Question: Can you recommend a solution for identifying weaknesses and improving security in my language model application?

If you're looking for a tool to find vulnerabilities and improve your language model application's security, Promptfoo is definitely worth a look. This tool has a command-line interface and a library to evaluate the quality of LLM output. It has options to customize evaluation metrics, support multiple LLM providers, and a red teaming feature to create custom attacks to try to find vulnerabilities and offer remediation advice. It's an open-source tool geared for developers who want to fine-tune their models and get the best possible output.

Another top contender is LangWatch, an integrated solution to ensure the quality and security of generative AI solutions. It has strong guardrails against risks like jailbreaking, sensitive data leakage, and hallucinations. LangWatch offers real-time metrics for conversion rates, output quality, and user feedback, so you can optimize your model performance. It also can be used to create test datasets and run simulation experiments, which can be used to continuously improve your application.

For a more comprehensive security approach, BoxyHQ provides a suite of tools to protect sensitive information and secure cloud applications. Its LLM Vault provides advanced encryption and fine-grained access controls for sensitive data. In addition, BoxyHQ offers features like Enterprise SSO, Directory Sync, and Audit Logs, making it a robust platform to increase trust and meet industry standards.

If you're looking to automate evaluation and spot common problems like hallucinations and bias, Deepchecks could be the way to go. The tool uses a "Golden Set" approach for automated evaluation and has features for monitoring, debugging, and version comparison. Deepchecks is designed to ensure high-quality LLM applications from development to deployment, so it's a great tool for keeping your AI systems reliable and secure.