Which vulnerability decisions should an LLM never make alone?
Models can draft and explain, but some calls need a named person: risk acceptance, exceptions, severity overrides and release blocks. A decision map.
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.
Models can draft and explain, but some calls need a named person: risk acceptance, exceptions, severity overrides and release blocks. A decision map.
vLLM's docs say multi-node traffic is insecure by default, and PyTorch Distributed has no auth or encryption. Here is how to isolate the nodes.
Dev mode, profiler endpoints, runtime LoRA updates and the tokenizer info endpoint each add risky routes. What vLLM's docs say to leave off or guard.
No. vLLM's API key covers some path prefixes, but its own security docs list inference and control endpoints outside them. Here is how to close the gap.
Set an allowlist of media domains, block redirects and cap download sizes. The vLLM docs explain why URL inputs to an inference server become an SSRF risk.
Treat AI output as untrusted input. Validate patches with deterministic checks on paths, packages, size and tests before a human reads the diff.
Treat an AI-suggested fix like a patch from an unknown contributor: read it, test it, rescan it. A step list for verifying it before merge.
An LLM app takes in text through training data, a retrieval corpus and prompts. Each fails differently and needs a different control. A comparison table.
Yes. Pre-trained models and datasets pulled for a prototype are supply chain inputs. Here are the checks to run before they reach production.
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