Secure Random Number Generation in C# with RandomNumberGe...
Why System.Random is a security liability in C# and how RandomNumberGenerator prevents predictable tokens, nonces, and keys in .NET applications.
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.
Why System.Random is a security liability in C# and how RandomNumberGenerator prevents predictable tokens, nonces, and keys in .NET applications.
Why java.util.Random and even UUID.randomUUID() can leak predictable tokens, and how Java's SecureRandom and NIST DRBG providers actually protect secrets.
A data-led look at software supply chain attacks in Q3 2025: npm maintainer phishing, VS Code extension abuse, and a quiet shift toward CI/CD targeting.
How Snyk AI-BOM's static analysis engine discovers agents, tools, models, datasets, and MCP servers hiding in code, even without a manifest file.
A technical look at how Snyk's AI-BOM statically detects MCP client-server connections in source code, what CycloneDX data it captures, and where its coverage stops.
How Snyk's aibom CLI uses static analysis to detect models, agents, and MCP servers, then maps them into a CycloneDX v1.6-compliant ML-BOM structure.
How Snyk's AI-BOM uses code-level analysis, not manifest parsing, to surface shadow AI models, agent frameworks, and MCP servers security teams don't know are running.
How Snyk's AI-BOM API exposes AI model and dataset inventories as queryable, CycloneDX-aligned data teams can pull into CI, GRC, and asset tooling programmatically.
How Snyk's AI-BOM tooling discovers prompt files, SKILL.md packages, and MCP tool chains, and the detection engine it uses to flag prompt-injection risk.
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