Paper Citation Record · LEDGER

AnyAttack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2410.05346.

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

pith.paper-citation-record.v1
2410.05346 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:35:55.980677Z

measured 1 of 1 external citation measurements

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

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

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f4eb6653-002f-47ac-a9d8-911967e89a21 · inbound

Visual Adversarial Attack on Vision-Language Models for Autonomous Driving cites this paper.

Visual Adversarial Attack on Vision-Language Models for Autonomous Driving AnyAttack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-23T16:35:42.215498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 543d7026-75b6-45ef-ae3e-8121015e13d5 · inbound

Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety cites this paper.

Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety AnyAttack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models

Reference 273

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:42:33.894284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9c5b7731-6173-402f-bac8-5f34f612c299 · inbound

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment cites this paper.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment AnyAttack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:55.980677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a8dffec-ce67-4e58-a44e-82eece6136dc · inbound

One Object, Multiple Lies: A Benchmark for Cross-task Adversarial Attack on Unified Vision-Language Models cites this paper.

One Object, Multiple Lies: A Benchmark for Cross-task Adversarial Attack on Unified Vision-Language Models AnyAttack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T18:40:56.361825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48776b79-377a-481d-997f-9bd9fe9e41f7 · inbound

Semantic Router: On the Feasibility of Hijacking MLLMs via a Single Adversarial Perturbation cites this paper.

Semantic Router: On the Feasibility of Hijacking MLLMs via a Single Adversarial Perturbation AnyAttack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T20:27:03.867334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b7f524e-808c-439b-a89d-7d53f69cdcc0 · inbound

VisInject: Disruption != Injection -- A Dual-Dimension Evaluation of Universal Adversarial Attacks on Vision-Language Models cites this paper.

VisInject: Disruption != Injection -- A Dual-Dimension Evaluation of Universal Adversarial Attacks on Vision-Language Models AnyAttack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:56:08.161856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3241874d-6ed6-49b7-8818-3af6b24136a4 · inbound

Adversarial Diffusion Across Modalities: A Fusion Survey of Attacks, Defenses, and Evaluation for Text, Vision, and Vision-Language Models cites this paper.

Adversarial Diffusion Across Modalities: A Fusion Survey of Attacks, Defenses, and Evaluation for Text, Vision, and Vision-Language Models AnyAttack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models

Reference 79

Resolution: verified exact; arxiv_id, observed 2026-06-26T04:38:59.181273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.