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Paper Citation Record · LEDGER

Building Safe GenAI Applications: An End-to-End Overview of Red Teaming for Large Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.01742.

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

pith.paper-citation-record.v1
2503.01742 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T06:53:48.841077Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T14:13:30.532224Z

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 f40a056d-35c4-456f-a6eb-3e87dfafb64e · inbound

DoubleAgents: Human-Agent Alignment in a Socially Embedded Workflow cites this paper.

DoubleAgents: Human-Agent Alignment in a Socially Embedded Workflow Building Safe GenAI Applications: An End-to-End Overview of Red Teaming for Large Language Models

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-18T17:11:40.326935Z

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=2026-05-18T17:09:02.466082Z digest=sha256:119df1954a1fa2e36b7e489372f60d1f053c4ae029835dbad8fc6658681fe000

Observation d3dc09c5-4620-4db3-8127-17a20a2f40a6 · inbound

How Reliable Are AI Attackers Against a Fixed Vulnerable Target? A 400-Run Empirical Study of LLM Penetration Testing Consistency cites this paper.

How Reliable Are AI Attackers Against a Fixed Vulnerable Target? A 400-Run Empirical Study of LLM Penetration Testing Consistency Building Safe GenAI Applications: An End-to-End Overview of Red Teaming for Large Language Models

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-06-29T14:13:30.533701Z

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=2026-06-29T06:53:48.841077Z digest=sha256:b97019a88b1ddd67b26c988e6481a33ebc0d8df7c77e3e0d462f77634fd434ba