The document discusses cybersecurity risks associated with large language models (LLMs), highlighting issues such as data poisoning, adversarial attacks, and model hallucinations, which pose threats across critical industries. It outlines various mitigation strategies, including data sanitization, robust model training, and bias audits, to enhance security and ethical governance. The importance of adapting to the evolving threat landscape through collaboration between AI developers and security experts is emphasized.

1. Cybersecurity Risks in Large
Language Models (LLMs)
Ali O. Elmi
Riyadh, Saudi Arabia
October 20, 2022
alielmi1@gmail.com
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”