Assessing Risks in LLM-Driven Applications: A Developer's Guide
This article provides a practical guide for developers on how to assess and mitigate risks in LLM-powered applications, covering key frameworks like OWASP Top 10 for LLMs and the NIST AI Risk Management Framework.
Why it matters
Developers need to proactively assess and mitigate AI risks to avoid regulatory issues, liability, and loss of customer trust.
Key Points
- 1Developers need to understand AI governance frameworks to address technical risks in LLM-driven applications
- 2OWASP Top 10 for LLM Applications covers critical risks like prompt injection, sensitive data disclosure, and supply chain vulnerabilities
- 3NIST AI Risk Management Framework provides a structured approach to identifying, assessing, and managing AI risks in enterprises
Details
The article explains that AI governance is no longer just a legal or compliance concern, but a technical one that developers need to address. It introduces three key frameworks: the OWASP Top 10 for LLM Applications, the NIST AI Risk Management Framework, and the EU AI Act. The focus is on the OWASP and NIST frameworks, which provide practical guidance for developers. The OWASP Top 10 covers risks like prompt injection, where user input can hijack the LLM's behavior, and sensitive data disclosure, where the LLM may inadvertently reveal confidential information. The article provides mitigation strategies for these risks, such as treating all external content as untrusted, limiting the model's capabilities, and implementing access controls. It also discusses supply chain vulnerabilities when using third-party models or checkpoints. The article emphasizes the importance of developers understanding and applying these frameworks to ensure responsible deployment of LLM-powered features.
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