Integrating AI Tools Without Expanding Your Attack Surface
Stanford researchers found developers using AI coding assistants wrote more security bugs — and felt more confident in them. Here's how to adopt AI safely.
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.
Stanford researchers found developers using AI coding assistants wrote more security bugs — and felt more confident in them. Here's how to adopt AI safely.
AI agents can browse regional pricing, compare tiers, start a Stripe checkout, and verify activation — the entire Safeguard procurement journey now runs through the MCP server.
An SBOM tells you what code you ship. An AIBOM answers the question that has no good answer today: what models, datasets, and prompts is our AI actually built on — and where did they come from?
AI coding assistants confidently recommend packages that do not exist. Attackers noticed. Slopsquatting turns a model's hallucination into a supply-chain foothold — and the fix is not to make models stop hallucinating.
GitHub reported in 2024 that Copilot writes up to 46% of code in enabled files — the same vulnerability classes humans write, now shipped at machine speed.
Snyk analyzed nearly 10,000 real developer environments and found 43% run 2+ AI coding tools at once — with MCP servers and skills quietly widening the attack surface.
Claude's May 2026 enterprise and desktop expansion put MCP tool calls in front of compliance teams and IDEs alike — governance can no longer be an afterthought.
Snyk found 76 confirmed-malicious skills among 3,984 analyzed and roughly a third of public MCP servers carrying exploitable flaws — legacy AppSec never modeled an agent as the author.
CVE-2025-6514 let a flawed MCP proxy escalate to full remote code execution — a preview of why AI/agentic risk needs its own budget line, not a slice of the AppSec line.
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