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 ...
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 ...
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 ...

How indirect prompt injection attacks on AI work - and 6 ways to shut them down

How indirect prompt injection attacks on AI work - and 6 ways to shut them down ...

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 ...
SiliconANGLE

Google updates Gemini 2.5 LLM series with new entry-level model, pricing changes

Google LLC today introduced a new large language model, Gemini 2.5 Flash-Lite, that can process prompts faster and more cost-efficiently than its predecessor. The algorithm is rolling out as part of a ...