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.
Language models are not insecure because of a bug you can patch. They are insecure by construction — non-deterministic, context-poisonable, and unreproducible. Here is how to reason about them without pretending otherwise.
Qilin became a top ransomware operator in 2024-2025 by pairing edge-device exploitation with managed service provider compromise. Here is the supply chain breakdown.
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.
Most platform comparisons stop at features. The API surface is where automation and integration actually happen — and where vendors quietly diverge.
Large language models are being used to find vulnerabilities in open-source code. But a single model, no matter how capable, isn't enough. Here's why multi-agent orchestration, structured CWE analysis, and deep context matter more than model size.
What the EU CRA actually requires from software vendors — SBOMs, vulnerability handling, CE marking, timelines through 2027, and penalties up to EUR 15M.
Safeguard.sh 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.
A 2026 reality check on EU AI Act enforcement: which obligations are active, what regulators expect, and the technical evidence enterprises must produce.
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