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AI Security

Securing MCP Servers: A Practical Checklist

MCP servers are runtime dependencies your agent trusts implicitly. Here is a concrete checklist for auth, tool pinning, sandboxing, and monitoring before you ship one.

Mar 22, 20266 min read
AI Security

Zero-Day Triage Without Drowning Engineers

A zero-day discovery pipeline is only as useful as the triage process around it. Here is what triage looks like when the pipeline gives engineers something they can defend.

Mar 21, 20267 min read
AI Security

Small Language Models: Security Use-Case Fit

Small language models aren't a worse version of large ones. For specific security workflows, they're the right tool — if you know which workflows.

Mar 21, 20262 min read
AI Security

Griffin AI vs OpenAI Pricing: Security Workloads

Per-token pricing on the OpenAI API looks cheap on a single call and expensive on a year-long security workload. Griffin AI's pricing reflects the architecture.

Mar 21, 20262 min read
AI Security

Claude MCP Tool Poisoning Threat Model 2026

A senior engineer's threat model for Claude MCP tool poisoning in 2026, covering malicious servers, description hijacking, and the authorization patterns that actually help.

Mar 21, 20267 min read
AI Security

Prompt Injection From Research To Bug Bounty

Prompt injection started as a research curiosity. In 2026 it is a regular line item on bug bounty leaderboards, with payout norms, scope definitions, and a maturing triage culture.

Mar 20, 20267 min read
AI Security

MCP Server Capability Drift Detection

MCP servers do not stay still. Tool surfaces drift, scopes expand, and the server you approved is not the server in production. Here is how to catch that.

Mar 20, 20267 min read
AI Security

Grounded Reasoning vs Hallucinated: Griffin AI vs Mythos

The difference between grounded reasoning and hallucinated reasoning is not eloquence — it's citation. A look at how Griffin AI anchors every claim.

Mar 20, 20266 min read
AI Security

Griffin AI vs Gemini On-Device: Developer Tools

Gemini on-device models are fast and cheap. For the developer-tool layer, they're useful. For the engine-plus-LLM layer, on-device is not the right fit.

Mar 20, 20262 min read

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