Stand-Alone Complex or Vibercrime? Exploring the adoption and innovation of GenAI tools, coding assistants, and agents within cybercrime ecosystems
- URL: http://arxiv.org/abs/2603.29545v1
- Date: Tue, 31 Mar 2026 10:28:33 GMT
- Title: Stand-Alone Complex or Vibercrime? Exploring the adoption and innovation of GenAI tools, coding assistants, and agents within cybercrime ecosystems
- Abstract summary: We argue that the real threats posed by generative AI for cybercrime are rather different.<n>At the high end, we propose the Stand-Alone Complex, in which cybercrime-gang-in-a-box solutions enable individual actors to largely automate existing cybercrime-as-a-service arrangements.<n>At the low end, we suggest the phenomenon of Vibercrime, in which 'vibe coding' lowers the barrier to entry, but do not fundamentally reshape the economic structures of cybercrime.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Existential risk scenarios relating to Generative Artificial Intelligence often involve advanced systems or agentic models breaking loose and using hacking tools to gain control over critical infrastructure. In this paper, we argue that the real threats posed by generative AI for cybercrime are rather different. We apply innovation theory and evolutionary economics - treating cybercrime as an ecosystem of small- and medium-scale tech start-ups, coining two novel terms that bound the upper and lower cases for disruption. At the high end, we propose the Stand-Alone Complex, in which cybercrime-gang-in-a-box solutions enable individual actors to largely automate existing cybercrime-as-a-service arrangements. At the low end, we suggest the phenomenon of Vibercrime, in which 'vibe coding' lowers the barrier to entry, but do not fundamentally reshape the economic structures of cybercrime. We analyse early empirical data from the cybercrime underground, and find the reality is prosaic - AI has some early adoption in existing large-scale, low-profit passive income schemes and trivial forms of fraud but there is little evidence so far on widespread disruption in cybercrime. This replaces existing means of code pasting, error checking, and cheatsheet consultation, for generic aspects of software development involved in cybercrime - and largely for already skilled actors, with low-skill actors finding little utility in vibe coding tools compared to pre-made scripts. The role of jailbroken LLMs (Dark AI) as instructors is also overstated, given the prominence of subculture and social learning in initiation - new users value the social connections and community identity involved in learning hacking and cybercrime skills as much as the knowledge itself. Our initial results, therefore, suggest that even bemoaning the rise of the Vibercriminal may be overstating the level of disruption to date.
Related papers
- Hybrid CNN-LSTM Framework for Intelligent Cyber Attack Detection and Prevention in U.S. Critical Digital Infrastructure: A Comparative Machine Learning Evaluation on CSE-CIC-IDS2018 [0.8496907566303933]
This research suggests a smart cyber-defense system, which utilizes Artificial Intelligence (AI) and Machine Learning (ML) algorithms.<n>The framework proposed combines data preprocessing, feature engineering, real-time traffic monitoring, intelligent threat classification with automated prevention mechanisms.
arXiv Detail & Related papers (2026-06-04T05:14:01Z) - CyberGym-E2E: Scalable Real-World Benchmark for AI Agents' End-to-End Cybersecurity Capabilities [66.48570184296857]
CyberGym-E2E is a large-scale and realistic end-to-end cybersecurity benchmark.<n>It comprehensively evaluates AI agents' abilities across the full lifecycle of vulnerability discovery, PoC generation, and patch generation.<n>The benchmark consists of 920 real-world vulnerabilities across 139 different open-source projects.
arXiv Detail & Related papers (2026-06-03T05:06:37Z) - Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw [42.9467373594646]
This report presents a comprehensive security analysis of the OpenClaw ecosystem.<n>We highlight critical vulnerabilities such as prompt injection-driven Remote Code Execution (RCE), sequential tool attack chains, context amnesia, and supply chain contamination.<n>To address these systemic architectural flaws, we introduce the Full-Lifecycle Agent Security Architecture (FASA)<n>This theoretical defense blueprint advocates for zero-trust agentic execution, dynamic intent verification, and cross-layer reasoning-action correlation.
arXiv Detail & Related papers (2026-03-13T04:33:05Z) - What hackers talk about when they talk about AI: Early-stage diffusion of a cybercrime innovation [0.0]
The rapid expansion of artificial intelligence (AI) is raising concerns about its potential to transform cybercrime.<n>This paper examines the evolving relationship between cybercriminals and AI using a unique dataset from a cyber threat intelligence platform.
arXiv Detail & Related papers (2026-02-16T14:31:33Z) - PACEbench: A Framework for Evaluating Practical AI Cyber-Exploitation Capabilities [42.61805002268063]
We introduce PACEbench, a practical AI cyber-exploitation benchmark.<n>PACEbench comprises four scenarios spanning single, blended, chained, and defense vulnerability exploitations.<n>We propose PACEagent, a novel agent that emulates human penetration testers by supporting multi-phase reconnaissance, analysis, and exploitation.
arXiv Detail & Related papers (2025-10-13T17:50:25Z) - CyFence: Securing Cyber-Physical Controllers via Trusted Execution Environment [45.86654759872101]
Cyber-physical systems (CPSs) have experienced a significant technological evolution and increased connectivity, at the cost of greater exposure to cyber-attacks.<n>We propose CyFence, a novel architecture that improves the resilience of closed-loop control systems against cyber-attacks by adding a semantic check.<n>We evaluate CyFence considering a real-world application, consisting of an active braking digital controller, demonstrating that it can mitigate different types of attacks with a negligible overhead.
arXiv Detail & Related papers (2025-06-12T12:22:45Z) - Automated Classification of Cybercrime Complaints using Transformer-based Language Models for Hinglish Texts [3.548191850334861]
We propose a framework for automated cybercrime complaint classification.<n>We employ the real-world dataset provided by Indian Cybercrime Coordination Centre (I4C) during CyberGuard AI Hackathon 2024.<n>Our solution achieves significant performance improvements, with HingRoBERTa attaining an accuracy of 74.41% and an F1-score of 71.49%.<n>This work bridges critical gaps in cybercrime complaint management, offering a scalable, privacy-conscious, and adaptable solution for modern cybersecurity challenges.
arXiv Detail & Related papers (2024-12-21T13:17:09Z) - Is Generative AI the Next Tactical Cyber Weapon For Threat Actors? Unforeseen Implications of AI Generated Cyber Attacks [0.0]
This paper delves into the escalating threat posed by the misuse of AI, specifically through the use of Large Language Models (LLMs)
Through a series of controlled experiments, the paper demonstrates how these models can be manipulated to bypass ethical and privacy safeguards to effectively generate cyber attacks.
We also introduce Occupy AI, a customized, finetuned LLM specifically engineered to automate and execute cyberattacks.
arXiv Detail & Related papers (2024-08-23T02:56:13Z) - Graph Mining for Cybersecurity: A Survey [61.505995908021525]
The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society.
Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities.
With the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
arXiv Detail & Related papers (2023-04-02T08:43:03Z) - Towards Effective Cybercrime Intervention [1.179179628317559]
We propose to build a systematic framework through the lens of a cyber threat actor.
We explore the motivation factors behind the crimes and the crime stages of the threat actors.
We then formulate intervention plans so as to discourage the act of committing malicious cyber activities and also aim to integrate ex-cyber offenders back into society.
arXiv Detail & Related papers (2022-11-17T13:40:53Z) - Proceedings of the Artificial Intelligence for Cyber Security (AICS)
Workshop at AAAI 2022 [55.573187938617636]
The workshop will focus on the application of AI to problems in cyber security.
Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities.
arXiv Detail & Related papers (2022-02-28T18:27:41Z) - Artificial Intelligence-Based Smart Grid Vulnerabilities and Potential
Solutions for Fake-Normal Attacks: A Short Review [0.0]
Smart grid systems are critical to the power industry, however their sophisticated architectural design and operations expose them to a number of cybersecurity threats.
Artificial Intelligence (AI)-based technologies are becoming increasingly popular for detecting cyber assaults in a variety of computer settings.
The present AI systems are being exposed and vanquished because of the recent emergence of sophisticated adversarial systems such as Generative Adversarial Networks (GAN)
arXiv Detail & Related papers (2022-02-14T21:41:36Z)
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.