AI-Generated Dockerfile Vulnerability Patterns
LLM-generated Dockerfiles repeat the same six or seven mistakes. Here is the pattern catalog and how to catch them before they ship.
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.
LLM-generated Dockerfiles repeat the same six or seven mistakes. Here is the pattern catalog and how to catch them before they ship.
Function calling gives models the ability to act. Acting safely on behalf of a specific user, in a specific context, within specific policy is a different problem.
LLM selection is ultimately a cost-quality optimisation under workflow constraints. The curve is not smooth, and the right point on it depends on where errors land in your pipeline.
AI cyber solutions are most useful where they reduce analyst toil: triaging alerts, prioritizing vulnerabilities, and drafting remediation. Here is what to trust and what to question.
Software licenses fall into a few clear buckets — permissive, copyleft, and proprietary — and knowing which is which decides what you can legally ship. Here is the map, without the legalese.
A function whose output space is finite and enumerable can be secured by testing. A function whose output space is every string of tokens up to some length cannot. That difference quietly invalidates most classical security contracts.
Model lock-in is the quiet liability of pure-LLM vendors. Safeguard's bring-your-own-model story gives enterprises the option Mythos-class competitors cannot match.
Vertex AI Safety is Google's approach to enterprise AI controls. For security-specific workflows, Griffin AI adds grounding the Safety layer doesn't.
The first enforcement window under the EU AI Act has closed. The actual pattern of enforcement looks different from the one vendors and advocacy groups predicted.
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