In the rapidly evolving world of artificial intelligence (AI), security risks are a growing concern. In the third episode of our podcast, host Brad Bussie explores the top AI security risks that organizations face today.
Bussie highlights the OWASP Top 10 for Large Language Models (LLM), a comprehensive list outlining the key vulnerabilities in AI systems.
- Prompt Injection: A vulnerability where AI systems can be manipulated by misleading prompts, leading to harmful outputs.
- Insecure Output Handling: Risks arise when AI outputs are not scrutinized, potentially leading to remote code execution.
- Training Data Poisoning: Compromised training data can lead AI models to exhibit biased or unethical behavior.
- Model Denial of Service: AI models can be disrupted by overwhelming requests or misleading data, rendering them ineffective.
- Supply Chain Vulnerabilities: AI systems relying on third-party data sets may face security breaches if these sources are compromised.
- Sensitive Information Disclosure: AI systems may inadvertently disclose private or sensitive information.
- Insecure Plugin Design: Vulnerabilities in AI plugins can introduce various security risks.
- Excessive Agency: Over-empowering AI systems with permissions or autonomy can lead to unintended consequences.
- Overreliance on AI: Heavy dependence on AI for critical tasks may create security gaps and operational risks.
- Model Theft: The theft of AI models poses a risk of their misuse.
These risks underscore the importance of vigilant cybersecurity measures and awareness as AI continues to permeate various technological and business aspects.
