Paper Citation Record · LEDGER

Deep Learning Model Security: Threats and Defenses

As of 13 August 2026, this Paper Citation Record lists 84 of 84 outbound references and 2 inbound Pith citation observations for arXiv:2412.08969.

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

pith.paper-citation-record.v1
2412.08969 v2

Coverage vector

measured 84 of 84 reference resolution

Typed states for the displayed outbound observations.

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measured 86 of 86 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T17:36:18.728469Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T09:54:34.401614Z

Reference resolution

84 of 84 outbound references displayed

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External citation measurements

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Outbound references

Observation 273ab71b-86fb-4aec-bef0-30766f9e2155 · outbound

This paper cites Deep learning.nature, 521(7553):436–444, 2015.

Deep Learning Model Security: Threats and Defenses Deep learning.nature, 521(7553):436–444, 2015

Reference 1

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Observation c0905f6e-d753-4a66-ad17-8d64aae5f004 · outbound

This paper cites MIT Press, 2016.

Deep Learning Model Security: Threats and Defenses MIT Press, 2016

Reference 2

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Observation 7a522030-018c-4a7c-8904-4c7af03e3dd3 · outbound

This paper cites Deep learning , volume 1.

Deep Learning Model Security: Threats and Defenses Deep learning , volume 1

Reference 3

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This paper cites Atoms of recognition in human and computer vision.

Deep Learning Model Security: Threats and Defenses Atoms of recognition in human and computer vision

Reference 4

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This paper cites Understanding natural language.

Deep Learning Model Security: Threats and Defenses Understanding natural language

Reference 5

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This paper cites Playing games.

Deep Learning Model Security: Threats and Defenses Playing games

Reference 6

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This paper cites An introduction to Python.

Deep Learning Model Security: Threats and Defenses An introduction to Python

Reference 7

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Observation 503b16a4-5358-48db-b99b-d5dd1cf132e3 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Deep Learning Model Security: Threats and Defenses Pytorch: An imperative style, high-performance deep learning library

Reference 8

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Observation 7dece865-78eb-4b45-bcc3-06b67b50d648 · outbound

This paper cites Deep Learning using Rectified Linear Units (ReLU).

Deep Learning Model Security: Threats and Defenses Deep Learning using Rectified Linear Units (ReLU)

Reference 9

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This paper cites Stochastic gradient descent tricks.

Deep Learning Model Security: Threats and Defenses Stochastic gradient descent tricks

Reference 10

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This paper cites Deep learning and machine learning – object detection and semantic segmentation: From theory to applications.

Deep Learning Model Security: Threats and Defenses Deep learning and machine learning – object detection and semantic segmentation: From theory to applications

Reference 11

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This paper cites Deep learning and machine learning – python data structures and math- ematics fundamental: From theory to practice.

Deep Learning Model Security: Threats and Defenses Deep learning and machine learning – python data structures and math- ematics fundamental: From theory to practice

Reference 12

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Observation 5101f9b6-03f0-457f-9987-5ac566aab3c4 · outbound

This paper cites Review on methods to fix number of hidden neurons in neural networks.

Deep Learning Model Security: Threats and Defenses Review on methods to fix number of hidden neurons in neural networks

Reference 13

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Observation da368dad-d64b-4711-b329-7b3f8b115bdc · outbound

This paper cites The influence of the sigmoid function parameters on the speed of backpropagation learning.

Deep Learning Model Security: Threats and Defenses The influence of the sigmoid function parameters on the speed of backpropagation learning

Reference 14

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Observation b8e927df-5f0e-429e-8b06-058fe993ae78 · outbound

This paper cites Learning both weights and connections for efficient neural network.

Deep Learning Model Security: Threats and Defenses Learning both weights and connections for efficient neural network

Reference 15

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This paper cites Deep learning, machine learning – digital signal and image processing: From theory to application.

Deep Learning Model Security: Threats and Defenses Deep learning, machine learning – digital signal and image processing: From theory to application

Reference 16

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Observation 40729590-7943-4ca8-bb6c-e28df75700b3 · outbound

This paper cites Adversarial Attacks on Neural Network Policies.

Deep Learning Model Security: Threats and Defenses Adversarial Attacks on Neural Network Policies

Reference 17

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Observation 34795f1a-f8ae-4346-9e3a-597130d2d469 · outbound

This paper cites Certified defenses for data poisoning attacks.

Deep Learning Model Security: Threats and Defenses Certified defenses for data poisoning attacks

Reference 18

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Observation e8bf3862-79dc-42a7-89e6-0718f36bceec · outbound

This paper cites Ensemble machine learning models for the detection of energy theft.

Deep Learning Model Security: Threats and Defenses Ensemble machine learning models for the detection of energy theft

Reference 19

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Observation d07015d5-3ac9-457d-9db8-40e5cc37199c · outbound

This paper cites Fast Gradient Non-sign Methods.

Deep Learning Model Security: Threats and Defenses Fast Gradient Non-sign Methods

Reference 20

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Observation 5f7920c6-25e9-42fe-81ca-0323617b2557 · outbound

This paper cites Model inversion attacks that exploit confidence information and basic countermeasures.

Deep Learning Model Security: Threats and Defenses Model inversion attacks that exploit confidence information and basic countermeasures

Reference 21

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This paper cites A survey on data poisoning attacks and defenses.

Deep Learning Model Security: Threats and Defenses A survey on data poisoning attacks and defenses

Reference 22

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Deep Learning Model Security: Threats and Defenses Unresolved cited work

Reference 23

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Observation 56b5e1b4-cdf9-4e57-b58f-866865ff14ba · outbound

This paper cites Securing large language models: Addressing bias, misinformation, and prompt attacks.

Deep Learning Model Security: Threats and Defenses Securing large language models: Addressing bias, misinformation, and prompt attacks

Reference 24

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Observation 0f5bbd3c-1c39-43fb-bdfe-219525c50bc7 · outbound

This paper cites Deep learning and machine learning with gpgpu and cuda: Unlocking the power of parallel computing.

Deep Learning Model Security: Threats and Defenses Deep learning and machine learning with gpgpu and cuda: Unlocking the power of parallel computing

Reference 25

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This paper cites Deep learning and machine learning, advancing big data analytics and management: Unveiling BIBLIOGRAPHY 173 ai’s potential through tools, techniques, and applications.

Deep Learning Model Security: Threats and Defenses Deep learning and machine learning, advancing big data analytics and management: Unveiling BIBLIOGRAPHY 173 ai’s potential through tools, techniques, and applications

Reference 26

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Observation c082e244-37dd-40e3-bfb2-14770b0df89e · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Deep Learning Model Security: Threats and Defenses Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 27

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This paper cites Deep learning based vulnerability detection: Are we there yet? IEEE Transactions on Software Engineering , 48(9):3280–3296, 2021.

Deep Learning Model Security: Threats and Defenses Deep learning based vulnerability detection: Are we there yet? IEEE Transactions on Software Engineering , 48(9):3280–3296, 2021

Reference 28

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This paper cites An empirical study of deep learning models for vulnerability detection.

Deep Learning Model Security: Threats and Defenses An empirical study of deep learning models for vulnerability detection

Reference 29

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Observation 3c49f5ee-7a92-48d2-b678-ceca6f9996d5 · outbound

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Deep Learning Model Security: Threats and Defenses Shallow or deep? an empirical study on detecting vulnerabilities using deep learning

Reference 30

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This paper cites Adversarial Manipulation of Deep Representations.

Deep Learning Model Security: Threats and Defenses Adversarial Manipulation of Deep Representations

Reference 31

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This paper cites A survey of the implementations of model inversion attacks.

Deep Learning Model Security: Threats and Defenses A survey of the implementations of model inversion attacks

Reference 32

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Deep Learning Model Security: Threats and Defenses Practical black-box attacks against machine learning

Reference 33

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Deep Learning Model Security: Threats and Defenses Survey on white-box attacks and solutions

Reference 34

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Deep Learning Model Security: Threats and Defenses Query efficient black-box adversarial attack on deep neural networks

Reference 35

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This paper cites Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples.

Deep Learning Model Security: Threats and Defenses Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

Reference 36

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Deep Learning Model Security: Threats and Defenses Logit Pairing Methods Can Fool Gradient-Based Attacks

Reference 37

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This paper cites Adversarial attacks and defenses against deep neural networks: a survey.

Deep Learning Model Security: Threats and Defenses Adversarial attacks and defenses against deep neural networks: a survey

Reference 38

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Deep Learning Model Security: Threats and Defenses Adversarial Examples that Fool Detectors

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Deep Learning Model Security: Threats and Defenses Hands-on machine learning on google cloud platform: Implementing smart and efficient analytics using cloud ml engine

Reference 40

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Deep Learning Model Security: Threats and Defenses Gpt understands, too

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Deep Learning Model Security: Threats and Defenses Recent advances in convolutional neural networks

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Deep Learning Model Security: Threats and Defenses A comprehensive survey on poisoning attacks and countermeasures in machine learning

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Deep Learning Model Security: Threats and Defenses Deep model poisoning attack on feder- ated learning

Reference 44

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This paper cites A huber loss minimization approach to byzantine ro- bust federated learning.

Deep Learning Model Security: Threats and Defenses A huber loss minimization approach to byzantine ro- bust federated learning

Reference 45

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Deep Learning Model Security: Threats and Defenses Rethinking label flipping attack: From sample masking to sample thresholding

Reference 46

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Deep Learning Model Security: Threats and Defenses Adversarial learning targeting deep neural network classification: A comprehensive review of defenses against attacks

Reference 47

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Deep Learning Model Security: Threats and Defenses Generative adversarial nets

Reference 48

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Deep Learning Model Security: Threats and Defenses Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 49

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Deep Learning Model Security: Threats and Defenses Towards evaluating the robustness of neural networks

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Deep Learning Model Security: Threats and Defenses Reflections on trusting trust

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Deep Learning Model Security: Threats and Defenses BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

Reference 52

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This paper cites An assessment by the statin intolerance panel: 2014 update.

Deep Learning Model Security: Threats and Defenses An assessment by the statin intolerance panel: 2014 update

Reference 53

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Deep Learning Model Security: Threats and Defenses Explaining and Harnessing Adversarial Examples

Reference 54

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Deep Learning Model Security: Threats and Defenses The limitations of deep learning in adversarial settings

Reference 55

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This paper cites Poisoning Attacks and Defenses on Artificial Intelligence: A Survey.

Deep Learning Model Security: Threats and Defenses Poisoning Attacks and Defenses on Artificial Intelligence: A Survey

Reference 56

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Observation 91d85854-0dec-4b64-87be-c9f58a9e2af5 · outbound

This paper cites Robust nonparametric regression under poisoning attack.

Deep Learning Model Security: Threats and Defenses Robust nonparametric regression under poisoning attack

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.587985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7ef301e5-7126-4564-955f-4b03fa5264a8 · outbound

This paper cites Calibrating noise to sensitivity in private data analysis.

Deep Learning Model Security: Threats and Defenses Calibrating noise to sensitivity in private data analysis

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.567831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 244228f9-02ca-4983-a48b-5215bdf59db6 · outbound

This paper cites Robust federated learning with realistic corruption.

Deep Learning Model Security: Threats and Defenses Robust federated learning with realistic corruption

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.550701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3a1aaf55-3f09-427a-81f5-01b1cf835c20 · outbound

This paper cites High dimensional distributed gradient descent with arbitrary number of byzantine attackers.

Deep Learning Model Security: Threats and Defenses High dimensional distributed gradient descent with arbitrary number of byzantine attackers

Reference 60

Resolution: verified exact; raw_fallback, observed 2026-08-11T17:24:26.403824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6c648534-42a6-4730-88bd-27fdc1d9a277 · outbound

This paper cites Protocols for secure computations.

Deep Learning Model Security: Threats and Defenses Protocols for secure computations

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.531182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a12a609e-b84a-4c1d-8c79-7ea52a6333fe · outbound

This paper cites Privacy preserving generative adversarial networks to model electronic health records.

Deep Learning Model Security: Threats and Defenses Privacy preserving generative adversarial networks to model electronic health records

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.512705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3d83fb98-44e3-43e4-97fb-009775feac93 · outbound

This paper cites Neural cleanse: Identifying and mitigating backdoor attacks in neural networks.

Deep Learning Model Security: Threats and Defenses Neural cleanse: Identifying and mitigating backdoor attacks in neural networks

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.485819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9bcbfb0f-d7ee-4128-abf9-7b03d8e81820 · outbound

This paper cites A survey on contrastive self-supervised learning.

Deep Learning Model Security: Threats and Defenses A survey on contrastive self-supervised learning

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.979887Z

Source-reported events for the cited work

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Observation d57a120e-95f7-4cbc-a744-c282942175cb · outbound

This paper cites Unsupervised visual representation learning by context prediction.

Deep Learning Model Security: Threats and Defenses Unsupervised visual representation learning by context prediction

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.452417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 80110ee1-a6fd-46d0-9334-c6d553e7843d · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Deep Learning Model Security: Threats and Defenses Distilling the Knowledge in a Neural Network

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.990309Z

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Unavailable: canonical work link unavailable.

Observation 0c7dd140-dec0-479a-b1c1-0b0a6c8e37ae · outbound

This paper cites Adversarial attack on graph structured data.

Deep Learning Model Security: Threats and Defenses Adversarial attack on graph structured data

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.433395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ee40af29-d022-4f59-9177-2837cb838e88 · outbound

This paper cites Poisoning Attacks against Support Vector Machines.

Deep Learning Model Security: Threats and Defenses Poisoning Attacks against Support Vector Machines

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:26.002330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24681ff7-bebb-46b8-8f3f-09f1589831ec · outbound

This paper cites Protecting sensitive knowledge by data sanitization.

Deep Learning Model Security: Threats and Defenses Protecting sensitive knowledge by data sanitization

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.410342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d956e29d-6195-4e91-9c78-b27506d46d1f · outbound

This paper cites Defending Model Inversion and Membership Inference Attacks via Prediction Purification.

Deep Learning Model Security: Threats and Defenses Defending Model Inversion and Membership Inference Attacks via Prediction Purification

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:24:26.266624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b4f52ba4-f23f-4fe7-a7b0-7b820eb892be · outbound

This paper cites Differential privacy: A survey of results.

Deep Learning Model Security: Threats and Defenses Differential privacy: A survey of results

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.386931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f0a72494-5677-4126-aff1-60ada85f98df · outbound

This paper cites A Survey on Poisoning Attacks Against Supervised Machine Learning.

Deep Learning Model Security: Threats and Defenses A Survey on Poisoning Attacks Against Supervised Machine Learning

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:24:26.240530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dcddf8d9-66d6-4e79-ade9-57c161e88c92 · outbound

This paper cites Robust loss functions under label noise for deep neural networks.

Deep Learning Model Security: Threats and Defenses Robust loss functions under label noise for deep neural networks

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.366483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 34edffb4-e149-4ae1-b88b-2c451325620a · outbound

This paper cites Online anomaly detection under adversarial impact.

Deep Learning Model Security: Threats and Defenses Online anomaly detection under adversarial impact

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.347657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1c1a70fd-37db-4f0f-ab55-3bc9dd0c6b8d · outbound

This paper cites Face recognition systems: A survey.

Deep Learning Model Security: Threats and Defenses Face recognition systems: A survey

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.326866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 120b946b-1aab-4b62-b870-cb14fbd931c2 · outbound

This paper cites A comprehensive review on malware detection approaches.

Deep Learning Model Security: Threats and Defenses A comprehensive review on malware detection approaches

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.301620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6bd75f38-be18-4321-b60f-7f694e97e63a · outbound

This paper cites A Review of Network Traffic Analysis and Prediction Techniques.

Deep Learning Model Security: Threats and Defenses A Review of Network Traffic Analysis and Prediction Techniques

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:24:26.209585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b16b4d08-4530-45ac-bb4d-bc48b7f687fc · outbound

This paper cites Automated Cyber Defence: A Review.

Deep Learning Model Security: Threats and Defenses Automated Cyber Defence: A Review

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:26.067370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 029b2624-a53a-4e95-b357-bd05a60586b0 · outbound

This paper cites No more chewy centers: Introducing the zero trust model of information security.

Deep Learning Model Security: Threats and Defenses No more chewy centers: Introducing the zero trust model of information security

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.281490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 40edd535-7ded-4435-92c0-c09acb80e921 · outbound

This paper cites Generative adversarial networks.

Deep Learning Model Security: Threats and Defenses Generative adversarial networks

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.263828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ddc34542-0057-49a9-a69f-c74516438079 · outbound

This paper cites Dynamical Variational Autoencoders: A Comprehensive Review.

Deep Learning Model Security: Threats and Defenses Dynamical Variational Autoencoders: A Comprehensive Review

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:26.086247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 474b858b-ed3d-46b6-9589-6da31eb329da · outbound

This paper cites Secure multi-party computation: theory, practice and applications.

Deep Learning Model Security: Threats and Defenses Secure multi-party computation: theory, practice and applications

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.242052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 69e21d91-4148-44e8-81de-5d9417a0ec29 · outbound

This paper cites Homomorphic encryption.

Deep Learning Model Security: Threats and Defenses Homomorphic encryption

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.223530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation adca9c7f-9bbc-41eb-96db-cea4df5a4016 · outbound

This paper cites A review of applications in federated learning.

Deep Learning Model Security: Threats and Defenses A review of applications in federated learning

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.205307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

Observation f1a692df-d60d-413f-8506-cd31bd64a80b · inbound

A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions cites this paper.

A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions Deep Learning Model Security: Threats and Defenses

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-06-30T09:54:34.403192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 621cf58c-e0d6-42ea-81c3-d49222504b18 · inbound

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift cites this paper.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift Deep Learning Model Security: Threats and Defenses

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-14T17:36:18.728469Z

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Unavailable: canonical work link unavailable.