In the rapidly evolving landscape of AI, Large Language Models (LLMs) have emerged as powerful tools, offering unprecedented capabilities in natural language processing. However, with great power comes great responsibility.


Copyright: Igor van Gemert – “Attention CISOs: The Hidden Dangers of Large Language Models (LLMs) or Lethal Logic Machines…”


As stewards of organizational security, it’s imperative to approach the integration of LLMs with caution, ensuring robust security protocols, continuous monitoring, and educating teams about their potential risks. The promise of LLMs is undeniable, but a proactive approach to their challenges is crucial to harness their potential safely.

Here’s a summary of the risks associated with Large Language Models (LLMs) and the suggested mitigations, especially relevant for Chief Information Security Officers (CISOs):

Risks of LLMs:

  1. Hallucinations: LLMs can generate outputs that may not be accurate or factual.
  2. Over-reliance: Excessive dependence on LLMs can lead to misinformation, miscommunication, legal issues, and security vulnerabilities.
  3. Model Theft: Unauthorized access, copying, or exfiltration of proprietary LLM models can lead to economic losses, compromised competitive advantage, and potential access to sensitive information.
  4. Prompt Injections: Attackers can manipulate LLMs through crafted inputs, causing unintended actions.
  5. Training Data Poisoning: Tampering with LLM training data can introduce vulnerabilities or biases that compromise security, effectiveness, or ethical behavior.
  6. Sensitive Information Disclosure: LLMs may inadvertently reveal confidential data in their responses.
  7. Insecure Output Handling: Accepting LLM output without scrutiny can expose backend systems to vulnerabilities.
  8. Insecure Plugin Design: LLM plugins can have insecure inputs and insufficient access control.
  9. Model Denial of Service: Attackers can cause resource-heavy operations on LLMs, leading to service degradation.
  10. Supply Chain Vulnerabilities: The LLM application lifecycle can be compromised by vulnerable components or services.


  1. Automatic Validation: Implement mechanisms that cross-verify the LLM’s output against known facts or data.
  2. Break Down Tasks: Divide complex tasks into subtasks and assign them to different agents to manage complexity and reduce hallucinations.
  3. Risk Communication: Clearly communicate the risks and limitations of using LLMs to users.
  4. Build Safe Interfaces: Develop APIs and user interfaces that encourage responsible and safe use of LLMs.
  5. Secure Coding Practices: When using LLMs in development environments, establish guidelines to prevent the integration of vulnerabilities.
  6. Data Sanitization: Implement data sanitization and strict user policies to prevent unauthorized data access.
  7. Access Controls & Encryption: Employ a comprehensive security framework that includes access controls, encryption, and continuous monitoring to protect LLM models.

Example Attack Scenarios:

  • A news organization heavily uses an AI model to generate articles. A malicious actor exploits this over-reliance, feeding the AI misleading information, causing the spread of disinformation.
  • A software development team utilizes an AI system like Codex to expedite the coding process. Over-reliance on the AI’s suggestions introduces security vulnerabilities into the application

About the Author

Igor van Gemert is a prominent figure in the field of cybersecurity and disruptive technologies, with over 15 years of experience in IT and OT security domains. As a Singularity University alumnus, he is well-versed in the latest developments in emerging technologies and has a keen interest in their practical applications.

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Apart from his expertise in cybersecurity, van Gemert is also known for his experience in building start-ups and advising board members on innovation management and cybersecurity resilience. His ability to combine technical knowledge with business acumen has made him a sought-after speaker, writer, and teacher in his field.

Overall, van Gemert’s multidisciplinary background and extensive experience in the field of cybersecurity and disruptive technologies make him a valuable asset to the industry, providing insights and guidance on navigating the rapidly evolving technological landscape.

Original article

Igor will speak at the SwissCognitive World-Leading AI Network AI Conference focused on Beyond Efficiency: AI’s Creative Potential on 5th September.

Attention CISOs: The Hidden Dangers of Large Language Models (LLMs) or Lethal Logic Machines