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.
Open-source LLM ecosystems hit a turning point in 2026 as supply chain incidents — backdoored fine-tunes, compromised weights, malicious adapter packages — moved from rare to recurring.
Picking a model for a security workflow is not the same as picking one for a chatbot. Here are the criteria that actually matter and how to weigh them.
Race conditions are the hardest class of vulnerabilities for static analysis. Specific architectural capabilities separate tools that find them from tools that claim to.
Evals that run once are marketing. Evals that run on every build are infrastructure. Griffin AI runs the harness on every change; Mythos does not describe one.
GitHub Copilot suggests fixes. Griffin AI generates fix PRs with taint paths and disproof attached. The difference is review burden.
Autonomous coding agents can escalate privilege in subtle ways that traditional threat models miss. A breakdown of the common escalation paths and how to constrain them.
Crypto exchanges remain the highest-value target for supply chain attackers. Here is the 2026 defence playbook that hardens the entire stack.
Quarterly inventories are wrong by the time they are signed. Continuous discovery is the only model that matches modern rates of change.
The Safeguard CLI brings SBOM generation, vulnerability scanning, policy checks, and supply chain queries directly into your development workflow.
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