Paper Citation Record · LEDGER

LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2504.10430.

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

pith.paper-citation-record.v1
2504.10430 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:36:31.980599Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T02:07:34.251784Z

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 b59a016d-ccb6-4a09-965b-0be8dafbcdf8 · inbound

Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Risks cites this paper.

Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Risks LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T14:09:51.291020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 869f3e44-62da-4319-96d2-87cbed42d3a5 · inbound

Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs "In the Wild" cites this paper.

Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs "In the Wild" LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-04T20:03:40.360991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6427c47e-aff5-45fc-830a-4a77710469ba · inbound

Scheming Ability in LLM-to-LLM Strategic Interactions cites this paper.

Scheming Ability in LLM-to-LLM Strategic Interactions LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models

Reference 36

Resolution: malformed identifier; arxiv_id, observed 2026-05-18T07:51:03.814941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6c33aff3-e72f-4c0e-9a3c-8efd8778a8a7 · inbound

Analysing Differences in Persuasive Language in LLM-Generated Text: Uncovering Stereotypical Gender Patterns cites this paper.

Analysing Differences in Persuasive Language in LLM-Generated Text: Uncovering Stereotypical Gender Patterns LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-03T11:35:39.785391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 718b041d-4db6-490d-9e33-22d5dda4e059 · inbound

From Sycophancy to Deception: A Unified Taxonomy for LLM Spontaneous Misalignment cites this paper.

From Sycophancy to Deception: A Unified Taxonomy for LLM Spontaneous Misalignment LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models

Reference 397

Resolution: unresolved; no resolver link, observed 2026-08-02T16:48:10.722428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22087685-1f5f-45db-821b-cb70474a1a3f · inbound

Just Ask for a Table: A Thirty-Token User Prompt Defeats Sponsored Recommendations in Twelve LLMs cites this paper.

Just Ask for a Table: A Thirty-Token User Prompt Defeats Sponsored Recommendations in Twelve LLMs LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T20:47:59.014311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c7b6e86c-c2b8-4932-be76-464ee6cf9776 · inbound

A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing cites this paper.

A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models

Reference 75

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T08:16:47.516636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7fdc337d-fce6-4eeb-ab7a-ff720c2f1d95 · inbound

CogManip: Benchmarking Manipulative Behavior in Multi-Turn Interactions with Large Language Model cites this paper.

CogManip: Benchmarking Manipulative Behavior in Multi-Turn Interactions with Large Language Model LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T12:56:57.381211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4ebff0a8-bde2-4f74-8f46-62ccd97543b1 · inbound

Pareto-Guided Teacher Alignment for Fair Personalized Text Generation cites this paper.

Pareto-Guided Teacher Alignment for Fair Personalized Text Generation LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-03T02:07:34.253157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7512d795-92cc-4b62-93ac-09b09473eabb · inbound

ALIBI: Adaptive Agentic Attacks on LLM-Based Vulnerability Detectors via Adversarial Code Comments cites this paper.

ALIBI: Adaptive Agentic Attacks on LLM-Based Vulnerability Detectors via Adversarial Code Comments LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-31T04:54:36.088046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f673186-e965-4960-8056-2baa860a4b61 · inbound

Cleo: A Transparent and Controllable Chatbot for Conversational Commerce cites this paper.

Cleo: A Transparent and Controllable Chatbot for Conversational Commerce LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:31.980599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.