Paper Citation Record · LEDGER

Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2309.17410.

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

pith.paper-citation-record.v1
2309.17410 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 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 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:47:15.244674Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T20:58:26.108736Z

Reference resolution

0 of 0 outbound references displayed

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verified fuzzy0
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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 00b4f6dc-fc83-4fdc-9a34-fcd0ed984109 · inbound

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation cites this paper.

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 76

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T17:56:23.597335Z

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 42192545-a00c-4085-ab55-cdde7149bb58 · inbound

TOFU: A Task of Fictitious Unlearning for LLMs cites this paper.

TOFU: A Task of Fictitious Unlearning for LLMs Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-16T11:07:39.289737Z

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 ccb6b89f-1795-401e-9014-5536df499b5b · inbound

Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning cites this paper.

Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:26:55.067099Z

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 e5dd1676-b5d7-4a18-9ad8-3da17918dcc1 · inbound

Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey cites this paper.

Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 114

Resolution: verified exact; arxiv_id, observed 2026-05-23T20:58:26.112961Z

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 e63164f3-c03d-4fef-8afa-59dedb47ab4f · inbound

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

Position: Adversarial ML for LLMs Is Not Making Any Progress Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-09T12:47:21.734049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11943eb2-7684-42a4-9402-04f4fe61a795 · inbound

Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities cites this paper.

Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-09T14:47:15.244674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f40819a-a57e-4375-ae14-5ec259a731e8 · inbound

Prompt Attacks Reveal Superficial Knowledge Removal in Unlearning Methods cites this paper.

Prompt Attacks Reveal Superficial Knowledge Removal in Unlearning Methods Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T04:37:07.507769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe641c6d-bf13-4359-9fbe-835593427b04 · inbound

Step-by-Step Reasoning Attack: Revealing 'Erased' Knowledge in Large Language Models cites this paper.

Step-by-Step Reasoning Attack: Revealing 'Erased' Knowledge in Large Language Models Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T00:56:31.588211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc899506-3af0-4496-93d5-c9a96ccdab7c · inbound

Report on NSF Workshop on Science of Safe AI cites this paper.

Report on NSF Workshop on Science of Safe AI Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T23:01:43.187959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb9729dd-18f4-4716-b14a-cbe114419238 · 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 Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T22:23:11.236791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ed82dce-814b-4478-b7ee-082a859ac8ec · inbound

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests cites this paper.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.717513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 683d92f7-89a7-46e7-9dc5-df3e00a8b553 · inbound

Towards Evaluation for Real-World LLM Unlearning cites this paper.

Towards Evaluation for Real-World LLM Unlearning Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T05:48:02.749611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15514194-b3b1-4602-82b3-ca4bf5d6e42d · inbound

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives cites this paper.

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 162

Resolution: unresolved; no resolver link, observed 2026-08-05T18:12:37.628103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 759df5d9-4ec4-40ee-87ff-3a91cfe38897 · inbound

Unlearning What Matters: Token-Level Attribution for Precise Language Model Unlearning cites this paper.

Unlearning What Matters: Token-Level Attribution for Precise Language Model Unlearning Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:36:06.685073Z

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 9288345e-050f-49c5-9eb1-944fb8e4c3b1 · inbound

MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents cites this paper.

MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:36:27.403960Z

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 6c58134d-4b76-4172-a919-40e2da98878d · inbound

MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents cites this paper.

MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:27:30.109958Z

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 00f35960-b29e-4429-ab16-4aaffe189413 · inbound

MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents cites this paper.

MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:45:05.863556Z

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.