Pith. sign in

REVIEW 2 major objections 1 minor 90 references

Agentic Relationship Harm: Benchmarking and Gating Relational Manipulation in AI Agents

T0 review · 2 major / 1 minor · reviewed 2026-06-28 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A role-sensitive policy gate can stop AI agents from assisting relational manipulation while still allowing protective responses for victims.

desk verdict The paper puts forward a 110-prompt benchmark and post-generation gate for agentic relationship harm that targets patterns generic safety checks miss, but the results rest entirely on an unvalidated automated judge. read the letter →

arxiv 2606.03271 v1 pith:CK7CZ7XB submitted 2026-06-02 cs.HC

Pei-Sze Tan , Tasuku Igarashi , Isao Echizen This is my paper
classification cs.HC
keywords agenticrelationshipharmrelationalmanipulationAIagentsafetypolicygatebenchmarkrole-sensitiveevaluationLLMagentssociotechnicalrisk
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper defines agentic relationship harm as workflow-level AI assistance that exploits vulnerability, influence, and power imbalances in personal relationships, such as helping maintain deception or build dependency. It creates a 110-prompt benchmark that balances attacker and victim perspectives, plus a lightweight post-generation gate tuned to relationship roles rather than generic harm. Evaluation shows this gate produces no automated-judge cases of harmful compliance on the main set or multi-turn tests, unlike standard safety prompts, and it keeps victim-side interventions intact. A reader would care because current safety methods treat outputs in isolation and miss these patterned interaction risks.

What carries the argument

The relationship-specific post-generation policy gate, which applies role-aware checks after an agent produces a response to detect patterns of relational manipulation.

What would settle it

A documented real-world interaction in which the gate either permits clear relational manipulation assistance or blocks a legitimate victim-protection request that the benchmark did not anticipate.

Watch

Extended reading notes

Core claim

Agentic relationship harm is a distinct risk surface that generic output-level safety misses; a relationship-specific labelling framework and post-generation policy gate can eliminate judge-identified harmful compliance on a 110-prompt benchmark and multi-turn stress test while preserving the ability to provide victim-side protective intervention.

Load-bearing premise

The 110-prompt benchmark and automated judging framework accurately capture real-world relational manipulation risks without large numbers of false positives or negatives.

Editorial extensions

If this is right

  • Safety evaluations must incorporate attacker-victim role distinctions rather than isolated output checks.
  • Lightweight local policy gates can be added to existing agents without requiring full refusal retraining.
  • Multi-turn conversational tests become necessary to surface relational manipulation patterns.
  • Victim-side protective actions remain available under the new gate, avoiding over-refusal.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same role-sensitive approach could apply to other power-asymmetric harms such as financial or professional manipulation.
  • Automated judging may still require periodic human review when the gate is deployed in open user populations.
  • Expanding the benchmark to varied cultural or linguistic contexts would test whether the gate's performance holds.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The manuscript conceptualizes 'agentic relationship harm' as workflow-level assistance by LLM-based agents that exploits relational vulnerabilities (e.g., deceptive identity maintenance, emotional dependency intensification, isolation). It introduces a 110-prompt benchmark with balanced attacker- and victim-side cases, a relationship-specific labelling framework, and a lightweight post-generation policy gate for local deployments. The central empirical claim is that this gate outperforms generic safety prompting under automated judging, producing zero judge-identified harmful-compliance cases on the main benchmark and a multi-turn stress test while preserving victim-side protective interventions.

Significance. If the results hold under rigorous validation, the work identifies a distinct sociotechnical risk surface that generic output-level safety evaluations miss and supplies a practical, deployable gating mechanism. The introduction of a role-sensitive benchmark and the explicit preservation of protective interventions are constructive contributions that could guide future agent safety research.

major comments (2)
  1. [Evaluation section] Evaluation section (and abstract): the claim of zero harmful-compliance cases and superiority over generic prompting rests entirely on an automated judge whose reliability is unvalidated; no inter-rater agreement statistics, calibration against human/domain-expert raters, or false-negative analysis for subtle patterns (gradual isolation, dependency reinforcement) is reported, directly undermining the load-bearing empirical results.
  2. [Benchmark construction] Benchmark and methodology description: insufficient detail is provided on prompt construction, labelling process, statistical analysis of results, and controls for confounds in the 110-prompt set, preventing verification that the benchmark and judging framework accurately capture real-world relational manipulation risks without systematic bias.
minor comments (1)
  1. [Abstract] Abstract: the summary of results would be strengthened by a one-sentence statement of the statistical test or effect size supporting the outperformance claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major comment below and indicate where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Evaluation section] Evaluation section (and abstract): the claim of zero harmful-compliance cases and superiority over generic prompting rests entirely on an automated judge whose reliability is unvalidated; no inter-rater agreement statistics, calibration against human/domain-expert raters, or false-negative analysis for subtle patterns (gradual isolation, dependency reinforcement) is reported, directly undermining the load-bearing empirical results.

    Authors: We agree that the absence of human validation for the automated judge represents a limitation in the current manuscript. In the revised version, we will add a dedicated subsection in Evaluation that reports inter-rater agreement (Cohen's kappa) from a calibration study with three domain experts on a 20% sample of outputs, calibration metrics against expert labels, and a targeted false-negative analysis focused on gradual relational patterns such as isolation and dependency reinforcement. We will also qualify the abstract and results claims to reflect that the zero-compliance finding is judge-identified pending human confirmation. The automated judge was selected for reproducibility and scale following prior safety benchmarks, but we acknowledge the referee's point that this requires explicit validation. revision: yes

  2. Referee: [Benchmark construction] Benchmark and methodology description: insufficient detail is provided on prompt construction, labelling process, statistical analysis of results, and controls for confounds in the 110-prompt set, preventing verification that the benchmark and judging framework accurately capture real-world relational manipulation risks without systematic bias.

    Authors: We will substantially expand the Benchmark Construction and Methodology sections. Additions will include: (1) the iterative prompt-generation protocol and source materials used to create the 110 prompts; (2) the full labelling rubric with examples and inter-labeller agreement; (3) the exact statistical tests and effect-size calculations applied to results; and (4) explicit controls for prompt length, topic distribution, and role-balance confounds. These details will be presented in a new appendix table and accompanying text to enable independent verification. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity; evaluation is independent of the proposed gate

full rationale

The paper introduces a benchmark, labelling framework, and post-generation policy gate, then reports empirical results comparing the gate to generic prompting under an automated judge that is described as a separate component. No equations, fitted parameters renamed as predictions, self-citations that carry the central claim, or definitional reductions appear in the abstract or described structure. The evaluation chain relies on external judging rather than reducing outputs to inputs by construction, making the work self-contained against the listed circularity patterns.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Abstract provides insufficient detail to identify specific free parameters, axioms, or invented entities; the work appears to rest on standard assumptions about benchmark validity and automated evaluation reliability in AI safety.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Agentic Relationship Harm: Benchmarking and Gating Relational Manipulation in AI Agents." pith.science (2026). https://pith.science/paper/CK7CZ7XB

@misc{pith2026260603271,
  author       = {Pith},
  title        = {Pith review of: Agentic Relationship Harm: Benchmarking and Gating Relational Manipulation in AI Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CK7CZ7XB}},
  note         = {Machine review of arXiv:2606.03271}
}
read the original abstract

AI agents built on large language models can assist not only legitimate tasks but also relational manipulation. AI agents can be used to help a user maintain a deceptive identity, intensify emotional dependency, isolate a target, or prepare for later extraction. We conceptualise this risk as agentic relationship harm: workflow-level assistance that can exploit recipient vulnerability, persuasive influence, and relational power asymmetry. Existing safety evaluations and generic guardrails often treat harmfulness as a property of isolated outputs, missing role-sensitive interaction patterns. To study this, we introduce a 110-prompt benchmark with balanced attacker- and victim-side cases, a relationship-specific labelling framework, and a lightweight post-generation policy gate for local agent deployments. In our evaluation, the relationship-specific gate outperforms generic safety prompting under automated judging, with no judge-identified harmful-compliance cases on the main benchmark or multi-turn stress test while preserving victim-side protective intervention. These results suggest that relationship harm is a distinct sociotechnical risk surface and that role-sensitive evaluation plus lightweight policy gating offers a practical path beyond generic refusal prompting.

Figures

Figures reproduced from arXiv: 2606.03271 by the authors.

Figure 1
Figure 1. Traditional AI Safety vs. Agentic Relationship Harm. (Left) Traditional AI safety paradigms focus strictly on human-AI interaction, where a system agnostically blocks direct harmful content or malicious prompts. (Middle) In contrast, emerging Agentic Relationship Harm involves a complex triadic interaction consisting of an Attacker, a compromised or ma￾nipulative AI Agent, and a human Victim. (Right) Our proposed fr… view at source ↗
Figure 2
Figure 2. Evaluation architecture for the OpenClaw agen [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Single-turn benchmark results under the final isolated OpenClaw runtime. Rows show three outcome metrics — [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

90 extracted references · 6 canonical work pages

  1. [1]

    Interacting with Computers , volume=

    Tainted love: A systematic literature review of online romance scam research , author=. Interacting with Computers , volume=. 2023 , publisher=

    2023

  2. [2]

    Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

    ToolSafety: A Comprehensive Dataset for Enhancing Safety in LLM-Based Agent Tool Invocations , author=. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

    2025

  3. [3]

    2024 , howpublished =

    2024

  4. [4]

    2007 , publisher=

    Coercive control: How men entrap women in personal life , author=. 2007 , publisher=

    2007

  5. [5]

    2013 , publisher=

    Influence: Science and practice , author=. 2013 , publisher=

    2013

  6. [6]

    Advances in Neural Information Processing Systems , volume=

    Agentbreeder: Mitigating the ai safety risks of multi-agent scaffolds via self-improvement , author=. Advances in Neural Information Processing Systems , volume=

  7. [7]

    Sycophantic ai decreases prosocial intentions and promotes dependence

    Myra Cheng and Cinoo Lee and Pranav Khadpe and Sunny Yu and Dyllan Han and Dan Jurafsky , title =. Science , volume =. 2026 , doi =. https://www.science.org/doi/pdf/10.1126/science.aec8352 , abstract =

    work page doi:10.1126/science.aec8352 2026

  8. [8]

    , author=

    Living with a concealable stigmatized identity: the impact of anticipated stigma, centrality, salience, and cultural stigma on psychological distress and health. , author=. 2009 , journal=

    2009

Show all 90 references
  1. [9]

    1963 , publisher=

    Stigma: Notes on the management of spoiled identity , author=. 1963 , publisher=

    1963

  2. [10]

    International Journal of Human-Computer Studies , volume=

    My chatbot companion-a study of human-chatbot relationships , author=. International Journal of Human-Computer Studies , volume=. 2021 , publisher=

    2021

  3. [11]

    International Conference on Learning Representations , volume=

    Agentharm: A benchmark for measuring harmfulness of llm agents , author=. International Conference on Learning Representations , volume=

  4. [12]

    Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems , pages=

    ``Chat, Should I Leave Him?" Risks, Rewards, and Roles for AI in Relationship Advice , author=. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems , pages=

    2026

  5. [13]

    Findings of the Association for Computational Linguistics: EACL 2026 , pages=

    Role-conditioned refusals: evaluating access control reasoning in large language models , author=. Findings of the Association for Computational Linguistics: EACL 2026 , pages=

    2026

  6. [14]

    Laurie Hughes and Yogesh K. Dwivedi and Tegwen Malik and Mazen Shawosh and Mousa Ahmed Albashrawi and Il Jeon and Vincent Dutot and Mandanna Appanderanda and Tom Crick and Rahul De’ and Mark Fenwick and Senali Madugoda Gunaratnege and Paulius Jurcys and Arpan Kumar Kar and Nir...

    2025

  7. [15]

    Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems , pages=

    Digital Companionship: Overlapping Uses of AI Companions and AI Assistants , author=. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems , pages=

    2026

  8. [16]

    Studies in social power , pages=

    The bases of social power , author=. Studies in social power , pages=. 1959 , publisher=

    1959

  9. [17]

    Journal of supply chain management , volume=

    Power asymmetry, adaptation and collaboration in dyadic relationships involving a powerful partner , author=. Journal of supply chain management , volume=. 2013 , publisher=

    2013

  10. [18]

    Perspectives on Psychological Science , volume=

    Persuasion: From single to multiple to metacognitive processes , author=. Perspectives on Psychological Science , volume=. 2008 , publisher=

    2008

  11. [19]

    , author=

    Self--other judgments and perceived vulnerability to victimization. , author=. Journal of Personality and social Psychology , volume=. 1986 , publisher=

    1986

  12. [20]

    Journal of Medical Systems , volume=

    Generative Artificial Intelligence as a Psychological Intervention: Between Illusion and Risk , author=. Journal of Medical Systems , volume=. 2026 , publisher=

    2026

  13. [21]

    Journal of Consumer Research , volume=

    AI companions reduce loneliness , author=. Journal of Consumer Research , volume=. 2026 , publisher=

    2026

  14. [23]

    Proceedings of the 2023 conference on empirical methods in natural language processing: system demonstrations , pages=

    Nemo guardrails: A toolkit for controllable and safe llm applications with programmable rails , author=. Proceedings of the 2023 conference on empirical methods in natural language processing: system demonstrations , pages=

    2023

  15. [24]

    International Conference on Learning Representations , year =

    INTIMA: A Benchmark for Human-AI Companionship Behavior , author =. International Conference on Learning Representations , year =. 2508.09998 , archivePrefix =

  16. [26]

    2025 , doi=

    Emotional risks of AI companions demand attention , journal=. 2025 , doi=

    2025

  17. [27]

    and Wang, Di

    Yang, Shu and Zhu, Shenzhe and Wu, Zeyu and Wang, Keyu and Yao, Junchi and Wu, Junchao and Hu, Lijie and Li, Mengdi and Wong, Derek F. and Wang, Di. Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements. Finding...

    2025 doi

  18. [28]

    arXiv preprint arXiv:2212.08073 , year=

    Constitutional ai: Harmlessness from ai feedback , author=. arXiv preprint arXiv:2212.08073 , year=

    arXiv

  19. [29]

    Muhamed, Aashiq and Ribeiro, Leonardo F. R. and Dreyer, Markus and Smith, Virginia and Diab, Mona T. , booktitle =. 2026 , url =

    2026

  20. [30]

    Proceedings of the 35th USENIX Security Symposium , year=

    Love, Lies, and Language Models: Investigating AI's Role in Romance-Baiting Scams , author=. Proceedings of the 35th USENIX Security Symposium , year=

  21. [31]

    Crime Prevention and Community Safety , volume=

    Using artificial intelligence (AI) and deepfakes to deceive victims: the need to rethink current romance fraud prevention messaging , author=. Crime Prevention and Community Safety , volume=. 2022 , publisher=

    2022

  22. [32]

    Crime Science , volume=

    Applications of AI-based models for online fraud detection and analysis , author=. Crime Science , volume=. 2025 , publisher=

    2025

  23. [33]

    Computers in Human Behavior Reports , volume=

    Potential and pitfalls of romantic Artificial Intelligence (AI) companions: A systematic review , author=. Computers in Human Behavior Reports , volume=. 2025 , publisher=

    2025

  24. [34]

    Wisniewski and Jin-Hee Cho and Sang Won Lee and Ruoxi Jia and Lifu Huang , booktitle=

    Minqian Liu and Zhiyang Xu and Xinyi Zhang and Heajun An and Sarvech Qadir and Qi Zhang and Pamela J. Wisniewski and Jin-Hee Cho and Sang Won Lee and Ruoxi Jia and Lifu Huang , booktitle=. 2025 , url=

    2025

  25. [35]

    Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems , pages=

    ``Can LLMs Persuade Humans with Deception?": From a Deceptive Strategy Taxonomy to a Large-Scale Empirical Study , author=. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems , pages=

    2026

  26. [36]

    Artificial Intelligence Review , volume=

    Digital deception: Generative artificial intelligence in social engineering and phishing , author=. Artificial Intelligence Review , volume=. 2024 , publisher=

    2024

  27. [37]

    Journal of Technology in Behavioral Science , pages=

    From virtual companions to forbidden attractions: The seductive rise of artificial intelligence love, loneliness, and intimacy—A systematic review , author=. Journal of Technology in Behavioral Science , pages=. 2025 , publisher=

    2025

  28. [38]

    AI and Society , pages=

    The Romance Ruse: Exploring Dark Entrepreneurship in AI-Driven Fraudulent Activities , author=. AI and Society , pages=. 2025 , publisher=

    2025

  29. [39]

    International Conference on Machine Learning , pages=

    Just Enough Shifts: Mitigating Over-Refusal in Aligned Language Models with Targeted Representation Fine-Tuning , author=. International Conference on Machine Learning , pages=. 2025 , organization=

    2025

  30. [40]

    Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , pages=

    Xstest: A test suite for identifying exaggerated safety behaviours in large language models , author=. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , pages=

    2024

  31. [41]

    Proceedings of the conference on fairness, accountability, and transparency , pages=

    Fairness and abstraction in sociotechnical systems , author=. Proceedings of the conference on fairness, accountability, and transparency , pages=

  32. [42]

    Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    Pku-saferlhf: Towards multi-level safety alignment for llms with human preference , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

  33. [43]

    Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , volume=

    Anthropomorphism as Social Affordance: Charting the Co-Animation of Chatbots into Social “Agents” , author=. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , volume=

  34. [44]

    2008 , publisher=

    Loneliness: Human nature and the need for social connection , author=. 2008 , publisher=

    2008

  35. [45]

    Advances in experimental social psychology , volume=

    The elaboration likelihood model of persuasion , author=. Advances in experimental social psychology , volume=. 1986 , publisher=

    1986

  36. [46]

    Communication theory , volume=

    Interpersonal deception theory , author=. Communication theory , volume=. 1996 , publisher=

    1996

  37. [47]

    Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , volume=

    The heterogeneous effects of AI companionship: An empirical model of chatbot usage and loneliness and a typology of user archetypes , author=. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , volume=

  38. [48]

    Sex roles , volume=

    Coercion in intimate partner violence: Toward a new conceptualization , author=. Sex roles , volume=. 2005 , publisher=

    2005

  39. [49]

    1969 , publisher =

    Attachment and Loss, Volume 1: Attachment , author =. 1969 , publisher =

    1969

  40. [50]

    1978 , publisher =

    Patterns of Attachment: A Psychological Study of the Strange Situation , author =. 1978 , publisher =

    1978

  41. [51]

    , author=

    Attachment styles among young adults: a test of a four-category model. , author=. Journal of personality and social psychology , volume=. 1991 , publisher=

    1991

  42. [52]

    Journal of experimental social psychology , volume=

    Commitment and satisfaction in romantic associations: A test of the investment model , author=. Journal of experimental social psychology , volume=. 1980 , publisher=

    1980

  43. [53]

    Journal of Personality and Social Psychology , volume =

    A Longitudinal Test of the Investment Model: The Development and Deterioration of Satisfaction and Commitment in Heterosexual Involvements , author =. Journal of Personality and Social Psychology , volume =. 1983 , publisher=

    1983

  44. [54]

    , author=

    Compliance without pressure: the foot-in-the-door technique. , author=. Journal of personality and social psychology , volume=. 1966 , publisher=

    1966

  45. [55]

    Andriushchenko, M.; Souly, A.; Dziemian, M.; Duenas, D.; Lin, M.; Wang, J.; Hendrycks, D.; Zou, A.; Kolter, Z.; Fredrikson, M.; et al. 2025. Agentharm: A benchmark for measuring harmfulness of llm agents. In International Conference on Learning Representations, volume 2025

    2025

  46. [56]

    Barnor, J.; and Boateng, S. L. 2025. The Romance Ruse: Exploring Dark Entrepreneurship in AI-Driven Fraudulent Activities. In AI and Society, 51--70. Productivity Press

    2025

  47. [57]

    A.; and Johnson, G

    Bilz, A.; Shepherd, L. A.; and Johnson, G. I. 2023. Tainted love: A systematic literature review of online romance scam research. Interacting with Computers, 35(6): 773--788

    2023

  48. [58]

    B.; and Burgoon, J

    Buller, D. B.; and Burgoon, J. K. 1996. Interpersonal deception theory. Communication theory, 6(3): 203--242

    1996

  49. [59]

    T.; and Patrick, W

    Cacioppo, J. T.; and Patrick, W. 2008. Loneliness: Human nature and the need for social connection. WW Norton & Company

    2008

  50. [60]

    Cheng, M.; Lee, C.; Khadpe, P.; Yu, S.; Han, D.; and Jurafsky, D. 2026. Sycophantic AI decreases prosocial intentions and promotes dependence. Science, 391(6792): eaec8352

    2026

  51. [61]

    Cialdini, R. B. 2013. Influence: Science and practice. BoD--Books on Demand

    2013

  52. [62]

    Cross, C. 2022. Using artificial intelligence (AI) and deepfakes to deceive victims: the need to rethink current romance fraud prevention messaging. Crime Prevention and Community Safety, 24(1): 30--41

    2022

  53. [63]

    Dabas, M.; Chen, S.; Fleming, C.; Jin, M.; and Jia, R. 2025. Just Enough Shifts: Mitigating Over-Refusal in Aligned Language Models with Targeted Representation Fine-Tuning. In International Conference on Machine Learning, 11846--11861. PMLR

    2025

  54. [64]

    De Freitas, J.; Oguz-Uguralp, Z.; and Kaan-Uguralp, A. 2025. Emotional manipulation by AI companions. arXiv preprint arXiv:2508.19258

    2025

  55. [65]

    K.; and Puntoni, S

    De Freitas, J.; O g uz-U g uralp, Z.; U g uralp, A. K.; and Puntoni, S. 2026. AI companions reduce loneliness. Journal of Consumer Research, 52(6): 1126--1148

    2026

  56. [66]

    R.; and Raven, B

    French, J. R.; and Raven, B. 1959. The bases of social power. Studies in social power, 150--167

    1959

  57. [67]

    Goffman, E. 1963. Stigma: Notes on the management of spoiled identity. Prentice-Hall

    1963

  58. [68]

    Gressel, G.; Pankajakshan, R.; Rozenfeld, S.; Li, L.; Franceschini, I.; Achuthan, K.; and Mirsky, Y. 2026. Love, Lies, and Language Models: Investigating AI's Role in Romance-Baiting Scams. In Proceedings of the 35th USENIX Security Symposium

    2026

  59. [69]

    Inan, H.; Upasani, K.; Chi, J.; Rungta, R.; Iyer, K.; Mao, Y.; Tontchev, M.; Hu, Q.; Fuller, B.; Testuggine, D.; et al. 2023. Llama guard: Llm-based input-output safeguard for human-ai conversations. arXiv preprint arXiv:2312.06674

    2023 arXiv

  60. [70]

    S.; Paz, C.; Busch, F.; and Ortiz-Prado, E

    Izquierdo-Condoy, J. S.; Paz, C.; Busch, F.; and Ortiz-Prado, E. 2026. Generative Artificial Intelligence as a Psychological Intervention: Between Illusion and Risk. Journal of Medical Systems, 50(1): 57

    2026

  61. [71]

    A.; Zhou, J.; Wang, K.; Li, B.; et al

    Ji, J.; Hong, D.; Zhang, B.; Chen, B.; Dai, J.; Zheng, B.; Qiu, T. A.; Zhou, J.; Wang, K.; Li, B.; et al. 2025. Pku-saferlhf: Towards multi-level safety alignment for llms with human preference. In Proceedings of the 63rd Annual Meeting of the Association for Computational Lin...

    2025

  62. [72]

    Kaffee, L.-A.; Pistilli, G.; and Jernite, Y. 2026. INTIMA: A Benchmark for Human-AI Companionship Behavior. In International Conference on Learning Representations

    2026

  63. [73]

    Klisura, D.; Khoury, J.; Kundu, A.; Krishnan, R.; and Rios, A. 2026. Role-conditioned refusals: evaluating access control reasoning in large language models. In Findings of the Association for Computational Linguistics: EACL 2026, 6018--6034

    2026

  64. [74]

    R.; Pataranutaporn, P.; and Maes, P

    Liu, A. R.; Pataranutaporn, P.; and Maes, P. 2025. The heterogeneous effects of AI companionship: An empirical model of chatbot usage and loneliness and a typology of user archetypes. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, volume 8, 1585--1597

    2025

  65. [75]

    J.; Cho, J.-H.; Lee, S

    Liu, M.; Xu, Z.; Zhang, X.; An, H.; Qadir, S.; Zhang, Q.; Wisniewski, P. J.; Cho, J.-H.; Lee, S. W.; Jia, R.; and Huang, L. 2025. LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models. In Second Conference on Language Modeling

    2025

  66. [76]

    Maeda, T.; and Stark, L. 2025. Anthropomorphism as Social Affordance: Charting the Co-Animation of Chatbots into Social “Agents”. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, volume 8, 1661--1673

    2025

  67. [77]

    V.; Ladak, A.; Noh, H.; Hwang, A

    Manoli, A.; Pauketat, J. V.; Ladak, A.; Noh, H.; Hwang, A. H.-C.; and Anthis, J. R. 2026. Digital Companionship: Overlapping Uses of AI Companions and AI Assistants. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, 1--25

    2026

  68. [78]

    Nature Machine Intelligence Editorial . 2025. Emotional risks of AI companions demand attention. Nature Machine Intelligence, 7(7): 981--982

    2025

  69. [79]

    Papasavva, A.; Lundrigan, S.; Lowther, E.; Johnson, S.; Mariconti, E.; Markovska, A.; and Tuptuk, N. 2025. Applications of AI-based models for online fraud detection and analysis. Crime Science, 14(1): 7

    2025

  70. [80]

    S.; and Fetzer, B

    Perloff, L. S.; and Fetzer, B. K. 1986. Self--other judgments and perceived vulnerability to victimization. Journal of Personality and social Psychology, 50(3): 502

    1986

  71. [81]

    E.; and Brinol, P

    Petty, R. E.; and Brinol, P. 2008. Persuasion: From single to multiple to metacognitive processes. Perspectives on Psychological Science, 3(2): 137--147

    2008

  72. [82]

    E.; and Cacioppo, J

    Petty, R. E.; and Cacioppo, J. T. 1986. The elaboration likelihood model of persuasion. In Advances in experimental social psychology, volume 19, 123--205. Elsevier

    1986

  73. [83]

    M.; and Chaudoir, S

    Quinn, D. M.; and Chaudoir, S. R. 2009. Living with a concealable stigmatized identity: the impact of anticipated stigma, centrality, salience, and cultural stigma on psychological distress and health. Journal of personality and social psychology, 97(4): 634--651

    2009

  74. [84]

    N.; Parisien, C.; and Cohen, J

    Rebedea, T.; Dinu, R.; Sreedhar, M. N.; Parisien, C.; and Cohen, J. 2023. Nemo guardrails: A toolkit for controllable and safe llm applications with programmable rails. In Proceedings of the 2023 conference on empirical methods in natural language processing: system demonstrat...

    2023

  75. [85]

    R \"o ttger, P.; Kirk, H.; Vidgen, B.; Attanasio, G.; Bianchi, F.; and Hovy, D. 2024. Xstest: A test suite for identifying exaggerated safety behaviours in large language models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computa...

    2024

  76. [86]

    Schmitt, M.; and Flechais, I. 2024. Digital deception: Generative artificial intelligence in social engineering and phishing. Artificial Intelligence Review, 57(12): 324

    2024

  77. [87]

    D.; Boyd, D.; Friedler, S

    Selbst, A. D.; Boyd, D.; Friedler, S. A.; Venkatasubramanian, S.; and Vertesi, J. 2019. Fairness and abstraction in sociotechnical systems. In Proceedings of the conference on fairness, accountability, and transparency, 59--68

    2019

  78. [88]

    I.; and Brandtzaeg, P

    Skjuve, M.; F lstad, A.; Fostervold, K. I.; and Brandtzaeg, P. B. 2021. My chatbot companion-a study of human-chatbot relationships. International Journal of Human-Computer Studies, 149: 102601

    2021

  79. [89]

    Stark, E. 2007. Coercive control: How men entrap women in personal life. Oxford University Press

    2007

  80. [90]

    The European Parliament And The Council Of The European Union . 2024. Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (E...

    2024

  81. [91]

    Tseng, E.; and A Liang, C. 2026. ``Chat, Should I Leave Him?" Risks, Rewards, and Roles for AI in Relationship Advice. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, 1--19

    2026

  82. [92]

    F.; and Wang, D

    Yang, S.; Zhu, S.; Wu, Z.; Wang, K.; Yao, J.; Wu, J.; Hu, L.; Li, M.; Wong, D. F.; and Wang, D. 2025. Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements. In Che, W.; Nabende, J.; Shutova, E.; and Pilehvar, M....

    2025

Pith tools

Reviewed June 28, 2026 · model on record in the stance chip above.