Paper Citation Record · LEDGER

Robustifying Safety-Aligned Large Language Models through Clean Data Curation

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2405.19358.

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

pith.paper-citation-record.v1
2405.19358 v2

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 2026-08-18T06:34:40.430872+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:55:13.870946Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T05:39:40.646531Z

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 f0b51629-049c-469b-8238-798f25eb3fa2 · 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 Robustifying Safety-Aligned Large Language Models through Clean Data Curation

Reference 101

Resolution: verified exact; arxiv_id, observed 2026-05-23T20:58:25.882305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0220af5e-d502-47e3-8036-525afa5ab5d0 · inbound

SafeTuneBed: A Toolkit for Benchmarking LLM Safety Alignment in Fine-Tuning cites this paper.

SafeTuneBed: A Toolkit for Benchmarking LLM Safety Alignment in Fine-Tuning Robustifying Safety-Aligned Large Language Models through Clean Data Curation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:34.676929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4af70bc5-9a73-4257-b581-701f3804edc8 · inbound

LoX: Low-Rank Extrapolation Robustifies LLM Safety Against Fine-tuning cites this paper.

LoX: Low-Rank Extrapolation Robustifies LLM Safety Against Fine-tuning Robustifying Safety-Aligned Large Language Models through Clean Data Curation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:56.430016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 831d6b8d-5863-4549-91ae-16d888141391 · inbound

The Man Behind the Sound: Demystifying Audio Private Attribute Profiling via Multimodal Large Language Model Agents cites this paper.

The Man Behind the Sound: Demystifying Audio Private Attribute Profiling via Multimodal Large Language Model Agents Robustifying Safety-Aligned Large Language Models through Clean Data Curation

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T17:45:45.113679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28e14957-bee0-429e-8e76-69e286284121 · inbound

The Safety Gap Toolkit: Evaluating Hidden Dangers of Open-Source Models cites this paper.

The Safety Gap Toolkit: Evaluating Hidden Dangers of Open-Source Models Robustifying Safety-Aligned Large Language Models through Clean Data Curation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T19:08:24.224530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 218c90f4-0d96-4391-a0fd-c8f4b35fb6dd · inbound

SDD: Self-Degraded Defense against Malicious Fine-tuning cites this paper.

SDD: Self-Degraded Defense against Malicious Fine-tuning Robustifying Safety-Aligned Large Language Models through Clean Data Curation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T17:55:13.870946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2e0d114-69e5-4e94-89e2-62545587e11c · inbound

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses cites this paper.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Robustifying Safety-Aligned Large Language Models through Clean Data Curation

Reference 119

Resolution: unresolved; no resolver link, observed 2026-08-04T09:25:48.263901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb5d68f3-c45c-4d73-9413-449c7f55a1bc · inbound

One Step to the Side: Why Defenses Against Malicious Finetuning Fail Under Adaptive Adversaries cites this paper.

One Step to the Side: Why Defenses Against Malicious Finetuning Fail Under Adaptive Adversaries Robustifying Safety-Aligned Large Language Models through Clean Data Curation

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-06-30T21:05:04.077615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 452714d2-905f-4bb4-be5c-5b59976d985b · inbound

Aligned but Fragile: Enhancing LLM Safety Robustness via Zeroth-Order Optimization cites this paper.

Aligned but Fragile: Enhancing LLM Safety Robustness via Zeroth-Order Optimization Robustifying Safety-Aligned Large Language Models through Clean Data Curation

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-29T07:43:13.526324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1424f6a0-ea11-42df-8125-197cc394bd12 · inbound

Open Weight AI Models Require Proportional Evaluation Approaches cites this paper.

Open Weight AI Models Require Proportional Evaluation Approaches Robustifying Safety-Aligned Large Language Models through Clean Data Curation

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T05:39:40.648490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.