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Paper Citation Record · LEDGER

Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2311.11796.

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

pith.paper-citation-record.v1
2311.11796 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:43:59.346325Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T04:37:37.268017Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 23cc0bc8-b105-491e-8761-5e589404d8b0 · inbound

Images in Motion?: A First Look into Video Leakage in Collaborative Deep Learning cites this paper.

Images in Motion?: A First Look into Video Leakage in Collaborative Deep Learning Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-04T19:43:59.346325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T19:43:59.346325Z digest=sha256:5dc8fbf0eda4e4876071e469446c76570374e85fe2c18f37bfa1ed50341219f5

Observation a8410538-4492-4576-99b9-cbb630b10456 · inbound

Quality Degradation Attack in Synthetic Data cites this paper.

Quality Degradation Attack in Synthetic Data Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-21T21:10:38.529698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-21T21:10:05.466845Z digest=sha256:434a077e45b9ff123bc1d7aaf42b8ba37c519da883b4a00ceb4284a16ccd5950

Observation 032ac974-357b-4e98-9e26-06665296b39e · inbound

If It's Good Enough for You, It's Good Enough for Me: Transferability of Audio Sufficiencies across Models cites this paper.

If It's Good Enough for You, It's Good Enough for Me: Transferability of Audio Sufficiencies across Models Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems

Reference 31

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T18:58:08.972692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-13T18:53:48.881039Z digest=sha256:2581a603bab23542383dbdbb81392694ce918f261fc0d532f569271f61bd098f

Observation 1900e504-7a6a-4091-b822-18175ca185ab · inbound

Improving Adversarial Transferability on Vision-Language Pre-training Models via Surrogate-Specific Bias Correction cites this paper.

Improving Adversarial Transferability on Vision-Language Pre-training Models via Surrogate-Specific Bias Correction Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems

Reference 30

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T04:37:37.269550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-27T13:46:06.519188Z digest=sha256:d212e49c581560e92a58defa0f086aed0a1cd52419da58cdf50888e439b2a6e7

Observation b908ed42-d923-40e7-a8d3-cd7895079cb1 · inbound

Securing Deep Learning Hardware: A Survey of Side-Channel Vulnerabilities and Countermeasures cites this paper.

Securing Deep Learning Hardware: A Survey of Side-Channel Vulnerabilities and Countermeasures Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems

Reference 41

Resolution
unresolved
no resolver link, observed 2026-07-11T22:00:06.085598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T22:00:06.085598Z digest=sha256:3db2daa590bf9bbf4bdabc3980c833bde1eb7f2d30ec22cb06cab34d6c6a195d