Beyond the hype: The top 7 cybersecurity risks in GenAI and how to mitigate these

Generative AI (GenAI) including Large Language Models (LLMs) are rapidly transforming industries from automating tasks to powering creative content generation.

But before you deploy your own GenAI model, it’s critical to address cybersecurity risk. Like any powerful solution, GenAI can be misused and requires caution and responsible use. Today, only 38% of organizations actively address cybersecurity risks associated with LLMs. That is why organizations embracing GenAI must prioritize secure adoption to navigate these challenges and ensure responsible, ethical advancement.

So, what are the main generative AI risks to watch out for and how can you steer clear of these? Let’s take a look.

Major LLM risks in the GenAI threat landscape 

• Prompt injection: This LLM top threat allows an attacker to craft an input to a GenAI application that, on the surface, seems harmless, but subtly tricks it into revealing sensitive information or performing unauthorized actions. This is called prompt injection, and it’s a worrying possibility as malicious actors can exploit the nuances of language to manipulate even sophisticated LLMs.
• Insecure output handling: Consider feeding unfiltered LLM outputs directly into websites or applications. Without proper validation, attackers can exploit this by feeding the LLM malicious instructions disguised as normal requests, potentially causing harm like stealing data or manipulating systems.
• Training data poisoning: Malicious actors can feed deliberately biased or malicious data to an LLM during the training phase. This can lead to the model inheriting that bias or becoming susceptible to specific attacks through backdoors embedded in the data. Malicious actors exploiting spam filters by training them on spam emails is a real-world example.
• Supply chain vulnerabilities: Any weak link in the LLM’s development chain, from training data to the model itself to the platform it runs on, can be exploited. Hackers could manipulate open-source models to inject vulnerabilities into software libraries used by LLMs.
• Insecure plugin design: Third-party plugins for LLMs can harbor vulnerabilities, enabling data exfiltration, remote code execution, or privilege escalation. Researchers have demonstrated how insecure ChatGPT plugins allowed unauthorized access to private data.
• Over-reliance and model theft: Extreme dependence on LLMs without a proper oversight can lead to misinformation, legal issues, and security vulnerabilities. One such example can be a lawyer who relied on ChatGPT for research submitted and fabricated cases, highlighting the dangers of blind trust.
• Model theft: Unauthorized access and copying of proprietary LLM models, like the leak of Meta’s LLaMA, can damage an organization’s reputation and competitive edge.

Navigating the GenAI adoption path securely

The good news is proactive measures can mitigate these risks and ensure secure GenAI adoption. Here are key steps that your enterprise can follow to sidestep AI risks and ensure a secure GenAI adoption:

1. Build a secure foundation.
   Establish a strong foundation by adopting established frameworks like Google’s Secure AI Framework (SAIF)and NIST’s AI Risk Management Framework. These frameworks provide comprehensive guidance on securing AI systems throughout their lifecycle.
2. Mitigate LLM-specific threats.
   • Conduct thorough threat modeling exercises to identify and address potential vulnerabilities specific to LLM applications. This includes focusing on risks like training data poisoning, prompt injection, and model theft.
   • Implement robust data validation measures for both model parameters and input/output data. This helps prevent malicious manipulation and ensures data integrity throughout the training and usage process.
   • Enforce the principle of least privilege to restrict access to models and prompts. This minimizes the attack surface and reduces the potential for unauthorized use or manipulation.
3. Integrate with existing security practices.
   Effectively integrate GenAI security controls into your existing application security programs. This ensures comprehensive protection and avoids creating isolated security silos. 
4. Stay informed and adapt.
   The GenAI landscape is constantly evolving. Stay updated on evolving threats and best practices by referencing industry resources like the OWASP Top 10 for LLM. This ensures your organization remains informed and adapts its security posture regularly accordingly.

Building a secure future with GenAI

By following these guidelines and leveraging available resources, organizations can embrace the transformative power of GenAI while ensuring responsible and secure implementation. Remember, prioritizing security from the outset is crucial for maximizing the benefits of GenAI while minimizing potential risks.

This article offers an overview of secure GenAI adoption and emerging threats. For a deep dive into the technical details and industry best practices around GenAI adoption and emerging threats, please refer to this article available on our Digital Security Magazine.

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