这篇论文揭示了使用共享技能库构建智能体时可能存在的安全风险，特别是EvoMal恶意软件的传播方式。它提供了关于如何减少这种风险的见解，值得一读。
共享技能库被用作编码智能体复用彼此工作的安全方式，但研究发现它们会传播恶意软件。EvoMal在库中植入恶意技能，但从不调用它。智能体将其作为创作模板检索，编写新技能以保留有效载荷，存储并运行。每个创作的副本重新进入库并再次被模仿。在153个与工具相关的SWE-bench验证任务中的六个模型上，智能体自我中毒率在20.3%到41.8%之间。被毒化的库最终持有的恶意技能数量是植入数量的4.9到9.0倍。删除所有植入的技能并不能清理干净。Qwen3在第五轮仍显示68%，因为智能体创作的副本仍然存在。一个反提示，鼓励不使用横幅式复制，将其降至6.7%，且没有显著的任务完成损失。论文：arxiv.org/abs/2608.25776
Important read if you build with agent skills. Shared skill libraries are treated as a safe way for...
Important read if you build with agent skills. Shared skill libraries are treated as a safe way for coding agents to reuse each other's work. New research shows they propagate malware. EvoMal plants a malicious skill in the library and never invokes it. The agent retrieves it as an authoring template, writes a new skill that preserves the payload, stores it, and runs it. Each authored copy re-enters the library and gets imitated again. Across six models on 153 tool-relevant SWE-bench Verified tasks, the agent self-poisoning rate runs 20.3% to 41.8%. Poisoned libraries end up holding 4.9 to 9.0 times as many malicious skills as were planted. Deleting every planted skill does not clean it up. Qwen3 still shows 68% at round five because the agent-authored copies remain. A counter-prompt that discourages banner-style copying drops it to 6.7% with no significant task-completion loss. Paper: arxiv.org/abs/2608.25776 Chat with Paper: academy.dair.ai/papers/evomal-… 💬 1 🔄 1 ❤️ 13 👀 1906 📊 4 ⚡