The Top 10 Risks Business Leaders Need to Know About Large Language Models

The rapid rise of AI-powered chatbots and large language models like ChatGPT is transforming how businesses operate and engage with customers. These systems, built on large language models (LLMs) trained on massive datasets, offer exciting new capabilities — from generating human-like text to powering interactive virtual assistants. However, as with any powerful new technology, LLMs also introduce new risks that business leaders need to understand and mitigate.

Recently, the Open Web Application Security Project (OWASP), a leading authority on cybersecurity, released their list of the Top 10 security risks for LLM applications. Here is what every executive should know about these critical LLM vulnerabilities:

The OWASP Top 10 Risks for LLM Applications

1. Prompt Injection: Attackers can manipulate the LLM to execute unintended actions by “injecting” malicious instructions. This could lead to data theft, privilege escalation, and more.
2. Insecure Output Handling: If an application blindly accepts LLM outputs without proper validation, it exposes backend systems to potential exploits like cross-site scripting (XSS) attacks.
3. Training Data Poisoning: LLMs are only as good as their training data. Manipulation of training datasets can introduce harmful biases, vulnerabilities, or enable backdoor access.
4. Model Denial of Service: Resource-intensive LLM operations triggered by attackers can degrade system performance and drive up computing costs.
5. Supply Chain Vulnerabilities: Compromised data, models, or components anywhere in the complex LLM development lifecycle introduces risks.
6. Sensitive Information Disclosure: LLMs may inadvertently reveal confidential data in generated outputs, violating data privacy.
7. Insecure Plugin Design: Extensible LLM plugins with poor input validation or access control are easier for attackers to exploit.
8. Excessive Agency: Granting an LLM too much functionality, autonomy or privilege amplifies the impact of any vulnerabilities.
9. Overreliance: Uncritically trusting LLM outputs without human oversight can propagate misinformation, bias, and security issues at scale.
10. Model Theft: Exfiltration of proprietary LLM models is a threat to intellectual property and can enable reverse engineering of sensitive training data.

Key Takeaways for Business Leaders

• Conduct a thorough risk assessment and threat modeling exercise before deploying any LLM application. Understand your organization’s specific threat landscape.
• Ensure strong access controls, monitoring, and security safeguards are in place across the entire LLM lifecycle — from initial model training to production deployment.
• Establish clear policies and staff training around responsible LLM use. Humans should remain in the loop for high-stakes decisions.
• Evaluate the security practices of any vendors or third-party LLM components. The security of your LLM application is only as strong as its weakest link.
• Keep abreast of this rapidly evolving risk landscape. Follow OWASP and other leading voices in AI security research to stay current on emerging LLM threats and countermeasures.

The potential of large language models is immense — but so are the risks they pose if not properly understood and mitigated. By taking proactive steps to address the OWASP Top 10 LLM risks, business leaders can harness the power of this transformative technology more securely and strategically. After all, responsible stewardship of AI systems is quickly becoming a core business imperative.