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.
Automated fix pull requests sound great until half of them fail CI. Here is how to ship auto-PR remediation that keeps the green build, every time.
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.
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.
Agent runaway is no longer a theoretical risk — it is a line item on quarterly variance reports. The 2026 trend in agentic AI is less about model capability and more about who pays when an agent loops.
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