How One Developer Built a Prompt Injection Defense Beyond Text Classification

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A developer on DEV Community demonstrated that AI agents can be manipulated through prompt injection attacks embedded in ordinary inputs like support tickets, where malicious instructions are indistinguishable from legitimate ones in a model's context window. The author argued that most existing defenses focus on text classification, which is insufficient because the core problem is one of provenance — the model cannot reliably distinguish user instructions from instructions embedded in documents it processes. To address this, they built a governance hook called GoalIntegrity, tested against five live AI models, that applies three layered controls: quarantining untrusted tool output, screening for instruction-shaped text patterns, and binding the agent's capabilities at the start of a run. The capability-binding step is described as the most critical, ensuring that tool calls outside the predefined scope are denied regardless of what text the model encounters mid-run. Full source code for the implementation was published in an open repository under agent-harness-patterns.

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