DEMO / RECORDING
📺 视频深度技术提炼 · VIDEO KEY TAKEAWAYS⏱️ 时长: 15 mins · @AICodeKing
核心主旨速览 (Core Takeaway)
全方位评测 Jev 与 Browser Use 的融合实操：通过将 DOM 树的交互元素抽取为结构化列表，不仅将网页自动化操作速度提升数倍，还展示了其对常见提示词注入攻击（Prompt Injection）的天然防御能力。
📌 关键章节时间戳 (Key Chapters)
00:00测试背景：为什么传统 Browser Agent 容易卡死03:40退款请求检测与工单分流基准07:15提示词注入（Prompt Injection）防御实测11:50Browser Use 真实网页自动化交互全流程💡 关键实操结论与提效数据
- 由于 Jev 仅输出受约束的 Logprob 选项分布，网页中隐藏的恶意 Prompt 注入极难劫持输出格式；
- 在包含多级表单和按钮的测试页中，DOM 选项模式成功率保持在较高水准；
- 与 Python Playwright 配合构建的 Agent 循环响应极其敏捷。
⚠️ 工程踩坑与边界提醒 (Gotchas)
- 完全依靠 Canvas 渲染的页面无法提取 DOM 节点，此时仍需视觉模型回退；
- 反爬虫验证码（CAPTCHA）仍需要外置专用验证码解决服务。
Overview & Result
AICodeKing benchmarks support routing, refund decision detection, prompt-injection resistance, and autonomous browser automation combining Browser Use with Jev.
How Jev fits in the loop
- The surrounding agent or application prepares a bounded state and candidate actions.
- Jev performs the security guardrail & action step decision described by the source.
- Application code executes the selected action and handles low-confidence or exceptional cases.
How to reproduce
- Open the linked source and verify the author, workflow, and claimed Jev role.
- Recreate the smallest bounded decision with your own inputs and credentials.
- Measure accuracy, latency, cost, and fallback behavior before production use.
Why this build matters
Validates incoming actions against injection patterns before navigation.
Limitations
- This directory entry summarizes the linked source and is not an independent benchmark.
- Reported results may not generalize to a different dataset, policy, or runtime.