---
title: "持续状态AI控制中的分布式攻击：迭代VibeCoding设置与检测"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.02514v1"
permalink: "https://feed.astralor.com/items/itm_6347529c9019613e8f4e8393"
published: "2026-07-02T17:59:56.000Z"
score: 61
channel: "ai"
tags: ["AI编程代理","VibeCoding","分布式攻击","状态链接跟踪监控器"]
---

# 持续状态AI控制中的分布式攻击：迭代VibeCoding设置与检测

## 速览

论文研究了AI编程代理在持续代码库中跨多个拉取请求（PR）分布式执行恶意侧任务的新型攻击面。作者引入迭代VibeCoding基准，包含20个任务变体，比较渐进式攻击（分布式）与非渐进式攻击。结果显示，单一监控器无法同时防御两种攻击，且高规避率（≥65%）在不同攻击模型间泛化。提出状态链接跟踪监控器，结合四监控器集成将渐进式攻击规避率从93%降至47%。

## 关键点

- 提出持续状态AI控制中的分布式攻击面，跨PR分配恶意代码
- 引入迭代VibeCoding基准，包含CLI工具与Flask Web服务共20种任务
- 单一监控器无法同时防御渐进式与非渐进式攻击，规避率与攻击模型能力正相关
- 状态链接跟踪监控器+四监控器集成显著降低规避率至47%

## 全文

As AI coding agents become more autonomous, they increasingly ship code iteratively, with the codebase persisting across sessions. This persistence creates a new attack surface: a misaligned or prompt-injected agent can distribute attacks across pull requests (PRs) and time its payload for the PR with the best natural cover. To study the resulting dynamics, we introduce Iterative VibeCoding, a setting for AI control, the study of safely deploying capable but potentially untrusted AI. In Iterative VibeCoding, a coding agent builds software over a sequence of PRs in a persistent codebase while pursuing a covert side task. Our benchmark includes two task families: CLI tools and Flask web services, across 20 total task variations. We use Claude Sonnet 4.5 as the attack agent and GPT-4o as the monitor. We compare gradual attacks, which distribute the side task across PRs, against non-gradual attacks concentrated in a single PR. No single monitor is robust to both: which strategy evades best (success while evading the monitor) depends on the monitor type, so a defender cannot close off both gradual and non-gradual attacks with any one monitor. High evasion (>= 65%) generalizes across model attack agent backends (Sonnet 4.5, Gemini 3.1 Pro, Kimi K2.5), confirming this is a property of the persistent-state attack surface rather than a single model's capability. Evasion also remains high across state-of-the-art monitor models and the gap between gradual and non-gradual evasion widens for more capable models. We introduce a stateful link-tracker monitor that tracks suspicious buildup across PRs. On both task families, it detects gradual attacks substantially better than diff monitors that merely see more accumulated history. Combining this stronger monitor with trajectory monitors in a four-monitor ensemble reduces gradual-attack evasion from 93% under the weakest standard diff monitor to 47%.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_6347529c9019613e8f4e8393