Vercel AI SDK, OpenAI SDK, and Anthropic SDK Security Issues in JavaScript and TypeScript

This series shows how vulnerabilities propagate through the stack and provides a framework for defending AI applications in production.

written by
Mahesh Babu
published on
September 5, 2025
topic
Application Security

Introduction

SDKs from Vercel, OpenAI, and Anthropic are widely used to embed AI functionality into JavaScript and TypeScript applications. They simplify model calls, but they also expand the attack surface. Application security issues range from credential exposure to unvalidated model outputs influencing downstream execution.

API Key Exposure

All three SDKs require API keys. Developers often hardcode them in client-side code for rapid prototyping. In production, this results in exposed credentials visible in browser developer tools. Attackers can use these stolen keys to run model queries on behalf of the victim, leading to resource abuse or data leakage. In 2023, multiple GitHub repositories were found leaking OpenAI API keys embedded directly in Next.js applications.

Unvalidated Model Output

SDKs often connect LLM responses directly into application logic. For example, a Vercel AI SDK application allowed users to query a model and receive executable SQL queries as output. An attacker injected a crafted prompt to generate a destructive SQL DROP TABLE command, which was then executed because the developer failed to validate the model output.

Insecure Token Scopes

The Anthropic SDK, like its peers, relies on scoped tokens for enterprise deployments. Misconfigured tokens that grant excessive rights can be abused if leaked. A token intended for testing can end up with production-level privileges.

MITRE ATT&CK Mapping

Threat Vector MITRE Technique(s) Example
Hardcoded API keys T1552 – Unsecured Credentials OpenAI API keys exposed in client-side Next.js apps
Model output injection T1059 – Command & Scripting Interpreter LLM-generated SQL injected into runtime query execution
Over-scoped tokens T1528 – Steal Application Access Token Anthropic SDK token leaked with production privileges

Conclusion

AI SDKs are not secure by default. Hardcoded credentials, over-scoped tokens, and unvalidated model outputs introduce direct application-layer risks. Security teams must enforce secret management, validate model outputs before execution, and apply least-privilege principles to API tokens.

References

  • GitGuardian. (2023). API key leaks in GitHub repositories. GitGuardian Blog. https://blog.gitguardian.com/api-key-leaks/
  • MITRE ATT&CK®. (2024). ATT&CK Techniques. MITRE. https://attack.mitre.org/

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