Monitoring LLM behavior: Drift, retries, and refusal patterns

The offline pipeline's primary objective is regression testing — identifying failures, drift, and latency before production.
SiliconANGLE

Mend.io launches AI system prompt hardening solution to secure LLM instructions

Application security solution provider White Source Ltd., also known as Mend.io, today launched System Prompt Hardening, a dedicated capability designed to detect issues within the hidden instructions ...
VentureBeat

Microsoft's new AI training method eliminates bloated system prompts without sacrificing model performance

In building LLM applications, enterprises often have to create very long system prompts to adjust the model’s behavior for their applications. These prompts contain company knowledge, preferences, and ...
BizTech Magazine

Prompt Injection Attacks: The LLM Security Risk IT Leaders Must Address

Security leaders must adapt large language model controls such as input validation, output filtering and least-privilege ...

LLM-As-A-Judge: What To Expect From Using AI To Evaluate AI

LLM-as-a-judge is exactly what it sounds like: using one language model to evaluate the outputs of another. Your first ...
Security Boulevard

LLM Proxies vs. MCP Gateways: What’s the Difference?

As enterprise adoption of generative AI accelerates, so does the number of new components showing up in architecture diagrams. Among the common are LLM proxies and MCP gateways. They are often grouped ...