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.
Griffin AI's evaluation harness results published for the first time. Benchmark methodology, comparison against baselines, and what the numbers mean for production use.
A senior engineer's view of OpenAI API key leakage on GitHub at scale, why automated secret scanning misses so many, and what actually stops the bleeding.
AI agents that can execute code, browse the web, and manage infrastructure are proliferating. The security implications of these autonomous frameworks demand scrutiny.
When your pipeline starts producing zero-days, you inherit responsible disclosure obligations. Here is how to do it well, with the artefacts the pipeline already gives you.
A practitioner's methodology for using LLMs to augment — not replace — traditional bug discovery workflows, with patterns that hold up under real review load.
Most AI observability stacks log prompts and completions. The actual security signal is in the tool calls. Here is how to capture it.
AI coding assistants are now standard developer tooling. The incident data from 2025 and early 2026 shows a recurring pattern of source code, credential, and customer data leaking through them.
Fixing a transitive dependency is rarely a single bump. It is a cascade. Here is how to manage those cascades without flooding reviewers or breaking builds.
Anthropic's Model Context Protocol standardizes how AI models interact with external tools. The security implications for software supply chains are significant.
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