Paper Citation Record · LEDGER

Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2311.06062.

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

pith.paper-citation-record.v1
2311.06062 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:30:15.788544Z

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

10
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 e8bf427f-d0c0-4209-b46e-6a5a0e81cf30 · inbound

Differentially Private Policy Gradient cites this paper.

Differentially Private Policy Gradient Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T21:30:15.788544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9deaa668-e970-4dd3-b7da-9a0082b88612 · inbound

Large Language Model Adversarial Landscape Through the Lens of Attack Objectives cites this paper.

Large Language Model Adversarial Landscape Through the Lens of Attack Objectives Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T10:29:49.897327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce1fdda3-e272-4d47-9dcc-1d208ea1ac43 · inbound

Large Language Model Agent: A Survey on Methodology, Applications and Challenges cites this paper.

Large Language Model Agent: A Survey on Methodology, Applications and Challenges Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

Reference 228

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:52:10.499147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0e4d2c32-4061-4cca-b461-054d3a91c160 · inbound

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation cites this paper.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T00:46:10.051917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a3f3687-8b3c-45a5-9fc2-74a4f06884c8 · inbound

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training cites this paper.

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T20:54:48.928617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2715318c-29d3-4f42-9d9e-b63ddd33a3d7 · inbound

A Survey: Towards Privacy and Security in Mobile Large Language Models cites this paper.

A Survey: Towards Privacy and Security in Mobile Large Language Models Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-05T11:39:20.317229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ed703ca-ca37-4283-9587-53ee309c3edc · inbound

Auditing Data Membership in Reinforcement Learning With Verifiable Rewards cites this paper.

Auditing Data Membership in Reinforcement Learning With Verifiable Rewards Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-17T21:40:17.744114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6f51ca35-e414-4d2e-9ca6-e7a32df6f7c0 · inbound

Single-Sample Black-Box Membership Inference Attack against Vision-Language Models via Cross-modal Semantic Alignment cites this paper.

Single-Sample Black-Box Membership Inference Attack against Vision-Language Models via Cross-modal Semantic Alignment Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:08:20.925754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 92ac6a1f-83e1-46a5-8d26-d657d769179b · inbound

Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications cites this paper.

Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T17:24:56.576884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation aa39a4be-eda0-4eea-babc-2ab6dea0fc07 · inbound

Leak It: A Probabilistic Approach to Training-Data Extraction from Black-Box Language Models cites this paper.

Leak It: A Probabilistic Approach to Training-Data Extraction from Black-Box Language Models Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T01:13:47.579666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.