Chronos漏洞：智能AI中基于时间持久性和内存欺骗的分类法
The Chronos Vulnerability: A Taxonomy of Temporal Persistence and Memory-Based Deception in Agentic AI
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中文总结 AI 辅助
研究人工智能中Chronos漏洞，形式化基于持久性攻击的威胁模型，指出传统端点内容过滤器不足，综合深度防御格局并分类新兴框架，应对智能体内存攻击等安全威胁。
中文摘要 AI 辅助
人工智能从无状态生成模型向有状态自主智能体的转变带来了新安全威胁——Chronos漏洞。它包括基于内存的攻击，如内存注入攻击和潜伏智能体，会损害自主智能体内部信念系统。本研究在工作流基准环境中形式化了基于持久性攻击的威胁模型和动态失明威胁，表明传统端点内容过滤器不足。进而综合了深度防御格局，对新兴框架进行分类。
英文摘要
The transition from stateless generative models in artificial intelligence to stateful, autonomous agents represents an architectural evolution that, while providing the capabilities of long-term planning and the automation of enterprise workflows, also represents the introduction of a new form of security threat, the Chronos Vulnerability. The Chronos Vulnerability represents the threat of memory-based attacks, including the Memory Injection Attack (MINJA) and the sleeper agent, in which the internal belief system of the autonomous agent is compromised, effectively decoupling the attack vector from the final catastrophic event. This study formalizes the threat model for persistence-based attacks and the threat of Dynamics Blindness in the context of the World of Workflows benchmark, demonstrating that traditional endpoint content filters are insufficient for the current stateful architecture. Consequently, this study synthesizes a defense-in-depth landscape, categorizing emerging frameworks such as diagnostic trajectory guardrails (AgentDoG), formal temporal verification (Agent-C), immunological memory consensus (A-MemGuard), and hardware-anchored trust via GPU-based Trusted Execution Environments (TEEs) and Zero-Trust memory architectures.
