How do you cap resource abuse on a self-hosted inference server?
Cap request size, output length, parallel samples, queue depth and per-user rate. A practical checklist built on OWASP LLM10 and vLLM's own limits.
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.
Cap request size, output length, parallel samples, queue depth and per-user rate. A practical checklist built on OWASP LLM10 and vLLM's own limits.
Allowing a tool is not enough. Constrain its arguments too: file paths, email recipients, record IDs and amounts, checked by code outside the model.
Yes. Grant an AI agent a risky tool for one task or a short window, then let it expire. How time boxed tool grants work and which tools deserve them.
Skipping permission prompts removes the human check. Anthropic says to run such sessions in a container, VM, or sandbox runtime. Here is what to set up.
torch.load unpickles by default, so the file source matters. See what weights_only=True restricts, what it leaves alone, and how to load checkpoints.
torch.hub.load fetches a GitHub repo and runs Python from its hubconf.py. What trust_repo does, the defaults to know, and how to use it with care.
Hugging Face notes TF and Flax checkpoints are not affected by pickle attacks and can be converted on load. When that helps, and where it stops.
Treat LLM output as untrusted input. OWASP's improper output handling guidance: validate, encode and parameterize before output reaches another system.
It can be safe if you limit what the assistant can read and do. Use this plain checklist before you connect an AI assistant to email, calendar, or files.
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