Paper Citation Record · LEDGER

Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2406.20053.

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

pith.paper-citation-record.v1
2406.20053 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:24:12.734737Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:38:43.807884Z

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 f146f0cf-5d29-4f7f-ad00-53bf24f4e648 · 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 Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-23T20:58:26.280364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d2dfd2aa-fc9e-44d8-8b2e-4d2b8ead1051 · inbound

Towards Data Governance of Frontier AI Models cites this paper.

Towards Data Governance of Frontier AI Models Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:54.101000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 889afef5-80b1-443f-a3a6-acb3d07690f1 · inbound

Fun-tuning: Characterizing the Vulnerability of Proprietary LLMs to Optimization-based Prompt Injection Attacks via the Fine-Tuning Interface cites this paper.

Fun-tuning: Characterizing the Vulnerability of Proprietary LLMs to Optimization-based Prompt Injection Attacks via the Fine-Tuning Interface Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T19:44:46.756162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ba51696-cc2b-45bd-98ac-6316dedf5d0e · inbound

Compromising Honesty and Harmlessness in Language Models via Deception Attacks cites this paper.

Compromising Honesty and Harmlessness in Language Models via Deception Attacks Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T05:42:43.475213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69074e5f-192c-4118-8ad4-074dc892b989 · 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 Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation

Reference 242

Resolution: unresolved; no resolver link, observed 2026-08-16T11:24:12.734737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2da52532-3665-422b-b902-6af623ea321d · inbound

Benchmarking Misuse Mitigation Against Covert Adversaries cites this paper.

Benchmarking Misuse Mitigation Against Covert Adversaries Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T10:32:14.767204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fef00b4a-a03c-461e-b9c9-0382cfbf997d · 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 Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T22:23:08.525510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a0b5139-e84a-41e3-9293-f1bc1211fd54 · inbound

Poison Once, Refuse Forever: Weaponizing Alignment for Injecting Bias in LLMs cites this paper.

Poison Once, Refuse Forever: Weaponizing Alignment for Injecting Bias in LLMs Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T16:51:54.758884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9032a2ed-a944-4fda-b5aa-a94c4b5857f4 · 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 Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-05T11:39:20.462571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e151fc8-31fa-45ff-b41c-4a353e8a2f19 · inbound

ASTRA: An Automated Framework for Strategy Discovery, Retrieval, and Evolution for Jailbreaking LLMs cites this paper.

ASTRA: An Automated Framework for Strategy Discovery, Retrieval, and Evolution for Jailbreaking LLMs Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:55:37.997525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a27b54f0-9bc7-4f98-83b4-a5b699d9b133 · inbound

The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training cites this paper.

The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T00:45:50.605409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5e9508a6-668e-4928-a60a-80e0d49d23df · inbound

Few-Shot Truly Benign DPO Attack for Jailbreaking LLMs cites this paper.

Few-Shot Truly Benign DPO Attack for Jailbreaking LLMs Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T07:07:27.068775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ac318e72-8f7c-494a-8eda-0db4c377f68e · inbound

Building Better Activation Oracles cites this paper.

Building Better Activation Oracles Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-06-30T14:24:45.001679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a8c446a6-f145-44fb-83b7-7d27d5f358e7 · inbound

Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs cites this paper.

Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:38:43.809194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.