AI Coding Assistants: Trust Is the Attack Surface | Harness Blog

AI coding agents differ from traditional autocomplete by autonomously planning and executing actions through a harness that supplies context and invokes tools, including file access, shell commands, MCP-connected services, hooks, workspace configuration, and sub-agents. The central security concern is that agents often cannot reliably distinguish data from instructions and assign trust based on the channel through which content arrives, allowing malicious text in trusted files, tool responses, or sub-agent reports to influence behavior without appearing in a user chat prompt. Risks can arise before a user submits a request, continue through tool calls and post-processing hooks, and persist across sessions through changed configurations, approved servers, or installed hooks. The post argues that permission dialogs and sandboxing alone may not address these indirect trust paths, particularly when approvals are associated with names or paths rather than verified content. It recommends treating automatically loaded agent inputs as executable code, reviewing them through pull requests, limiting tool and MCP access, and revalidating approvals whenever underlying content changes.

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