arXiv subjects

Jaron Mink

Publications and source records attributed to Jaron Mink.

9 recordsLinked to original sources

arXiv2026

"I Thought You Were The Uncensored Place": Norms, Rules, and Moderation in AI-Generated Sexual Content Communities

As AI-generated sexual content (AIG-SC) is increasingly produced, online communities have emerged to support creators' needs. To understand whether and how community governance attempts work to prevent abuse while supporting free expression, we interviewed 24 members and moderators of large AIG-SC online communities (10,000+ members) with stated rules against creating and sharing abusive content (e.g., AI-generated CSAM). Through in-depth interviews, we offer insight into: (1) how and why these communities form; (2) implicit community norms; (3) explicitly stated rules---and their operationalization via content moderation; and (4) tensions between community values and moderation that leave space for abusive behavior. Our findings reveal a complex picture: while many creators and communities have personal boundaries against abuse, advice and resources for creating any form of AI-generated sexual content are accessible to users regardless of their intentions. Further complicating community moderation are norms that center anti-censorship and non-judgment, which leave moderators to justify their actions using the limits of the law and terms of service. We end by reflecting on the ways in which technical, community, and legal governance may most effectively mitigate the production of abusive content.

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arXiv2026

Is This AI? Longitudinal Analysis of Strategies Used for AI Detection on Two Subreddits

As AI-generated content (e.g., "slop") becomes more prevalent online, people are developing strategies to attempt to identify it (or, conversely, to gain confidence that something is not AI-generated). What strategies are people using, and how are they changing over time as generative AI models themselves change? In this work, we catalog and analyze 2 years and 8 months of the AI detection strategies discussed by users of two popular Reddit communities (r/isthisAI and r/RealOrAI) that use the wisdom of crowds to identify AI-generated media. Through a mixed-method analysis of 13,098 posts and 222,060 comments within these communities, we catalog and analyze the prevalence of 12 AI-detection strategies, including examining fine-grained physical details, recognizing trends in AI-created content, and the assumptions people make about what models are capable of producing. Furthermore, we find that these strategies and mental models shift over time in accordance with changing AI capabilities and in response to online social trends. By systematically cataloging users' AI detection strategies, we lay the groundwork for user-facing guidance and future research.

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arXiv2026

Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit

Large language models (LLMs) have recently emerged as promising tools for augmenting Security Operations Center (SOC) workflows, with vendors increasingly marketing autonomous AI solutions for SOCs. However, there remains a limited empirical understanding of how such tools are used, perceived, and adopted by real-world security practitioners. To address this gap, we conduct a mixed-methods analysis of discussions in cybersecurity-focused forums to learn how a diverse group of practitioners use and perceive modern LLM tools for security operations. More specifically, we analyzed 892 posts between December 2022 and September 2025 from three cybersecurity-focused forums on Reddit, and, using a combination of qualitative coding and statistical analysis, examined how security practitioners discuss LLM tools across three dimensions: (1) their stated tools and use cases, (2) the perceived pros and cons of each tool across a set of critical factors, and (3) their adoption of such tools and the expected impacts on the cybersecurity industry and individual analysts. Overall, our findings reveal nuanced patterns in LLM tools adoption, highlighting independent use of LLMs for low-risk, productivity-oriented tasks, alongside active interest around enterprise-grade, security-focused LLM platforms. Although practitioners report meaningful gains in efficiency and effectiveness in LLM-assisted workflows, persistent issues with reliability, verification overheads, and security risks sharply constrain the autonomy granted to LLM tools. Based on these results, we also provide recommendations for developing and adopting LLM tools to ensure the security of organizations and the safety of cybersecurity practitioners.

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arXiv2026

Do Hackers Dream of Electric Teachers?: A Large-Scale, In-Situ Evaluation of Cybersecurity Student Behaviors and Performance with AI Tutors

To meet the ever-increasing demands of the cybersecurity workforce, AI tutors have been proposed for personalized, scalable education. But, while AI tutors have shown promise in introductory programming courses, no work has evaluated their use in hands-on exploration and exploitation of systems (e.g., ``capture-the-flag'') commonly used to teach cybersecurity. Thus, despite growing interest and need, no work has evaluated how students use AI tutors or whether they benefit from their presence in real, large-scale cybersecurity courses. To answer this, we conducted a semester-long observational study on the use of an embedded AI tutor with 309 students in an upper-division introductory cybersecurity course. By analyzing 142,526 student queries sent to the AI tutor across 396 cybersecurity challenges spanning 9 core cybersecurity topics and an accompanying set of post-semester surveys, we find (1) what queries and conversational strategies students use with AI tutors, (2) how these strategies correlate with challenge completion, and (3) students' perceptions of AI tutors in cybersecurity education. In particular, we identify three broad AI tutor conversational styles among users: Short (bounded, few-turn exchanges), Reactive (repeatedly submitting code and errors), and Proactive (driving problem-solving through targeted inquiry). We also find that the use of these styles significantly predicts challenge completion, and that this effect increases as materials become more advanced. Furthermore, students valued the tutor's availability but reported that it became less useful for harder material. Based on this, we provide suggestions for security educators and developers on practical AI tutor use.

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arXiv2026

Before the Vicious Cycle Starts: Preventing Burnout Across SOC Roles Through Flow-Aligned Design

The sustainability of Security Operations Centers depends on their people, yet 71% of practitioners report burnout and 24% plan to exit cybersecurity entirely. Flow theory suggests that when job demands misalign with practitioner capabilities, work becomes overwhelming or tedious rather than engaging. Achieving challenge-skill balance begins at hiring: if job descriptions inaccurately portray requirements, organizations risk recruiting underskilled practitioners who face anxiety or overskilled ones who experience boredom. Yet we lack empirical understanding of what current SOC job descriptions actually specify. We analyzed 106 public SOC job postings from November to December 2024 across 35 organizations in 11 countries, covering Analysts (n=17), Incident Responders (n=38), Threat Hunters (n=39), and SOC Managers (n=12). Using Inductive Content Analysis, we coded certifications, technical skills, soft skills, tasks, and experience requirements. Three patterns emerged: (1) Communication skills dominate (50.9% of postings), exceeding SIEM tools (18.9%) or programming (30.2%), suggesting organizations prioritize collaboration over technical capabilities. (2) Certification expectations vary widely: CISSP leads (22.6%), but 43 distinct credentials appear with no universal standard. (3) Technical requirements show consensus: Python dominates programming (27.4%), Splunk leads SIEM platforms (14.2%), and ISO 27001 (13.2%) and NIST (10.4%) are most cited standards. These findings enable organizations to audit job descriptions against empirical baselines, help practitioners identify valued certifications and skills, and allow researchers to validate whether stated requirements align with actual demands. This establishes the foundation for flow-aligned interview protocols and investigation of how AI reshapes requirements. Dataset and codebook: https://git.tu-berlin.de/wosoc-2026/soc-jd-analysis.

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arXiv2026

"Unlimited Realm of Exploration and Experimentation": Methods and Motivations of AI-Generated Sexual Content Creators

AI-generated media is radically changing the way content is both consumed and produced on the internet, and in no place is this potentially more visible than in sexual content. AI-generated sexual content (AIG-SC) is increasingly enabled by an ecosystem of individual AI developers, specialized third-party applications, and foundation model providers. AIG-SC raises a number of concerns from older debates about the line between pornography and obscenity to newer debates about fair use and labor displacement (in this case, of sex workers), and has spurred new regulations to curb the spread of non-consensual intimate imagery (NCII) created using the same technology used to create AIG-SC. However, despite the growing prevalence of AIG-SC, little is known about its creators, their motivations, and what types of content they produce. To inform effective governance in this space, we conducted an in-depth study to understand what AIG-SC creators make, along with how and why they make it. Interviews with 28 AIG-SC creators, ranging from hobbyists to entrepreneurs to those who moderate communities of hundreds of thousands of other creators, revealed a wide spectrum of motivations, including sexual exploration, creative expression, technical experimentation, and in a handful of cases, the creation of NCII.

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arXiv2025

Signals of Provenance: Practices & Challenges of Navigating Indicators in AI-Generated Media for Sighted and Blind Individuals

AI-Generated (AIG) content has become increasingly widespread by recent advances in generative models and the easy-to-use tools that have significantly lowered the technical barriers for producing highly realistic audio, images, and videos through simple natural language prompts. In response, platforms are adopting provable provenance with platforms recommending AIG to be self-disclosed and signaled to users. However, these indicators may be often missed, especially when they rely solely on visual cues and make them ineffective to users with different sensory abilities. To address the gap, we conducted semi-structured interviews (N=28) with 15 sighted and 13 BLV participants to examine their interaction with AIG content through self-disclosed AI indicators. Our findings reveal diverse mental models and practices, highlighting different strengths and weaknesses of content-based (e.g., title, description) and menu-aided (e.g., AI labels) indicators. While sighted participants leveraged visual and audio cues, BLV participants primarily relied on audio and existing assistive tools, limiting their ability to identify AIG. Across both groups, they frequently overlooked menu-aided indicators deployed by platforms and rather interacted with content-based indicators such as title and comments. We uncovered usability challenges stemming from inconsistent indicator placement, unclear metadata, and cognitive overload. These issues were especially critical for BLV individuals due to the insufficient accessibility of interface elements. We provide practical recommendations and design implications for future AIG indicators across several dimensions.

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arXiv2025

Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents

AI agents are beginning to interact with each other directly and across internet platforms and physical environments, creating security challenges beyond traditional cybersecurity and AI safety frameworks. Free-form protocols are essential for AI's task generalization but enable new threats like secret collusion and coordinated swarm attacks. Network effects can rapidly spread privacy breaches, disinformation, jailbreaks, and data poisoning, while multi-agent dispersion and stealth optimization help adversaries evade oversight - creating novel persistent threats at a systemic level. Despite their critical importance, these security challenges remain understudied, with research fragmented across disparate fields including AI security, multi-agent learning, complex systems, cybersecurity, game theory, distributed systems, and technical AI governance. We introduce multi-agent security, a new field dedicated to securing networks of AI agents against threats that emerge or amplify through their interactions - whether direct or indirect via shared environments - with each other, humans, and institutions, and characterise fundamental security-utility and security-security trade-offs across both distributed and decentralised settings. Our preliminary work (1) taxonomizes the threat landscape arising from interacting AI agents, (2) offers applications to multi-agent security for work across diffuse subfields, and (3) proposes a unified research agenda addressing open challenges in designing secure agent systems and interaction environments. By identifying these gaps, we aim to guide research in this critical area to unlock the socioeconomic potential of large-scale agent deployment, foster public trust, and mitigate national security risks in critical infrastructure and defense contexts.

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arXiv2024

SoK (or SoLK?): On the Quantitative Study of Sociodemographic Factors and Computer Security Behaviors

Researchers are increasingly exploring how gender, culture, and other sociodemographic factors correlate with user computer security and privacy behaviors. To more holistically understand relationships between these factors and behaviors, we make two contributions. First, we broadly survey existing scholarship on sociodemographics and secure behavior (151 papers) before conducting a focused literature review of 47 papers to synthesize what is currently known and identify open questions for future research. Second, by incorporating contemporary social and critical theories, we establish guidelines for future studies of sociodemographic factors and security behaviors that address how to overcome common pitfalls. We present a case study to demonstrate our guidelines in action, at-scale, that conduct a measurement study of the relationships between sociodemographics and de-identified, aggregated log data of security and privacy behaviors among 16,829 users on Facebook across 16 countries. Through these contributions, we position our work as a systemization of a lack of knowledge (SoLK). Overall, we find contradictory results and vast unknowns about how identity shapes security behavior. Through our guidelines and discussion, we chart new directions to more deeply examine how and why sociodemographic factors affect security behaviors.

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