Researchers Warn AI Agents Must Be Treated as Untrusted Systems or Security Will Fail
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Researchers from Google, Meta, UC San Diego and other universities publish a paper showing eleven real-world prompt-injection attacks that bypass model-level defenses every time and argue models must be treated as untrusted components rather than the primary security layer. They call for system-level protections—instruction data separation, least-privilege sandboxing and information-flow control—which has direct security and adoption implications for crypto services and AI-driven DeFi, DEX and CEX products.
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- Google and Meta researchers say AI model robustness alone cannot secure agentic systems.
- Eleven real-world attacks show prompt injection bypasses model-level defences every time.
- Agents need instruction data separation, least privilege sandboxing and information flow control.
A research paper from scientists at Google, Meta, UC San Diego, and several universities has taken a direct position that challenges how the industry currently approaches AI agent security.
The paper, titled Agent Security Is a Systems Problem, argues that treating AI models as the primary security layer is fundamentally insufficient. The model powering any agent must instead be treated as an untrusted component, the same way an operating system treats an external process, with security enforced at the system level around it.
“Efforts to increase model robustness are insufficient on their o…
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