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.
pip-audit is the PyPA-backed tool for scanning Python dependencies against the OSV and PyPI advisory databases. Here is how to run it well — and where it needs backup.
PyTorch is the dominant deep-learning framework for research and production — and its torch.load remote-code-execution history makes loading a model checkpoint one of the most security-sensitive operations in modern ML.
React escapes JSX text for you, but XSS sinks, secrets in the client bundle, token storage, and a 500-package npm worm are still yours to handle.
Rails is safe by default — until a developer reaches for YAML.load, Kernel#open, or a raw-string query. Here are the Ruby footguns and the gem hygiene that keep them closed.
Spring Boot's convenience defaults can quietly widen your attack surface. Here's how to harden actuators, dependencies, and auto-configuration for 2026.
TensorFlow is one of the most widely deployed machine-learning frameworks — and its history of model-deserialization RCE and crafted-tensor memory bugs makes its version and loading habits genuinely security-relevant.
WebAssembly runs untrusted code in a memory-isolated sandbox, but sandboxed is not the same as safe. Here is how the Wasm security model actually works and where it breaks.
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