AI Engineering Blog
Practical engineering research, benchmark studies, and security disclosures from the PromptImageLab engineering team.
Deep dives into AI agent loops, Model Context Protocol security vulnerabilities, RAG chunking economics, and real-world enterprise ServiceNow operations.
Recent Engineering Research (10)
Audit Model Context Protocol (MCP) server definitions by classifying filesystem paths, checking network endpoints, flagging shell execution privileges, and calculating blast radius scores before granting client access.
Detect excessive MCP permissions by comparing tools declared in your server configuration against the actual tools invoked during runtime execution logs, identifying unused over-privileged capabilities.
A comprehensive engineering blueprint for security testing Model Context Protocol (MCP) servers, covering authentication, schema validation, rate limiting, and prompt injection defense.
Diagnose MCP schema validation errors by inspecting JSON-RPC tool parameters, fixing mismatched parameter types, ensuring required properties exist, and validating JSON Schema draft versions.
A technical reference defining all Model Context Protocol permission types, capability scopes, capability flags, and associated security risk categories for AI engineering teams.
An inside look at how PromptImageLab's MCP Security Scanner evaluates manifests, detects command injection vectors, analyzes permission blast radius, and verifies argument validation deterministic algorithms.
Learn how PromptImageLab detects redundant and duplicate chunks in RAG retrieval pipelines using semantic vector cosine similarity and Jaccard token overlap metrics.
Discover how PromptImageLab detects and breaks infinite tool call loops in autonomous AI agents using execution graphs, state signature hashing, and call frequency thresholds.
Diagnose RAG retrieval failures by isolating vector embedding mismatch, chunk boundary fragmentation, index stale state, and query term misalignment using step-by-step diagnostic scoring.
A practical framework for building automated LLM regression testing suites to catch model drift, prompt change regressions, and formatting breakages before deploying to production.