Paper Citation Record · LEDGER

Adversarial Attacks on Neural Network Policies

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:1702.02284.

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

pith.paper-citation-record.v1
1702.02284 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T14:03:37.150597Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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malformed identifier0
metadata mismatch0

External citation measurements

68
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d93574a5-7dc8-4e5f-8b4d-d69ea2a4719f · inbound

Learning to Cope with Adversarial Attacks cites this paper.

Learning to Cope with Adversarial Attacks Adversarial Attacks on Neural Network Policies

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-05-25T13:50:53.769731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f10ca7dc-b9ce-470e-9d6c-e05c3705f090 · inbound

MirrorCheck: Efficient Adversarial Defense for Vision-Language Models cites this paper.

MirrorCheck: Efficient Adversarial Defense for Vision-Language Models Adversarial Attacks on Neural Network Policies

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-05-25T09:05:35.870395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fcad3b20-2fbb-4f94-9883-a6b99f23b3ce · inbound

Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning cites this paper.

Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning Adversarial Attacks on Neural Network Policies

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-23T03:25:20.350624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6d288016-9eb8-4b83-869e-0d337605d80e · inbound

How Vulnerable Is My Learned Policy? Universal Adversarial Perturbation Attacks On Modern Behavior Cloning Policies cites this paper.

How Vulnerable Is My Learned Policy? Universal Adversarial Perturbation Attacks On Modern Behavior Cloning Policies Adversarial Attacks on Neural Network Policies

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-23T03:32:28.011343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3078af4f-9a09-4e73-ad6c-aa7ab92ce482 · inbound

Density-Ratio Weighted Behavioral Cloning: Learning Control Policies from Corrupted Datasets cites this paper.

Density-Ratio Weighted Behavioral Cloning: Learning Control Policies from Corrupted Datasets Adversarial Attacks on Neural Network Policies

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-21T20:54:21.640370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3b9940fd-b6b9-445c-b4a1-d5b1100a41da · inbound

A Speculative GLRT-Backed ApproachRobust Deep Learning-Based Array Processing cites this paper.

A Speculative GLRT-Backed ApproachRobust Deep Learning-Based Array Processing Adversarial Attacks on Neural Network Policies

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-05-16T23:18:40.268463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ee25e3e8-bcca-48fe-bfd5-7a60bd25b530 · inbound

Corruption-robust Offline Multi-agent Reinforcement Learning From Human Feedback cites this paper.

Corruption-robust Offline Multi-agent Reinforcement Learning From Human Feedback Adversarial Attacks on Neural Network Policies

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-14T21:19:28.993455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2a7aa528-7c1d-43c9-8cdc-fdb971b18a30 · inbound

Efficient Preference Poisoning Attack on Offline RLHF cites this paper.

Efficient Preference Poisoning Attack on Offline RLHF Adversarial Attacks on Neural Network Policies

Reference 124

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T05:50:27.176565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3f9d9642-540c-4a41-ae04-aa69856ec973 · inbound

SoK: A Comprehensive Analysis of the Current Status of Neural Tangent Generalization Attacks with Research Directions cites this paper.

SoK: A Comprehensive Analysis of the Current Status of Neural Tangent Generalization Attacks with Research Directions Adversarial Attacks on Neural Network Policies

Reference 42

Resolution: metadata mismatch; local_arxiv, observed 2026-05-14T20:42:57.410541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c2168dc1-5b1f-4f85-a5e2-be22a5ba2fe3 · inbound

When Actions Disappear: Adversarial Action Removal in Self-Play Reinforcement Learning cites this paper.

When Actions Disappear: Adversarial Action Removal in Self-Play Reinforcement Learning Adversarial Attacks on Neural Network Policies

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T23:43:51.046144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 22c69ecf-735e-4fbe-8005-820b52349a0f · inbound

Threats to Arabic Handwriting Recognition: Investigating Black-Box Adversarial Attacks on embedded ConvNet models cites this paper.

Threats to Arabic Handwriting Recognition: Investigating Black-Box Adversarial Attacks on embedded ConvNet models Adversarial Attacks on Neural Network Policies

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-20T11:38:14.683369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c49c3b66-32f3-447d-aaa0-014e75316f29 · inbound

Same Weights, Different Robot: A Deployment Safety View of VLA Policies cites this paper.

Same Weights, Different Robot: A Deployment Safety View of VLA Policies Adversarial Attacks on Neural Network Policies

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-07-02T04:06:34.647998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4470cdd6-9d5c-46ab-a5c9-78100c9808a7 · inbound

Latent Anchor-Driven Test Generation for Deep Neural Networks cites this paper.

Latent Anchor-Driven Test Generation for Deep Neural Networks Adversarial Attacks on Neural Network Policies

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-07-02T06:06:41.168085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a5e0a130-9ecc-492c-84b8-f31e6dd1dafc · inbound

Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes cites this paper.

Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes Adversarial Attacks on Neural Network Policies

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-07-02T06:06:41.378000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 17595ccd-4956-42d5-898b-14b0aa97250b · inbound

Test-time Adversarial Takeover: A Real-time Hijacking Interface against Robotic Diffusion Policies cites this paper.

Test-time Adversarial Takeover: A Real-time Hijacking Interface against Robotic Diffusion Policies Adversarial Attacks on Neural Network Policies

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-07-03T05:47:41.582150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 961a64c5-89d9-445c-b1ac-75db8950541a · inbound

The Game Changer Problem: Controlling Equilibria with Discrete Rewards cites this paper.

The Game Changer Problem: Controlling Equilibria with Discrete Rewards Adversarial Attacks on Neural Network Policies

Reference 104

Resolution: metadata mismatch; local_arxiv, observed 2026-06-30T08:24:25.795696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation de9cd5f5-a8e8-4029-8e68-5b894008512a · inbound

RoAd-RL: A Unified Library and Benchmark for Robust Adversarial Reinforcement Learning cites this paper.

RoAd-RL: A Unified Library and Benchmark for Robust Adversarial Reinforcement Learning Adversarial Attacks on Neural Network Policies

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-06-30T07:04:21.123322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6240d8a6-600c-4a10-86d1-204d47bdf497 · inbound

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies cites this paper.

Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies Adversarial Attacks on Neural Network Policies

Reference 12

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T21:36:34.384909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9c01e764-00f5-41d6-bd00-bf267e5e1873 · inbound

Security of World-Model-Based Embodied AI: A Lifecycle of Threats, Defenses, and Evaluation cites this paper.

Security of World-Model-Based Embodied AI: A Lifecycle of Threats, Defenses, and Evaluation Adversarial Attacks on Neural Network Policies

Reference 130

Resolution: unresolved; no resolver link, observed 2026-07-31T14:03:37.150597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.