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.
AI agents break least-privilege assumptions built for humans: they chain tools, act autonomously, and compose narrow scopes into broad access no one reviewed.
Model flaws and application flaws in AI agents cause different breaches and need different fixes. Real incidents show where each risk actually lives — and how to test for both.
Attackers smuggle malicious payloads into trusted AI skill repositories via typosquats, staged fetches, and split-file obfuscation — here is exactly how it works.
Most AI model inventories are name-and-owner spreadsheets. Here's the provenance, licensing, CVE, and revalidation fields a real AI model risk registry needs to track.
A precise glossary of agentic AI security terms — prompt injection, tool poisoning, model jailbreaking, excessive agency, and MCP rug pulls — with concrete attack examples.
A practical checklist for scoping MCP server permissions, denying risky defaults, and limiting the blast radius when an AI agent's tool access is exploited.
Agentic codebases build call graphs at runtime, defeating static analysis. Here's why SAST tools miss prompt injection and tool-schema risks—and how Safeguard closes the gap.
VC funding for agentic AI security startups hit new highs in 2026, with identity governance, autonomous pentesting, and SOC automation drawing the biggest rounds.
The June 2025 MCP spec made every server an OAuth 2.1 resource server, mandated RFC 8707 resource indicators, and added elicitation. Here is what changes for blue teams.
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