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.
Pattern-matching scanners miss zero-days by definition. An engine that follows taint across package boundaries plus a model that hypothesizes exploit conditions can find what either would miss alone. Here is how that pipeline works end to end.
Signature-based scanners only know what other people have already named. Here is the architectural reason they cannot find zero-days, and what actually does.
Anthropic's Mythos model is generating buzz for AI-powered vulnerability detection. We break down what it does well, where it struggles, and why security teams should approach the results with healthy skepticism.
Anthropic's Mythos model claims to find vulnerabilities in open-source code using a single LLM. We analyze where this approach falls short and why production-grade zero-day discovery requires Safeguard's Multi-Agent TAOR Deep Think AI Engine.
Safeguard launches its Zero-Day Discovery Engine, powered by the Multi-Agent TAOR Deep Think AI Engine — a multi-lead, multi-sub-agent architecture that performs deep CWE analysis on open-source packages to uncover vulnerabilities that traditional scanners miss.
Most zero-days that hurt enterprises in 2026 live three or four hops deep in the dependency graph. Here is what it takes to actually find them there.
Most security pipelines are organised around CVEs that already exist. Here is what changes when you flip the pipeline to surface zero-days first instead.
When your pipeline starts producing zero-days, you inherit responsible disclosure obligations. Here is how to do it well, with the artefacts the pipeline already gives you.
The difference between an engine-plus-LLM bug hunter and a pure-LLM one is not a tuning detail. It is a structural divide that determines whether the findings are usable.
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