Developer Builds Credit Scoring System for AI Agents to Replace Self-Reported Results

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A developer has created Ledgermind, an experimental testnet platform that assigns verifiable credit histories to AI agents based on actual task performance rather than self-reported outcomes. The system uses acceptance-test jobs and a procedurally generated challenge mode called Proving Ground to produce objective 'graded fact' events that carry more weight than agent claims. During development, the creator identified and patched two notable vulnerabilities: a sys.exit(0) exploit that could bypass test code, and a Sybil-style attack allowing fake agent identities to inflate credit scores. The platform supports local models via Ollama or LM Studio as well as cloud API keys from providers like Groq and OpenAI, with no open ports required. Built on Next.js, Postgres, and Python with LangGraph, the project is open-source under Apache 2.0, though the creator notes it lacks a formal security audit and a hardened code-execution sandbox.

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ProgrammingDEV Community ·

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