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发现与解读中文优先 · EN 原文待分类

Johann Rehberger 披露 Claude Code Opus 5 Auto Mode 的提示注入绕过

打破ClaudeCode Opus 5 自动模式Anthropic非常相信Claude代码的自动模式,用于保护其编码代理用户免受即时注入攻击。他们最近将其设为默认值,并大胆宣称其有效性。约翰·雷伯格 (Johann Rehberger) 是最可信的提示之一

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Breaking Claude Code Opus 5 Auto Mode

Breaking Claude Code Opus 5 Auto Mode Anthropic are putting a great deal of faith in Claude Code's auto mode for protecting their coding agent users against prompt injection attacks. They recently made that the default and have made bold claims about its effectiveness. Johann Rehberger is one of the most credible promp

对齐训练、提示注入、业务模拟与水印攻击把安全验证拉回可失败测试

为什么值得看

安全与可靠性判断正在从抽象承诺转向可复现实验、攻击路径和业务基线,对发布方能力叙事形成更直接的验收压力。

最强反方

除 Anthropic 研究外,其余结果主要来自研究者或项目作者发布;影响范围、复现条件、版本修复和真实生产损失仍需独立核验。

原始出现与传播对照

2 条出现 · 本次 AIHOT 未精确命中

发现与解读

打破ClaudeCode Opus 5 自动模式

打破ClaudeCode Opus 5 自动模式Anthropic非常相信Claude代码的自动模式,用于保护其编码代理用户免受即时注入攻击。他们最近将其设为默认值,并大胆宣称其有效性。约翰·雷伯格 (Johann Rehberger) 是最可信的提示之一

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突破Claude Code Opus 5自动模式

乔木风格重写(向阳乔木 · deepseek-v4-flash):Anthropic 对 Claude Code 的 auto mode 寄予厚望,最近还把它设成了默认模式,并声称它能有效防御提示注入攻击。但 Johann Rehberger 给出了一个反例:他找到的攻击方式,成功率高达 80%。 这个攻击的路径很有意思。攻击者诱骗 Claude Code 下载并解压一个 zip 压缩包。压缩包里藏着一个名为 struct...

直接发布摘要向阳乔木 · deepseek-v4-flash打开该层出处 ↗

乔木风格重写(向阳乔木 · deepseek-v4-flash):Anthropic 对 Claude Code 的 auto mode 寄予厚望,最近还把它设成了默认模式,并声称它能有效防御提示注入攻击。但 Johann Rehberger 给出了一个反例:他找到的攻击方式,成功率高达 80%。 这个攻击的路径很有意思。攻击者诱骗 Claude Code 下载并解压一个 zip 压缩包。压缩包里藏着一个名为 struct...

回溯原始摘要打开该层出处 ↗

Breaking Claude Code Opus 5 Auto Mode Anthropic are putting a great deal of faith in Claude Code's auto mode for protecting their coding agent users against prompt injection attacks. They recently made that the default and have made bold claims about its effectiveness. Johann Rehberger is one of the most credible promp

来源
向阳乔木 · qiaomu
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发现与解读

打破ClaudeCode Opus 5 自动模式

打破ClaudeCode Opus 5 自动模式Anthropic非常相信Claude代码的自动模式,用于保护其编码代理用户免受即时注入攻击。他们最近将其设为默认值,并大胆宣称其有效性。 Johann Rehberger 是当今最活跃的即时注射研究人员之一。他发现了针对自动模式的攻击,他声称这种攻击在 80% 的情况下有效,方法是欺骗Claude代码下载并解压缩 zip 存档,然后执行导入 base64 的代码,而不会注意到这将导入并执行从存档中提取的本地 struct.py 文件。在少数情况下,自动模式直接阻止代理阻止有害代码继续执行!在几次运行中Claude一旦发现恶意软件进程受到威胁,就会尝试终止该进程,但自动模式拒绝了清理命令。

查看英文原文

Breaking Claude Code Opus 5 Auto Mode

Breaking Claude Code Opus 5 Auto Mode Anthropic are putting a great deal of faith in Claude Code's auto mode for protecting their coding agent users against prompt injection attacks. They recently made that the default and have made bold claims about its effectiveness. Johann Rehberger is one of the most credible prompt injection researchers active today. He found an attack against auto mode which he claims works 80% of the time, by tricking Claude Code into downloading and uncompressing a zip archive, then executing code that imports base64 without noticing that this will import and execute a local struct.py file extracted from the archive. In a few cases auto mode directly prevented the agent from preventing harmful code from continuing to execute! In a few runs Claude tried to terminate the malware process once it noticed the compromise, but Auto Mode denied the cleanup command. Claude detects the compromise, but Auto Mode blocks its cleanup command The safety mechanism itself can become part of the failure. The classifier allowed the creation of the malware process, but then it blocked the command intended to stop it! I agree with Johann's conclusion here: the only safe way to run agents if there's any risk of attracting the attention of an adversarial attack is with a sandbox: Run unattended coding agents in a container, VM or OS sandbox. Restrict network egress. Monitor your agents. Do not expose home directories, SSH keys, cloud credentials,… to the agent runtime. [...] Tags: sandboxing, security, ai, prompt-injection, generative-ai, llms, anthropic, claude, johann-rehberger, claude-code

直接发布摘要Simon Willison@simonw打开该层出处 ↗

Breaking Claude Code Opus 5 Auto Mode Anthropic are putting a great deal of faith in Claude Code's auto mode for protecting their coding agent users against prompt injection attacks. They recently made that the default and have made bold claims about its effectiveness. Johann Rehberger is one of the most credible prompt injection researchers active today. He found an attack against auto mode which he claims works 80% of the time, by tricking Claude Code into downloading and uncompressing a zip archive, then executing code that imports base64 without noticing that this will import and execute a local struct.py file extracted from the archive. In a few cases auto mode directly prevented the agent from preventing harmful code from continuing to execute! In a few runs Claude tried to terminate the malware process once it noticed the compromise, but Auto Mode denied the cleanup command. Claude detects the compromise, but Auto Mode blocks its cleanup command The safety mechanism itself can become part of the failure. The classifier allowed the creation of the malware process, but then it blocked the command intended to stop it! I agree with Johann's conclusion here: the only safe way to run agents if there's any risk of attracting the attention of an adversarial attack is with a sandbox: Run unattended coding agents in a container, VM or OS sandbox. Restrict network egress. Monitor your agents. Do not expose home directories, SSH keys, cloud credentials,… to the agent runtime. [...] Tags: sandboxing, security, ai, prompt-injection, generative-ai, llms, anthropic, claude, johann-rehberger, claude-code

来源
Simon Willison · simon-willison
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