Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Fun-tuning: Characterizing the Vulnerability of Proprietary LLMs to Optimization-based Prompt Injection Attacks via the Fine-Tuning Interface
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2501.09798.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:27:03.532090Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T13:11:05.821591Z
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 30fba0b6-0662-48c2-9a97-08c7f135c1c9 · inbound
Security Concerns for Large Language Models: A Survey cites this paper.
Security Concerns for Large Language Models: A Survey Fun-tuning: Characterizing the Vulnerability of Proprietary LLMs to Optimization-based Prompt Injection Attacks via the Fine-Tuning Interface
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:27:03.532090Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b58182b9-d6e6-4d72-b42b-41026ce39bb6 · inbound
DART: Mitigating Harm Drift in Difference-Aware LLMs via Distill-Audit-Repair Training cites this paper.
DART: Mitigating Harm Drift in Difference-Aware LLMs via Distill-Audit-Repair Training Fun-tuning: Characterizing the Vulnerability of Proprietary LLMs to Optimization-based Prompt Injection Attacks via the Fine-Tuning Interface
Reference 10
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-10T07:26:59.924242Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 08f49dd7-b7c0-4594-b5ef-a7477a85f05f · inbound
An AI Agent Execution Environment to Safeguard User Data cites this paper.
An AI Agent Execution Environment to Safeguard User Data Fun-tuning: Characterizing the Vulnerability of Proprietary LLMs to Optimization-based Prompt Injection Attacks via the Fine-Tuning Interface
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T13:11:05.825940Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.