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.
How Snyk Code tracks tainted data across function and file boundaries using call-graph summaries, taint propagation, and hybrid symbolic AI rules.
A mechanical look at how Snyk Code builds control flow and data flow graphs to trace paths that produce race conditions and null pointer dereferences.
How Snyk Code's taint-tracking engine tells sanitizers apart from insecure sources and sinks, and where the source-sink-sanitizer model still needs human review.
Snyk Code cuts SAST false positives using semantic analysis: AST/data-flow graphs plus ML trained on real code, not regex patterns. Here is how the mechanics work.
A mechanical, publicly-documented look at how Snyk Code's symbolic analysis and ML model trace source-to-sink data flow to detect SQL injection vulnerabilities.
How Snyk Code's taint analysis traces untrusted input from source to sink to flag reflected, DOM-based, and stored XSS with fewer false positives.
A technical look at how Snyk Code's static analysis engine detects hardcoded API keys, tokens, and credentials in source code — and where source-code scanning alone falls short.
A technical look at how Snyk Code structures, scores, and versions its SAST rules — from the DeepCode AI engine to CWE mapping and custom rule bundles.
Snyk Code scans multi-language monorepos in a single pass by parsing source files directly into a shared internal representation, no build step required.
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