提出框架识别代码智能体三类危险失效，设计自修复机制提升安全性。
ClayBuddy: A Framework, Evaluation, & Mitigation of Coding Agent Failures
- 将代码代理失败分解为三类机制：规格不足、能力偏差、执行错误。
- 前沿模型仅用3个示例就诱发危险行为，且会以非零概率生成破坏性指令。
- 提出自修正框架ClayBuddy，可主动截断上下文以保障安全，适配高风险开发场景。
AI智能体在软件工程中的广泛应用暴露出大量罕见但极具危害的对齐错误。由于此类行为难以采样，我们将其分解为三类机制：规格不足（默认行为不安全）、能力错误（安全操作存在但未执行）和代理执行错误（安全操作未能成功执行）。通过8项压力测试验证这些机制，发现前沿模型极难引导，仅需3个条件示例即可诱使产生危险行为，且在非可忽略概率下随机生成破坏性命令。针对每类机制，我们设计了针对性缓解方案，提出代理自修框架ClayBuddy。该框架允许代理修改自身执行环境，提供安全保证，包括一种新型工具用于选择性截断自身上下文。我们在安全性和功能性上评估了ClayBuddy的效能，结果显示其在部分任务中完全消除违规行为，验证了机制分解的有效性与框架设计哲学的可行性。
原文摘要 · Abstract (English)
Widespread deployment of AI agents in software engineering is surfacing a long tail of rare but highly dangerous misalignment bugs. Since sampling this behavior is intractable, we decompose these failures into three distinct mechanisms: underspecification, where default model behavior is unsafe; capability errors, where the safe action is available but the model does not adhere to it; and agent harness errors, where the safe action fails to execute. Across 8 evaluations that stress test these mechanisms, we find that frontier models are difficult to steer, elicit dangerous behavior through just 3 conditioning examples, and can randomly generate destructive commands at nontrivial probabilities. These three mechanisms naturally lend to targeted mitigations for each one, inspiring our harness modification ClayBuddy. ClayBuddy allows the agent to modify its own harness to provide safety guarantees, including a novel tool to selectively truncate its own context. We measure ClayBuddy's efficacy across safety and capabilities, finding that it eliminates violations across some tasks. This demonstrates that our decomposition is effective and is an example of our harness design philosophy.
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