Pith. sign in

Paper Citation Record · LEDGER

Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2409.19798.

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

pith.paper-citation-record.v1
2409.19798 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:13:35.177904Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:49:57.323922Z

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 ab3d86a1-1f34-40b1-91e0-4293ba203efc · inbound

LUMIA: Linear probing for Unimodal and MultiModal Membership Inference Attacks leveraging internal LLM states cites this paper.

LUMIA: Linear probing for Unimodal and MultiModal Membership Inference Attacks leveraging internal LLM states Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-12T05:48:19.123210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:48:19.123210Z digest=sha256:9f2745d1539b0f1f765f6f6888a74b795fdc2db7677e5428b1636acc5912b7c0

Observation 93f267b9-b1cb-4b61-9756-1543e4fd02f3 · inbound

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

Position: Adversarial ML for LLMs Is Not Making Any Progress Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-09T12:47:21.814843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T12:47:21.814843Z digest=sha256:3d62b99a7cd41c7e3805b951d4a7ccc4c8b9715d347a36940d3e0c052ecbc586

Observation ad46f5e7-154d-427a-8d10-8a84a5600387 · inbound

STAMP Your Content: Proving Dataset Membership via Watermarked Rephrasings cites this paper.

STAMP Your Content: Proving Dataset Membership via Watermarked Rephrasings Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-16T12:13:35.177904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:13:35.177904Z digest=sha256:71dd95561d778b987b0ff535514143b987050fa8a41d7505c08f5a89e3cc4fdf

Observation 64e113d1-8645-42f4-8f16-0c4c9ddf4cf8 · inbound

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment cites this paper.

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

Reference 116

Resolution
unresolved
no resolver link, observed 2026-08-16T11:24:12.308782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:24:12.308782Z digest=sha256:0122cfac931d368345b227f439de9f8fb6b42c15d4169a28ae737d3715e2960f

Observation 5f7037d6-eea1-4f06-b42c-6a84212bd4da · inbound

Efficient Machine Unlearning by Model Splitting and Core Sample Selection cites this paper.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-15T22:36:06.676088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:36:06.676088Z digest=sha256:7068f44c5f7ea3f595eb024de2dc00b54a9f252d82da02985a405be98b2eb47f

Observation c3918873-35cc-415c-bd61-7447db2bab9b · inbound

Membership Inference Attacks on Sequence Models cites this paper.

Membership Inference Attacks on Sequence Models Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T10:27:29.099807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:27:29.099807Z digest=sha256:00b38118651213a747ac1f16be312b35c0cf8c3b39d8b5daf7d7540f3f409a03

Observation 46da7838-d8a7-46bc-a8c6-f476d6370050 · inbound

What Really is a Member? Discrediting Membership Inference via Poisoning cites this paper.

What Really is a Member? Discrediting Membership Inference via Poisoning Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T06:13:04.724789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:13:04.724789Z digest=sha256:ff85718bb8b741eb3bbd13c410ec29799e71827b419fa7dab8e0b63ff9df3d74

Observation d7d1ba9e-f3ba-4b3b-81e3-47f987ded3f4 · inbound

Membership Inference Attacks for Unseen Classes cites this paper.

Membership Inference Attacks for Unseen Classes Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

Reference 28

Resolution
malformed identifier
no resolver link, observed 2026-08-07T06:04:25.061599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:04:25.061599Z digest=sha256:ed4f497dd3bbd591702d2c9052ec74c89342f228c5cbe45af5aa69545c58947d

Observation 06486087-dd1c-4781-ba8e-977b5d196c03 · inbound

SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks cites this paper.

SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

Reference 97

Resolution
unresolved
no resolver link, observed 2026-08-07T04:33:17.059579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:33:17.059579Z digest=sha256:c55163f32cc9aa762de53db75c9ac1069fc929e9b778f29df3c3e9bc3ef93203

Observation 031ef1be-6e45-46b2-b94c-9973d3bad810 · inbound

Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble cites this paper.

Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-07T00:30:35.313912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:30:35.313912Z digest=sha256:6054c4fbc25ff4cf114f999d8c07e0ae2d74c2bbee732dbf739f510d98808d43

Observation ee29afb1-f6d5-4e8c-a1cc-88f9d6a9fd20 · inbound

Winter Soldier: Backdooring Language Models at Pre-Training with Indirect Data Poisoning cites this paper.

Winter Soldier: Backdooring Language Models at Pre-Training with Indirect Data Poisoning Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-15T19:54:57.865792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:54:57.865792Z digest=sha256:915de9e279372af1c259ba5dffc135ad9249fbed795cd86f093dcd2424494c7a

Observation 5bae93e0-d757-4939-a913-394acf31b34a · inbound

Unlocking Post-hoc Dataset Inference with Synthetic Data cites this paper.

Unlocking Post-hoc Dataset Inference with Synthetic Data Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-15T19:42:25.208322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:42:25.208322Z digest=sha256:22fad7bc1bbbd9230e908092f9edc9467814a895c67f7ca4939ca3eee8d64de5

Observation 21032e1b-e41d-4d7a-bfb3-785fb5e58b9e · inbound

Identifying Pre-training Data in LLMs: A Neuron Activation-Based Detection Framework cites this paper.

Identifying Pre-training Data in LLMs: A Neuron Activation-Based Detection Framework Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-06T15:15:21.422233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T15:15:21.422233Z digest=sha256:8bfb64d5664955bfcd2746a55476337a0b17bf70b6a94403f660c3a302f2663a

Observation 9e649d56-6673-4c36-ad51-d58c94c904bc · inbound

Causal Evaluation of Membership Inference Attacks cites this paper.

Causal Evaluation of Membership Inference Attacks Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-03T05:23:05.159658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:23:05.159658Z digest=sha256:1e394811b5762c73484f933fad401be9f9ca0c54c797caae6300e73fe0d53920

Observation 4a897c75-5e11-4b8a-b097-6872d97a307e · inbound

Natural Identifiers for Privacy and Data Audits in Large Language Models cites this paper.

Natural Identifiers for Privacy and Data Audits in Large Language Models Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

Reference 22

Resolution
verified exact
arxiv_id, observed 2026-07-04T16:49:57.325646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-26T00:16:34.961376Z digest=sha256:5bd3a6ae5325406b17d18786ca04de192d78cb8df25ee8dd2e32763196c69ad8