What is AI Agent Security
AI agent security explained: how autonomous AI agents get attacked through prompt injection, tool poisoning, and exposed MCP servers, and how to stop it.
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 agent security explained: how autonomous AI agents get attacked through prompt injection, tool poisoning, and exposed MCP servers, and how to stop it.
AI coding assistants now write nearly half of some codebases—and research shows 45% of that code ships with exploitable flaws. Here's what security teams need to know.
Vibe coding lets AI write entire apps from a prompt with little human review — here's what it is, real incidents it's caused, and how to detect the risk.
A vulnerability in version 1.2.0 may not affect your 1.3.5 install if the fix reshaped the call signature. Version-aware resolution is where deterministic engines beat pure-LLM heuristics.
EU AI Act enforcement began in 2026. Vendors sold as "AI security tools" are now high-risk systems with documentation obligations. The shape of the documentation matters.
Training data poisoning corrupts an ML model's training data to plant hidden backdoors. Learn how it works, real incidents, and how to detect it.
Slopsquatting exploits AI coding assistants that hallucinate nonexistent package names, which attackers then register as real, malicious packages.
Model inversion attacks reconstruct sensitive training data from a model's outputs. Learn how they work, real cases, and how to defend your ML APIs.
Adversarial machine learning exploits model decision boundaries via evasion, poisoning, extraction, and inference attacks -- here's how it works and how to defend against it.
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