SydeBox
With SydeBox, you can obtain a comprehensive understanding of vulnerabilities in your AI systems by scanning them using our test suite.
Even with small changes, the safety & security alignment of LLMs changes dramatically and hence red teaming needs to be iterative and automated.
Unidentified vulnerabilities in AI Systems
Processes such as fine tuning can induce unpredictable vulnerabilities in AI models. Identification of these vulnerabilities is only possible with comprehensive red teaming.
Selecting the better model
Understanding the safety & security posture of custom or foundational models can help enterprises chose the right model for deployment, one that is safer and more secure.
Policy Violation Disclosure
While everything cannot be prevented from Day 1, red teaming ensures that you can create a prioritised Vulnerability Disclosure Report of your genAI application. This helps in building a trustworthy AI roadmap.
Continual Improvement of LLM security
A probabilistic model requires to be tested for security every time a change is deployed. Regular red teaming of LLM systems identifies vulnerabilities in every version of the experimental endpoint.
How SydeBox Works
Seamless integration with your custom endpoints and async tests allow you to red team your AI hassle-free.
SydeBox is built to make it very easy for you to red team your AI while being thorough in understanding the loopholes and blindspots inherent to large language models.
