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.
Snyk Code traces untrusted data from source to sink using interprocedural static analysis and ML ranking. Here's how the taint-tracking mechanics work.
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.
Weekly insights on software supply chain security, delivered to your inbox.
Your first fix PR is minutes away.
No sales call required, even your agent can complete the purchase over MCP.
