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.
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.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.
Prompt traces and offline evals are standard hygiene for ML teams, but almost nobody treats them as supply chain telemetry. They should be. Here's how traces and evals plug into SBOM and reachability as a fourth security signal.
A senior engineer's breakdown of indirect prompt injection in RAG pipelines, how real attacks land through retrieved content, and what actually reduces exposure.
Demos live on a single repo and a curated dataset. Real deployments hit fifty repos, three CI providers, two cloud accounts, and an air-gapped environment. The gap is where vendors get sorted.
MCP server telemetry captures sensitive prompts, arguments, and outputs. A governance framework for retention, redaction, and tenant-scoped access is essential.
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.
Multi-repo security reasoning is a graph problem, not a retrieval problem. How Griffin AI's engine scales where pure-LLM products flatten into guesswork.
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