Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models
As of 14 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:36:31.980599Z
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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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.