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.
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.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.
CWE-502 deserialisation chains are the canonical stress test for AI bug hunters. Why Griffin AI's grounded synthesis finds real chains and Mythos-class scanners hallucinate them.
What happens when the bug does not match any known CWE? A study of how grounded and pure-LLM scanners perform on genuinely novel vulnerability patterns.
Finding a bug is not the same as proving it is exploitable. How Griffin AI synthesises concrete exploit paths and why pure-LLM scanners rarely get past the sketch stage.
AI-powered fuzzing and code analysis are accelerating zero-day discovery. Here's what that means for defenders.
Getting the CWE right is not a taxonomic hobby. It drives remediation, compliance mapping, and detection engineering. Here is how grounded and pure-LLM scanners compare.
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