers.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Invisible Backdoor Attack with Sample- Specific Triggers

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.662780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3bf9014d-2bc1-4c59-9fb0-9ff30b618ec4 · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.ACM Computing Surveys, 2023.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.ACM Computing Surveys, 2023

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.648945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f0a7c784-6686-49e0-a83b-974f37b9f3b2 · outbound

This paper cites Backdoor Attacks Against Dataset Distillation.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Backdoor Attacks Against Dataset Distillation

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:43.481158Z

Source-reported events for the cited work

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Observation 8c91e79e-ac9f-4126-913c-5775a4e12dad · outbound

This paper cites Maas, Raymond E.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Maas, Raymond E

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.632879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation da05c03c-73a0-468b-b0f7-5b4eb3584fcf · outbound

This paper cites Recent advances in natural language processing via large pre-trained language models: A survey.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Recent advances in natural language processing via large pre-trained language models: A survey

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.618992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 61d94991-bdd7-48ea-8124-15510d817afe · outbound

This paper cites Multi-Source Social Feedback of Online News Feeds.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Multi-Source Social Feedback of Online News Feeds

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:43.495128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4830965-be93-4db0-a29a-84111d6cf694 · outbound

This paper cites Backdooring Bias ($B^2$) into Stable Diffusion Models.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Backdooring Bias ($B^2$) into Stable Diffusion Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:43.500348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e400c819-44f4-4dae-9e26-f623204fe936 · outbound

This paper cites Input-Aware Dynamic Backdoor Attack.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Input-Aware Dynamic Backdoor Attack

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.606189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eef4311e-34d2-4db9-9614-c8d31cc85081 · outbound

This paper cites Pre-trained Models for Natural Language Processing: A Survey.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Pre-trained Models for Natural Language Processing: A Survey

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:43.509593Z

Source-reported events for the cited work

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Observation 60b39d87-62b6-41f1-a763-cba2a0c7ef5f · outbound

This paper cites Language Models are Unsuper- vised Multitask Learners.OpenAI blog, 2019.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Language Models are Unsuper- vised Multitask Learners.OpenAI blog, 2019

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.592892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation aad42926-50ec-426f-8f16-8d90fbb8c544 · outbound

This paper cites an unresolved cited work.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:55:44.578070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation af4ec447-23ad-4fc6-bfeb-e3a92ec73aeb · outbound

This paper cites Backdoor Attacks on Self- Supervised Learning.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Backdoor Attacks on Self- Supervised Learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.564079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9d3e1f04-a721-4e22-a93f-14897ba0d5ee · outbound

This paper cites Don't Trigger Me! A Triggerless Backdoor Attack Against Deep Neural Networks.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Don't Trigger Me! A Triggerless Backdoor Attack Against Deep Neural Networks

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:43.528169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86f3bf6d-232b-45de-9bcc-80ce344068c8 · outbound

This paper cites BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:43.533415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d8b2d81-89b6-42e6-9319-b564990a8ee1 · outbound

This paper cites Dynamic Backdoor Attacks Against Machine Learning Models.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Dynamic Backdoor Attacks Against Machine Learning Models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.550291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 906ca001-76f0-4729-864d-9342bc3e8e35 · outbound

This paper cites Poi- son Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Poi- son Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.537369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e4da71f9-aba9-4840-9ac0-686454ab6157 · outbound

This paper cites Backdoor Pre-trained Models Can Transfer to All.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Backdoor Pre-trained Models Can Transfer to All

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.523022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 090cba7f-3e6d-411f-ba59-09de94cabea0 · outbound

This paper cites Backdoor Attacks in the Supply Chain of Masked Image Modeling.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Backdoor Attacks in the Supply Chain of Masked Image Modeling

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:43.552982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97b494b4-081f-4d44-a14f-1c8d8eaa2476 · outbound

This paper cites Manning, Andrew Y.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Manning, Andrew Y

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.508889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 85e629af-1dfa-4c65-be79-288fc92258d8 · outbound

This paper cites Machine Learning Models that Remember Too Much.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Machine Learning Models that Remember Too Much

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.495134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2e83b5d8-6c48-492c-9ed2-5bef015b61e3 · outbound

This paper cites Environmental Claim Detection.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Environmental Claim Detection

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:43.566662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 388d1a32-c54e-43d1-821d-2c60e4c0b633 · outbound

This paper cites Disaster tweets.https: //www.kaggle.com/dsv/1640141, 2020.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Disaster tweets.https: //www.kaggle.com/dsv/1640141, 2020

Reference 51

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:55:43.914489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5174d5e6-fa44-425f-997e-cdc71210a362 · outbound

This paper cites Truth serum: Poisoning machine learning models to reveal their secrets.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Truth serum: Poisoning machine learning models to reveal their secrets

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.480708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 615fbfb2-aeca-4a58-b33f-5a313ad448b5 · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.466609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7aa9541e-4df0-457b-8c92-7053bf92e502 · outbound

This paper cites Model Supply Chain Poisoning: Backdooring Pre-trained Models via Embedding Indistinguishability.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Model Supply Chain Poisoning: Backdooring Pre-trained Models via Embedding Indistinguishability

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:43.585008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1bc142d-d17d-48a6-a4c3-d20793e2c83a · outbound

This paper cites Neural Network Acceptability Judgments.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Neural Network Acceptability Judgments

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:43.589516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16d0d0d2-1791-4ea7-b05d-eb28aad63a7a · outbound

This paper cites Backdooring instruction-tuned large language mod- els with virtual prompt injection.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Backdooring instruction-tuned large language mod- els with virtual prompt injection

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.451551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5711afc8-adb9-4678-9b66-bab603c6177b · outbound

This paper cites Rethinking stealthiness of backdoor attack against nlp mod- els.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Rethinking stealthiness of backdoor attack against nlp mod- els

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.351247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 320ede39-3d8d-4945-b690-1f07c2a6836d · outbound

This paper cites an unresolved cited work.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:55:44.335694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2d7d449d-c81c-45a7-9abb-da175997c9df · outbound

This paper cites Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.314307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6a7e10a6-d9ab-4c45-b236-ad4e2de86aa6 · outbound

This paper cites TinyLlama: An Open-Source Small Language Model.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks TinyLlama: An Open-Source Small Language Model

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:43.611824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 507920f6-d542-4ffa-b22d-b65f7f31e4ae · outbound

This paper cites Instruction backdoor attacks against customized{LLMs}.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Instruction backdoor attacks against customized{LLMs}

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.272750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 74060e8e-fc19-4409-a5e1-8dc03eca084e · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks OPT: Open Pre-trained Transformer Language Models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T20:55:43.620387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 017cde68-6aee-4cd4-b80f-0dcc63ffc702 · outbound

This paper cites Character- level Convolutional Networks for Text Classification.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Character- level Convolutional Networks for Text Classification

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.246981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7938c0cd-116e-4f86-b11e-fea217d6f0f0 · outbound

This paper cites An overview of multi-task learn- ing.National Science Review, 2018.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks An overview of multi-task learn- ing.National Science Review, 2018

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.232095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 56b59100-8f0c-4580-9956-d16b38ed06d6 · outbound

This paper cites Backdoor Attacks to Graph Neural Networks.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Backdoor Attacks to Graph Neural Networks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:55:44.170539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f18d7772-cc3b-4b0d-87e4-fd96981dddfa · outbound

This paper cites Removing backdoors in pre-trained models by regu- larized continual pre-training.Transactions of the Association for Computational Linguistics, 11:1608–1623, 2023.

The Ripple Effect: On Unforeseen Complications of Backdoor Attacks Removing backdoors in pre-trained models by regu- larized continual pre-training.Transactions of the Association for Computational Linguistics, 11:1608–1623, 2023

Reference 66

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T20:55:44.125706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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