What is AIDR? AI detection and response explained
AIDR is an emerging category that watches prompts, agents, models and data pipelines for attacks and misuse. Here is what it covers and what it does not.
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
AIDR is an emerging category that watches prompts, agents, models and data pipelines for attacks and misuse. Here is what it covers and what it does not.
An AI impact assessment documents purpose, data, impacts, reassessment, costs, review and risk acceptance. A template from public federal guidance.
VS Code can run MCP servers from workspace files, and local servers can run arbitrary code. A checklist on trust, config location, secrets and sandboxing.
The FBI warns criminals use AI voice and video to impersonate executives. Set a verification policy so a convincing call cannot move money or access.
A USENIX Security 2025 paper tested 16 code models and 576,000 samples for invented packages. What the figures say, what they do not, and what to do.
How to convert each successful AI attack into a repeatable regression test, so fixes stay fixed when prompts, models or data sources all keep changing.
trust_remote_code=True lets a Hub repository supply Python modeling code that runs locally. Here is when you need it and how to review it first.
weights_only=True limits what torch.load will unpickle to tensors and simple types. Here is what that buys you, and where you still need to be careful.
Permissions limit what an agent can do. Guardrails check what it tries to do. A decision guide to using both, grounded in OWASP and MCP guidance.
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