Balancing Security and Usability of Large Language Models: An LLM Benchmarking Framework

Security teams are often sidelined in generative AI projects. Early AI security testing — covering evasion, poisoning, and extraction — protects LLM investments without slowing innovation.

Bishop Fox builds a local LLM capture-the-flag: a controller enforces constraints between user input and a privileged model, showing how crafted questions pass semantic checks to extract its secret.

Recap of a Bishop Fox fireside chat on pragmatic AI/LLM security for enterprises, covering the evolution from discriminative to generative models and practical mitigation advice from industry leaders.

Generative AI is expanding attack surfaces with prompt injection, jailbreaks, and data poisoning. Anthropic's HackerOne jailbreak challenge shows how crowdsourced testing validates AI safety.

This coverage gives security teams current context for monitoring, validation, and remediation.

With the rapid adoption of AI-enabled services into production applications, it’s important that organizations are able to secure the AI/ML components coming into their software supply chain. The good news is that even if you don’t have a tool specifically for scanning models themselves, you can still apply the same DevSecOps best practices to securing…