In an internal red team test before Claude Opus 4.6's release, Anthropic's Frontier Red Team did something brutally simple: they put Opus 4.6 in a sandbox environment, gave it Python and a set of standard vulnerability analysis tools, provided no specialized instructions or domain knowledge injection, and let it mine open-source repositories for vulnerabilities on its own. The result: over 500 previously unknown high-severity zero-day vulnerabilities. This number has led many security practitioners to joke, half-seriously, that "we're about to be replaced by AI." This topic deserves a serious conversation.

Simply by deploying Clawdbot and conversing with it, your computer could be completely controlled by attackers. This is a fundamental architectural problem, not a bug—it's a "feature". This article systematically analyzes the root causes of this risk, the conditions required for a successful attack, and why existing defenses can only mitigate, but never eliminate, the threat.

Large Language Models (LLMs) are evolving from simple conversational tools into intelligent agents capable of writing code, operating browsers, and executing system commands. As LLM applications advance, the threat of prompt injection attacks continues to escalate. Imagine this scenario: you ask an AI assistant to help write code, but it suddenly starts executing malicious instructions, taking control of your computer. This seemingly science fiction plot is now becoming reality. This article introduces a novel prompt injection attack paradigm. Attackers need only master a set of "universal triggers" to precisely control LLM outputs to produce any attacker-specified content, thereby leveraging AI agents to achieve high-risk operations like remote code execution.