AIP vs prompt guardrails - why we chose cryptography
Most AI safety tools use prompt-level filters: - "Don't do anything harmful" - LLM-as-judge (another model watching the first one) - Retrieval-based guardrails The problem: all of these are probabilistic. A clever prompt injection bypasses them. They work 95% of the time, but 5% failure rate on financial transactions is a disaster. AIP takes a different approach: cryptographic enforcement. -...
@observe - free agent debugging before you need security
We just shipped v0.4.0 with @observe, a free, lightweight way to see what your agents are doing. from aip_protocol import passport, observe agent = passport(name="my-bot", domain="korven.cc") @observe(agent) def process_payment(to, amount): return stripe.charge(to, amount) Every call logs: agent DID, action, parameters, latency, success/failure. Zero enforcement, your function always executes....
Show me your scariest AI agent failure story
We've all seen it - an agent goes rogue during a demo, a test agent accesses production data, a chatbot sends emails it shouldn't. What's the worst thing an AI agent has done in your system? (Bonus points if it happened in production 😅) I built Korven because my agent nearly drained a test account during a LIVE demo. Would love to hear your stories.
Why do we trust AI agents with zero verification?
Honest question for the community: We require HTTPS for every web request. We require OAuth for every API call. We require 2FA for every login. But AI agents? We let them execute actions with nothing more than a system prompt and a prayer. An agent can transfer money, delete data, email customers — and the only "security" is hoping the LLM follows instructions. Prompt injection can bypass that...





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