This experiment highlights a deeper issue than just prompt injection—it exposes the lack of a clear trust boundary in LLM-based systems. When external sources like GitHub READMEs are treated as executable instructions rather than untrusted data, the model effectively collapses the distinction between code, content, and control logic. What makes this particularly concerning is that modern AI tools automatically ingest context from repositories, issues, and documentation. That means the attack surface is not the prompt—it’s the entire development environment. We’re already seeing similar patterns in real-world incidents where hidden instructions in GitHub content lead to unintended actions or data exposure. A more robust approach would require: Strict separation between system instructions and external context Context sanitization before ingestion Explicit instruction hierarchy enforcement Without these, improving models alone won’t solve the problem, because this is fundamentally an architecture-level vulnerability, not a capability issue.
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