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.
SecBench positioned itself as a comprehensive cybersecurity knowledge and reasoning benchmark for LLMs. A methodology review of its construction, scoring, and the gaps that separate the advertised coverage from what the benchmark actually exercises.
Griffin AI reports 98-100% hold rate against adversarial probes. Most Mythos-class tools have never published an adversarial number at all.
SWE-bench became the default benchmark for measuring AI coding agents, but the security extensions that were bolted on afterwards deserve their own scrutiny. A field review of what they measure, where they break, and whether you should trust the numbers.
A benchmark number is only as good as the methodology that produced it. Here is how Griffin AI builds its harness and why most Mythos-class tools cannot be audited.
A working engineer's review of CyberSecEval, the Meta-originated benchmark that has quietly become the default sniff test for AI-for-security claims. What it actually measures, what it misses, and how to read its scores without fooling yourself.
Griffin AI publishes a five-family eval harness with concrete numbers. Most Mythos-class competitors ask buyers to trust marketing claims instead of data.
Weekly insights on software supply chain security, delivered to your inbox.