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Paper Citation Record · LEDGER

Breach By A Thousand Leaks: Unsafe Information Leakage in `Safe' AI Responses

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2407.02551.

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

pith.paper-citation-record.v1
2407.02551 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 2026-08-15T06:32:42.880941+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:47:21.670033Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T10:32:14.773793Z

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 25e8cd30-8d47-46a1-8a3d-3b7220e4fa23 · inbound

Position: Adversarial ML for LLMs Is Not Making Any Progress cites this paper.

Position: Adversarial ML for LLMs Is Not Making Any Progress Breach By A Thousand Leaks: Unsafe Information Leakage in `Safe' AI Responses

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-09T12:47:21.670033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T12:47:21.670033Z digest=sha256:20ef5f67459b96e8a443504f11849d26dcca264b5211e1edf00f64a9caca9825

Observation 10f8b811-0254-4319-9964-7d2beae0018a · inbound

Benchmarking Misuse Mitigation Against Covert Adversaries cites this paper.

Benchmarking Misuse Mitigation Against Covert Adversaries Breach By A Thousand Leaks: Unsafe Information Leakage in `Safe' AI Responses

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-19T10:32:14.775623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-19T10:29:05.104520Z digest=sha256:64f74e213a6abc79ac32d364c5d36c50a684de9d56f1f573384b14926cfc8a98

Observation dcd95c5d-ffb9-484a-b7fc-9d929a2e496a · inbound

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses cites this paper.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Breach By A Thousand Leaks: Unsafe Information Leakage in `Safe' AI Responses

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-04T09:25:41.056143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T09:25:41.056143Z digest=sha256:1d88bc8be354d33f6cf4594fc42bdb128510e8033738dbdef223c6e7690abd59

Observation c5b9060d-406a-42a1-a836-973c8ef463cf · inbound

Cross-Session Threats in AI Agents: Benchmark, Evaluation, and Algorithms cites this paper.

Cross-Session Threats in AI Agents: Benchmark, Evaluation, and Algorithms Breach By A Thousand Leaks: Unsafe Information Leakage in `Safe' AI Responses

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-11T14:01:06.443389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-09T23:44:44.036643Z digest=sha256:425424ea85e85dc91cbe9f27a155c7acd01007b1fd23e6e281332cc7b47e8f21

Observation b26dd81b-395d-4418-a466-aaa50b355dc4 · inbound

Safeguards Based on Copyable Context Cannot Provide Reliable Safety for LLMs cites this paper.

Safeguards Based on Copyable Context Cannot Provide Reliable Safety for LLMs Breach By A Thousand Leaks: Unsafe Information Leakage in `Safe' AI Responses

Reference 16

Resolution
unresolved
no resolver link, observed 2026-07-31T22:25:22.240902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-31T22:25:22.240902Z digest=sha256:2085b1a6f764072259a3be70c916625a4c868b5825b9ccfb17f52370d7b889e7