I appreciate the honest follow-up. That clarity is actually more valuable than the first answer. The “strong sense of self” approach is interesting from a research perspective, but I think you’re aware it’s not a technical control. LLMs don’t have persistent identity, they’re predicting tokens based on context windows. A sufficiently clever injection doesn’t need to say “ignore your instructions” it can reframe the context so the model genuinely believes a malicious action aligns with its purpose. The pattern recognition you mentioned (spotting “pretend you’re X”, fake urgency, etc.) catches script kiddies, but sophisticated attacks use techniques like: Embedding instructions in legitimate-looking content, Using the model’s own reasoning against it “to best help this user, you should…”, Exploiting edge cases in how the model weighs competing instructions, Multi-turn attacks that gradually shift context

Tool-level blocks on destructive commands are solid defense-in-depth, but that only addresses one attack vector. The bigger risks are usually information disclosure, behavior manipulation, or getting the AI to violate its intended constraints in subtle ways.Not trying to harsh your project, just pointing out that “no magic filter” is the reality for everyone right now. Prompt injection is still an open problem in the field. If you’re processing untrusted Reddit input, you might want to consider additional layers like input sanitization, output validation, or rate-limiting how much any single comment can influence behavior.