Paper Citation Record · LEDGER

Evaluating Large Language Models' Capability to Launch Fully Automated Spear Phishing Campaigns: Validated on Human Subjects

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2412.00586.

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

pith.paper-citation-record.v1
2412.00586 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:04:33.462290Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T20:03:43.949505Z

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 5c2bca68-90e1-41da-8702-9d652720202e · inbound

LLM Cyber Evaluations Don't Capture Real-World Risk cites this paper.

LLM Cyber Evaluations Don't Capture Real-World Risk Evaluating Large Language Models' Capability to Launch Fully Automated Spear Phishing Campaigns: Validated on Human Subjects

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T22:04:33.462290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f634637-86d3-4e83-a6a9-930c4db07724 · inbound

Can LLMs Hack Enterprise Networks? Autonomous Assumed Breach Penetration-Testing Active Directory Networks cites this paper.

Can LLMs Hack Enterprise Networks? Autonomous Assumed Breach Penetration-Testing Active Directory Networks Evaluating Large Language Models' Capability to Launch Fully Automated Spear Phishing Campaigns: Validated on Human Subjects

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-08T23:10:10.979778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8251340-9940-4529-9b26-d5f2fa42defb · inbound

Manipulation Attacks by Misaligned AI: Risk Analysis and Safety Case Framework cites this paper.

Manipulation Attacks by Misaligned AI: Risk Analysis and Safety Case Framework Evaluating Large Language Models' Capability to Launch Fully Automated Spear Phishing Campaigns: Validated on Human Subjects

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T16:39:35.146041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 163d4f56-c02a-4c61-818d-1f09ad3605c7 · inbound

Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning cites this paper.

Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning Evaluating Large Language Models' Capability to Launch Fully Automated Spear Phishing Campaigns: Validated on Human Subjects

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:41:50.480709Z

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.

Observation b6da78f3-ccca-4274-bc33-d8798e0f2f92 · inbound

SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing cites this paper.

SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Evaluating Large Language Models' Capability to Launch Fully Automated Spear Phishing Campaigns: Validated on Human Subjects

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-18T21:01:51.466133Z

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.

Observation 43eec7a8-46ca-4356-934c-5ccf06e4c888 · inbound

The End of Trust: How Agentic AI Breaks Security Assumptions cites this paper.

The End of Trust: How Agentic AI Breaks Security Assumptions Evaluating Large Language Models' Capability to Launch Fully Automated Spear Phishing Campaigns: Validated on Human Subjects

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:03:43.952566Z

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.