VQV Signal
AI agents use skills that bundle instructions and resources, producing artifacts like files and cached data that persist across sessions. These persistent artifacts create a new attack surface, allowing potential cross-session attacks when accessed by other skills later.
AI agents use skills that bundle instructions and resources, producing artifacts like files and cached data that persist across sessions. These persistent artifacts create a new attack surface, allowing potential cross-session attacks when accessed by other skills later.
AI-assisted summary based on the listed source.
Agent skills extend the capabilities of AI agents by bundling instructions, code, and resources into reusable packages. During skill execution, agents may produce or modify artifacts, such as files, configurations, and cached data, that are reused by other skills in later sessions. Since these artifacts can...
Understanding this vulnerability is crucial for securing AI agents as they increasingly rely on reusable skill packages. Mitigating risks from persistent artifacts helps prevent unauthorized access and manipulation across agent sessions.
VQV organizes public signals from inspectable sources. It does not independently verify the underlying report.
Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.
VQV surfaced this signal because it is recent, relevant to AI Agents, connected to arXiv.
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