Paper Citation Record · LEDGER

Model Leeching: An Extraction Attack Targeting LLMs

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2309.10544.

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

pith.paper-citation-record.v1
2309.10544 v1

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 2026-08-09T12:55:02.423410Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T13:46:59.650105Z

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 3a69a0b3-abf8-4a6c-9e65-ebd0b02b1853 · inbound

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models cites this paper.

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models Model Leeching: An Extraction Attack Targeting LLMs

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T12:55:02.423410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73b10eb1-71ed-441d-88f6-30ce08ce716d · 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 Model Leeching: An Extraction Attack Targeting LLMs

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T22:23:07.300309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ca130f7-cf81-4ff7-bca1-729d12bf1e73 · inbound

From Rookie to Expert: Manipulating LLMs for Automated Vulnerability Exploitation in Enterprise Software cites this paper.

From Rookie to Expert: Manipulating LLMs for Automated Vulnerability Exploitation in Enterprise Software Model Leeching: An Extraction Attack Targeting LLMs

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-16T20:03:22.216172Z

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.

Observation a7f0a8dc-0324-41b5-b1c4-b932b4150b68 · inbound

An Embarrassingly Simple Detector for Model Extraction Attacks in Large Language Model API Traffic cites this paper.

An Embarrassingly Simple Detector for Model Extraction Attacks in Large Language Model API Traffic Model Leeching: An Extraction Attack Targeting LLMs

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-02T13:46:59.651681Z

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.

Observation fde6ccbc-4cd0-4a6d-a098-eae3c262c79a · inbound

RECAST: Model Reconstruction via Counterfactual-Aware Wasserstein Geometry under Limited Data cites this paper.

RECAST: Model Reconstruction via Counterfactual-Aware Wasserstein Geometry under Limited Data Model Leeching: An Extraction Attack Targeting LLMs

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-06-29T17:33:45.652128Z

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.