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AIArtificial Intelligence and Darkweb - Risks & Opportunities on large learning models

Artificial Intelligence and Darkweb – Risks & Opportunities on large learning models

The dark web consists of websites with hidden IP addresses, and small and big networks that are completely anonymous. Precisely, the dark web is only accessible through the use of encrypted browsers and special software. 

Activities on the dark web are risky and untraceable which makes it an attractive proposition for threat actors. It’s the home of different criminal activities that include cybercrime, human trafficking, and drug trafficking. In the recent past, significant cyber events such as the Silk Road Shutdown, AlphaBay Hack, and WannaCry ransomware attack demonstrated the ingenuity of cybercriminals in leveraging dark web resources. AI is increasingly becoming popular on the dark web and its growing use raises huge concerns. 

Artificial Intelligence (AI) and Machine Learning (ML) are becoming popular with each passing day. Several new large learning models (LLMs) are popping up each day. The most notable ones include ChatGPT, OpenAI, and Bard which are trained on text data. These advancements in artificial intelligence and machine learning are providing novel ways for businesses to keep their data safe and for criminals to continue mounting sophisticated attacks on organizations. Broadly speaking, AI can perform tasks that typically require a certain level of human intelligence. AI-based digital security stands to offer immense benefits for the cybersecurity industry. 

Potential Issues of AI Use on the Dark Web

AI is becoming an increasingly important underpinning technology to cybercriminals across the board. Malicious actors can leverage their ability to learn and anticipate what’s happening at the moment or in the future. For example, it is expected to underpin the development of tools that can map networks and identify weak spots in a security regime and then organize resources for an attack. 

AI comes into play since even highly sophisticated hacking tools require human-like intelligence to direct them against potential victims. Cybercriminals can remain undetected within an organization’s network for long durations allowing them to set up back doors to critical infrastructure, eavesdrop on meetings, extract data, set up privileged accounts, and launch attacks on the wider business. 

Some of the methods threat actors use AI include: 

  • Building better malware
  • Stealth attacks
  • Creating deep fake data
  • Deep exploits
  • Generative adversarial networks (GANs)
  • AI-supported password-guessing and CAPTCHA-cracking
  • ML-enabled penetration testing tools
  • Human impersonation on social networking platforms
  • Weaponizing AI frameworks to hack vulnerable hosts

So, what happens when LLMs are trained on the dark web? Threat actors are coming up with creative ways of exploiting the potential of generative AI. The dark web is filled with creative methods of exploiting powerful AI-based tools. The misuse of GPT-4 and ChatGPT shouldn’t be a laughing matter. Organizations and individuals must recognize the potential dangers posed by AI algorithms as they continue to evolve and gain increased prominence. 

For example, ChatGPT and generative AI can be used to create chatbots that scam people, steal personal information, and spread propaganda and misinformation. AI-based technologies are also used to create untraceable deepfakes for disinformation, to defame people or groups, and to extort and blackmail people. 

AI is expected to underpin the development of polymorphic malware. AI will change the current approach of using pre-coded algorithms to alter signatures of malware to avoid detection by anti-malware tools. AI-backed approaches can produce more than a million variations of a virus in a single day. The malware can also create new attack forms that specifically hit weak points that AI has identified.

Lastly, generative AI on the dark web can have unintended consequences that can be devastating. These technologies when fed on data can become highly biased and malicious due to faulty data and lack of stringent supervision. Unintended consequences may include the reinforcement of prejudices, the creation of harmful stereotypes, and the propagation of hate speech or disinformation.

Role of AI in Mitigating Dark Web Risks

Cybersecurity professionals and organizations need to stay vigilant and regularly adapt their strategies to stay ahead in the ever-changing threat landscape. AI cannot be a replacement for traditional security approaches. AI is a powerful tool that can strengthen cybersecurity initiatives and works best when used alongside traditional security methods. The major ways organizations and security teams are using AI to defend against threats include:

  • Behavioral analytics
  • Bot mitigation
  • Breach and attack simulation 
  • Compliance and privacy risk management
  • Data discovery and categorization
  • Fraud detection
  • Identity analytics and fraud detection
  • Policy automation
  • Security orchestration
  • Real-time threat and anomaly detection

Organizations and cybersecurity professionals must fight fire with fire to keep their networks and data safe. Embracing AI and its possibilities as well as evolving along with it will provide cybersecurity teams with a significant advantage over threat actors. They must leverage AI and ML to create leading cybersecurity solutions. AI combined with automated solutions can be used to search the dark web, detect illegal activity, and bring threat actors to justice. 

These technologies can be used for threat intelligence to analyze vast amounts of data from the dark web and identify patterns and trends in criminal activity. Once information is collected, it is used to inform law enforcement actions and develop highly effective cybersecurity measures.

LLMs are being incorporated into cyber threat intelligence to rapidly and accurately assess threats. Deployment of AI and ML-based systems such as security event management (SEM), security information and event management (SIEM), and security information management (SIM) ensures that security teams detect threats and respond to incidents faster and more effectively. 

Sentiment analysis through the use of AI tools can help identify potential threats by analyzing the language used in dark web forums and other online communities. Sentiment analysis picks out the tone and sentiment of ongoing discussions/threads. The collected information can then be used to inform law enforcement actions.


The combination of AI and well-crafted cybersecurity practices and zero trust is the best way to bolster an organization’s cybersecurity best practices and plans. Generative AI and LLM tools are evidence of human ingenuity and the massive potential of AI. It’s the collective responsibility of everybody to ensure that AI capabilities are used for the collective benefit rather than for nefarious reasons. 

Author: Alessandro Civati

Email: author.ac@bitstone.net

Blockchain ID: https://lrx.is/FcER4aSdbg

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