When an LLM agent controls real money, prompt injection stops being an information risk and becomes a financial one—and its hardest form is the confused deputy: paying is the legitimate job and only the recipient or amount is attacker-supplied, so an instruction-level safeguard has nothing to refuse. Across five models (Claude, Qwen, MiniMax) a normally-built agent is driven to a forbidden money action on up to 87% of such scenarios—and a hardened refusal prompt still loses up to 84%. Like prior work on policy enforcement outside the model (e.g. CaMeL), we take the model off the money path rather than trust it to refuse; we contribute the first open, installable, end-to-end realization of this for agent payments: BNB Agent Studio, an open-source toolchain (CLI, Python SDK, MCP server) that takes a developer from an empty directory to a deployed, self-funding LLM agent in six commands. Its two-layer, keyless-edge architecture keeps signing as fixed code isolated from the model context, prices deterministic, and spend guardrails enforced below the LLM: an identical agent fooled at a comparable rate moves funds on 0/45 everywhere, and a live testnet deployment confirms escrow, pricing, key-access, and suspension on-chain. The toolchain implements the Agent Lifecycle Protocol (ALP), a vendor-neutral draft spec composing ERC-8004 identity, ERC-8183 task commerce, x402 payments, and A2A. It is on PyPI (pip install bnbagent-studio, Apache-2.0), with a screencast at https://youtu.be/yjdvN8vE37A.

Code translation benchmarks are essential for evaluating the accuracy and efficiency of LLM-based systems. Existing benchmarks mainly target individual functions, overlooking repository-level challenges like intermodule coherence and dependency management. Recent repository-level efforts exist, but suffer from poor maintainability and coarse evaluation granularity. We introduce Skeleton-Guided-Translation, a framework for benchmarking Java-to-C# translation at the repository level, featuring fine-grained quality evaluation. It follows a two-step process: first translating repository “skeletons”, then refining the entire repository guided by these skeletons. Based on this, we present TRANSREPO-BENCH , the first test-driven benchmark of high-quality Java repositories paired with C# skeletons, unit tests, and build configurations. Our adaptive unit tests support multiple and incremental translations without manual tuning, enhancing automation and scalability. We also propose fine-grained metrics that evaluate translation quality per test case, overcoming limitations of binary metrics in distinguishing build failures. Evaluations using TRANSREPO-BENCH reveal issues like broken cross-file references, showing that our structured approach reduces dependency errors and preserves interface consistency.