Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:55:43.638457Z
Paper Citation Record · LEDGER
The Ripple Effect: On Unforeseen Complications of Backdoor Attacks
As of 19 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 0 inbound Pith citation observations for arXiv:2505.11586.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
66 of 66 outbound references displayed
- verified exact2
- verified fuzzy39
- unresolved24
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Unavailable: canonical work link unavailable.
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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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
Unavailable: canonical work link unavailable.
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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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
Unavailable: canonical work link unavailable.
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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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
Unavailable: canonical work link unavailable.
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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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
Unavailable: canonical work link unavailable.
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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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
Unavailable: canonical work link unavailable.
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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
No inbound Pith citation observations are available.