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Paper Citation Record · LEDGER

Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2408.02416.

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

pith.paper-citation-record.v1
2408.02416 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 2026-08-07T10:54:53.613562Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:59:20.697040Z

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 122f18a0-d412-4dd3-8907-aa78f2d2b866 · inbound

To trust or not to trust: Attention-based Trust Management for LLM Multi-Agent Systems cites this paper.

To trust or not to trust: Attention-based Trust Management for LLM Multi-Agent Systems Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-19T11:32:17.381468Z

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=2026-05-19T11:30:47.877793Z digest=sha256:f13b4ef805ca04204d421bc24055d76be6c256256ae8ab2b1e667da447e5a94d

Observation 009ef784-4367-4d16-8ada-b42c2898a859 · inbound

Privacy and Security Threat for OpenAI GPTs cites this paper.

Privacy and Security Threat for OpenAI GPTs Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T10:54:53.613562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:54:53.613562Z digest=sha256:4bca0c43cfd7fbd7da24e368508782eb3dbe2e747089eb9445bf1de034a579ce

Observation 414498dc-713b-4933-89a7-c561d909e403 · inbound

A Survey on Model Extraction Attacks and Defenses for Large Language Models cites this paper.

A Survey on Model Extraction Attacks and Defenses for Large Language Models Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T22:23:09.752446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:23:09.752446Z digest=sha256:9bfa5b2a7bbb473827fa67a55b436add376bd1dc8b148ae6f8cf635ea9b4ae6c

Observation 8b8826cc-f58b-44a6-a5f9-b5353d3a9a5f · inbound

SLIP: Soft Label Mechanism and Key-Extraction-Guided CoT-based Defense Against Instruction Backdoor in APIs cites this paper.

SLIP: Soft Label Mechanism and Key-Extraction-Guided CoT-based Defense Against Instruction Backdoor in APIs Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-19T00:56:56.354399Z

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=2026-05-19T00:54:00.464780Z digest=sha256:df9f80a8db77d4a553ada35cc8b4b1f4f5d3dfafbc84096fbdeb01dabfcb5f66

Observation 9a8515bc-a01e-45d7-9006-93550d9b20eb · inbound

Understanding and Mitigating Prompt Leaking Attacks in Real-World LLM-Based Applications cites this paper.

Understanding and Mitigating Prompt Leaking Attacks in Real-World LLM-Based Applications Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models

Reference 42

Resolution
verified exact
arxiv_id, observed 2026-07-04T00:59:20.699931Z

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=2026-06-26T20:47:36.337189Z digest=sha256:ed1c2f5a0622430261365c4d55c1c3afb778fae3151be21378ab30e89bcc8ebd