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Paper Citation Record · LEDGER

Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2405.16833.

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

pith.paper-citation-record.v1
2405.16833 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:32:57.956937Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T20:58:26.247370Z

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 7feaade8-75e4-49bd-bddd-a2e639dc9b9b · 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 Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-05-23T20:58:26.252024Z

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.

source=pdf_text observed=2026-05-23T20:58:16.237327Z digest=sha256:b3d9ff06730c9821bb369dfd493e876b321520a914448f4949db1825baac7b3c

Observation 8617ac82-4cff-4eb9-bba4-54a6f04e6661 · inbound

Topological Signatures of Adversaries in Multimodal Alignments cites this paper.

Topological Signatures of Adversaries in Multimodal Alignments Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-10T04:32:57.956937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T04:32:57.956937Z digest=sha256:28c589ab25bc9bc5e7343e4292e916a3fd9f47f9c07682017e0769e05b6fbb40

Observation 21dcb9ae-d6d4-444f-96b0-4f402b7abfbe · inbound

Activation Approximations Can Incur Safety Vulnerabilities Even in Aligned LLMs: Comprehensive Analysis and Defense cites this paper.

Activation Approximations Can Incur Safety Vulnerabilities Even in Aligned LLMs: Comprehensive Analysis and Defense Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-09T17:37:39.538531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T17:37:39.538531Z digest=sha256:3f92427f05aa1689514483a7f11b253a38f4a6ddc8a59aa41ccfab1115f71b6a

Observation 0376012b-a46d-4bbd-92da-a577106ff95a · inbound

Model Organisms for Emergent Misalignment cites this paper.

Model Organisms for Emergent Misalignment Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T04:07:28.158770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:07:28.158770Z digest=sha256:4167d3aa21cb363f6458d45ca6624de4f91fc425a9114a7ae26fc6de1ab07672

Observation b5585b38-4dda-4f39-b81e-1f6ac8fb0a08 · inbound

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead cites this paper.

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models

Reference 128

Resolution
unresolved
no resolver link, observed 2026-08-06T21:36:32.704651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:36:32.704651Z digest=sha256:ffb3f756b957c890371cc562999ecb314951e40ac7d7ca87d90bbd3fd621b446

Observation dbf4c8b7-f714-4c6f-a26b-2a8f31aea69f · inbound

Anchoring Refusal Direction: Mitigating Safety Risks in Tuning via Projection Constraint cites this paper.

Anchoring Refusal Direction: Mitigating Safety Risks in Tuning via Projection Constraint Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-04T23:10:21.190493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T23:10:21.190493Z digest=sha256:5fcd2bfb0214279419e9b00d32afb1232f8728afa391ac22bfd5b27709e1099e

Observation ef3557ae-8039-45ce-8b44-83a2979f2965 · inbound

MoGU V2: Toward a Higher Pareto Frontier Between Model Usability and Security cites this paper.

MoGU V2: Toward a Higher Pareto Frontier Between Model Usability and Security Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-04T23:09:41.491116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T23:09:41.491116Z digest=sha256:df84eaeb703ce702ccc379604ed46407d3ff3874612bff131de6e8b3092bf5a7

Observation 18bcd5c1-1aa6-4df8-a04d-8f3ed6d97f5a · inbound

LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems cites this paper.

LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models

Reference 124

Resolution
unresolved
no resolver link, observed 2026-08-04T17:46:19.311164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T17:46:19.311164Z digest=sha256:b8ffbce666b49c90acc769e104bf562bca74ed6fb814330427fe04e52a42aa53

Observation e7ccab32-557c-4d6b-ab7d-541fe2f73b7c · inbound

SafeAnchor: Preventing Cumulative Safety Erosion in Continual Domain Adaptation of Large Language Models cites this paper.

SafeAnchor: Preventing Cumulative Safety Erosion in Continual Domain Adaptation of Large Language Models Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-10T05:25:55.123958Z

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.

source=pdf_text observed=2026-05-10T05:23:15.746795Z digest=sha256:a37b332f235d269f064d2781f868362a7222a8ce3b5b779fac4392fc0b040f8c