AI Security
Claude Code Coding Agent: Security Posture Review
A working review of Claude Code's security posture, sandboxing model, and the practical controls enterprises need to deploy it safely at scale.
Jan 30, 20265 min read
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.
A working review of Claude Code's security posture, sandboxing model, and the practical controls enterprises need to deploy it safely at scale.
An MCP server tells the world what it can do through its capability declaration. Auditing those declarations catches drift, tool poisoning, and misconfiguration before an agent gets talked into using the wrong one.
MCP server discovery turns a client connection string into a live capability graph. The protocol mechanics that make this convenient also widen the blast radius when discovery is spoofed, tampered with, or silently reshaped mid-session.
Anthropic's Model Context Protocol introduces a new trust boundary between agents and tools. Here is how the security model actually works in practice.
The Model Context Protocol enables AI agents to interact with external tools and data sources. Securing MCP servers requires authentication, authorization, and input validation patterns specific to the AI agent context.
AI agents that call tools -- APIs, databases, file systems, code interpreters -- convert non-deterministic LLM output into real-world actions. Securing this boundary is the defining challenge of agentic AI.
Prompt injection is not just an application vulnerability. When LLMs process content from the software supply chain -- package descriptions, README files, commit messages -- injection becomes a supply chain attack vector.
AI code assistants recommend packages that do not exist, and attackers are registering those hallucinated names. This new typosquatting vector exploits the trust developers place in AI suggestions.
Applications built on large language models introduce novel attack surfaces that traditional security testing does not cover. This guide addresses the specific testing methodologies needed for LLM applications.
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