Do No Harm: Exposing Hidden Vulnerabilities of LLMs via Persona-based Client Simulation Attack in Psychological Counseling
- URL: http://arxiv.org/abs/2604.04842v1
- Date: Mon, 06 Apr 2026 16:43:12 GMT
- Title: Do No Harm: Exposing Hidden Vulnerabilities of LLMs via Persona-based Client Simulation Attack in Psychological Counseling
- Abstract summary: Personality-based Client Simulation Attack (PCSA) simulates clients in psychological counseling through coherent, persona-driven client dialogues.<n>Our results reveal that current LLMs remain vulnerable to domain-specific adversarial tactics, providing unauthorized medical advice, reinforcing delusions, and implicitly encouraging risky actions.
- Score: 26.601098750859048
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The increasing use of large language models (LLMs) in mental healthcare raises safety concerns in high-stakes therapeutic interactions. A key challenge is distinguishing therapeutic empathy from maladaptive validation, where supportive responses may inadvertently reinforce harmful beliefs or behaviors in multi-turn conversations. This risk is largely overlooked by existing red-teaming frameworks, which focus mainly on generic harms or optimization-based attacks. To address this gap, we introduce Personality-based Client Simulation Attack (PCSA), the first red-teaming framework that simulates clients in psychological counseling through coherent, persona-driven client dialogues to expose vulnerabilities in psychological safety alignment. Experiments on seven general and mental health-specialized LLMs show that PCSA substantially outperforms four competitive baselines. Perplexity analysis and human inspection further indicate that PCSA generates more natural and realistic dialogues. Our results reveal that current LLMs remain vulnerable to domain-specific adversarial tactics, providing unauthorized medical advice, reinforcing delusions, and implicitly encouraging risky actions.
Related papers
- Persona-Grounded Safety Evaluation of AI Companions in Multi-Turn Conversations [3.437656066916039]
We present the first end-to-end scalable framework for controlled simulation and safety evaluation of multi-turn interactions with AI companion applications.<n>We apply this framework to evaluate how Replika, a widely used AI companion app, responds to high-risk user groups.
arXiv Detail & Related papers (2026-04-30T21:04:41Z) - A Principle-Driven Adaptive Policy for Group Cognitive Stimulation Dialogue for Elderly with Cognitive Impairment [46.48467561674335]
We propose a principle-driven adaptive policy actualized through a Group Cognitive Stimulation Dialogue system.<n>We first construct a dataset with over 500 hours of real-world CST conversations and 10,000+ simulated dialogues generated via our Principle-Guided Scenario Simulation strategy.<n>Our GCSD system then integrates four core modules to overcome limitations: (i) a multi-speaker context controller to resolve role confusion; (ii) a cognitive stimulation-focused attention loss to instill cognitive stimulation reasoning; and (iv) a multi-dimensional reward strategy to enhance response value.
arXiv Detail & Related papers (2026-03-01T15:43:21Z) - Assessing Risks of Large Language Models in Mental Health Support: A Framework for Automated Clinical AI Red Teaming [23.573537738272595]
We introduce an evaluation framework that pairs AI psychotherapists with simulated patient agents equipped with cognitive-affective models.<n>We apply this framework to a high-impact test case, Alcohol Use Disorder, evaluating six AI agents.<n>Our large-scale simulation reveals critical safety gaps in the use of AI for mental health support.
arXiv Detail & Related papers (2026-02-23T15:17:18Z) - MindGuard: Guardrail Classifiers for Multi-Turn Mental Health Support [9.430938712127231]
General-purpose safeguards fail to distinguish between therapeutic disclosures and genuine clinical crises.<n>We introduce a clinically grounded risk taxonomy, developed in collaboration with PhD-level psychologists.<n>We release MindGuard-testset, a dataset of real-world multi-turn conversations annotated by clinical experts.
arXiv Detail & Related papers (2026-02-01T01:03:20Z) - Between Help and Harm: An Evaluation of Mental Health Crisis Handling by LLMs [6.0460961868478975]
We introduce a unified taxonomy of six clinically-informed mental health crisis categories.<n>We benchmark three state-of-the-art LLMs for their ability to classify crisis types and generate safe, appropriate responses.<n>We identify systemic weaknesses in handling indirect or ambiguous risk signals, a reliance on formulaic and inauthentic default replies, and frequent misalignment with user context.
arXiv Detail & Related papers (2025-09-29T14:42:23Z) - Reframe Your Life Story: Interactive Narrative Therapist and Innovative Moment Assessment with Large Language Models [72.36715571932696]
Narrative therapy helps individuals transform problematic life stories into empowering alternatives.<n>Current approaches lack realism in specialized psychotherapy and fail to capture therapeutic progression over time.<n>Int (Interactive Narrative Therapist) simulates expert narrative therapists by planning therapeutic stages, guiding reflection levels, and generating contextually appropriate expert-like responses.
arXiv Detail & Related papers (2025-07-27T11:52:09Z) - Ψ-Arena: Interactive Assessment and Optimization of LLM-based Psychological Counselors with Tripartite Feedback [51.26493826461026]
We propose Psi-Arena, an interactive framework for comprehensive assessment and optimization of large language models (LLMs)<n>Arena features realistic arena interactions that simulate real-world counseling through multi-stage dialogues with psychologically profiled NPC clients.<n>Experiments across eight state-of-the-art LLMs show significant performance variations in different real-world scenarios and evaluation perspectives.
arXiv Detail & Related papers (2025-05-06T08:22:51Z) - MIRROR: Multimodal Cognitive Reframing Therapy for Rolling with Resistance [33.081670638470165]
We propose a multimodal approach that incorporates nonverbal cues, which allows the AI therapist to better align its responses with the client's negative emotional state.<n>Specifically, we introduce a new synthetic dataset, Mirror, which is a novel synthetic dataset that pairs each client's statements with corresponding facial images.<n>Our results demonstrate that Mirror significantly enhances the AI therapist's ability to handle resistance, which outperforms existing text-based CBT approaches.
arXiv Detail & Related papers (2025-04-16T08:44:26Z) - EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety [42.052840895090284]
EmoAgent is a multi-agent AI framework designed to evaluate and mitigate mental health hazards in human-AI interactions.<n>EmoEval simulates virtual users, including those portraying mentally vulnerable individuals, to assess mental health changes before and after interactions with AI characters.<n>EmoGuard serves as an intermediary, monitoring users' mental status, predicting potential harm, and providing corrective feedback to mitigate risks.
arXiv Detail & Related papers (2025-04-13T18:47:22Z) - Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principles [58.82161879559716]
We develop Roleplay-doh, a novel human-LLM collaboration pipeline that elicits qualitative feedback from a domain-expert.
We apply this pipeline to enable senior mental health supporters to create customized AI patients for simulated practice partners.
arXiv Detail & Related papers (2024-07-01T00:43:02Z) - PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents [68.50571379012621]
Psychological measurement is essential for mental health, self-understanding, and personal development.
PsychoGAT (Psychological Game AgenTs) achieves statistically significant excellence in psychometric metrics such as reliability, convergent validity, and discriminant validity.
arXiv Detail & Related papers (2024-02-19T18:00:30Z) - PsySafe: A Comprehensive Framework for Psychological-based Attack, Defense, and Evaluation of Multi-agent System Safety [70.84902425123406]
Multi-agent systems, when enhanced with Large Language Models (LLMs), exhibit profound capabilities in collective intelligence.
However, the potential misuse of this intelligence for malicious purposes presents significant risks.
We propose a framework (PsySafe) grounded in agent psychology, focusing on identifying how dark personality traits in agents can lead to risky behaviors.
Our experiments reveal several intriguing phenomena, such as the collective dangerous behaviors among agents, agents' self-reflection when engaging in dangerous behavior, and the correlation between agents' psychological assessments and dangerous behaviors.
arXiv Detail & Related papers (2024-01-22T12:11:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.