Cybersecurity Risks in Large Language Models LLMs.pptx

As Large Language Models (LLMs) like GPT continue to reshape industries such as healthcare, finance, and government, new and evolving cybersecurity risks are emerging. This presentation delves into the critical threats associated with LLMs, including data poisoning, adversarial attacks, model inversion, and malicious use cases like AI-generated phishing. We also explore robust mitigation strategies such as data sanitization, adversarial training, and continuous monitoring to ensure safe and secure AI deployment.

1. Cybersecurity Risks in Large Language Models (LLMs) Ali O. Elmi Riyadh, Saudi Arabia October 20, 2022 [email protected] Emerging Threats and Mitigation Strategies “No amount of sophistication is going to allay the fact that all your knowledge is about the past and all your decisions are about the future.” – Ian E. Wilson

2. Why LLMs and Cybersecurity Matter Critical to industries: healthcare, finance, government Increasing deployment, rising security concerns Risks range from technical to societal impacts

3. Key Cybersecurity Risks in LLMs Data poisoning Adversarial attacks Model inversion Malicious use (phishing/social engineering) Model hallucinations Backdoors Inference attacks

4. Data Poisoning Manipulated training data → corrupt model outputs False results, biased recommendations Example: healthcare data manipulation

5. Adversarial Attacks Small input changes → major output differences LLMs misclassify or malfunction Example: small image tweaks fool AI

6. Model Inversion Attacks Extract sensitive data by reverse-engineering the model High risk of leaking private or proprietary info Targets: confidential business data, personal data

7. Malicious Use: Phishing & Social Engineering AI-generated phishing emails Social engineering at scale Automated, convincing attacks

8. Model Hallucinations False information generation Risks in critical sectors: healthcare, law, finance Undetected hallucinations lead to bad decisions

9. Ethical Risks and Bias Bias embedded in training data Discrimination in AI decision-making Example: biased hiring recommendations

10. Backdoors in Model Architecture Hidden vulnerabilities inserted during development Exploitable backdoors for future attacks Challenge: detecting these weaknesses

11. Inference Attacks Sensitive info inferred from model outputs Attackers gather data from seemingly benign queries Increased risk with personal/financial data

12. Mitigation Strategy: Data Sanitization Ensure clean, vetted training data Prevent data poisoning at the source Regular audits of data sources

13. Mitigation Strategy: Robust Model Training Adversarial training to harden models Resilience against manipulated inputs Test models in real-world attack scenarios

14. Mitigation Strategy: Continuous Monitoring Real-time detection of anomalies Regular auditing for unexpected behavior AI-driven monitoring systems

15. Mitigation Strategy: Differential Privacy Adds controlled noise to outputs Protects sensitive data while maintaining utility Example: protecting user data in healthcare models

16. Mitigation Strategy: Bias Audits and Ethical Oversight Regular bias audits to ensure fairness Ethical governance of AI systems Correcting biases found in outputs

17. Case Study: Real-World LLM Breaches Example: AI-generated phishing campaign Attack on a financial LLM, extracting sensitive data Lessons learned and response strategies

18. Future Directions – AI Governance for LLMs Global trends in AI policy Emerging frameworks for LLM security Future of AI governance and ethical standards

19. Adapting to an Evolving Threat Landscape Cybersecurity must evolve with LLMs Continuous defense strategy adaptation Collaboration between AI developers and security experts

20. Q&A and Resources Questions Closing thought: “Securing the future of AI together”