VQV Signal
Agentic AI systems combining LLM-driven planning with external tools face risks of data leakage and misuse through instruction boundary failures and prompt injection attacks. A new pre-deployment pipeline is proposed to enforce controls consistently across multi-agent workflows to mitigate these vu...
Agentic AI systems combining LLM-driven planning with external tools face risks of data leakage and misuse through instruction boundary failures and prompt injection attacks. A new pre-deployment pipeline is proposed to enforce controls consistently across multi-agent workflows to mitigate these vu...
AI-assisted summary based on the listed source.
Agentic systems integrate LLM driven planning with interfaces to external tools, making data leakage and tool misuse feasible via instruction/data boundary failures and prompt injection attacks. Enforcing required controls consistently is particularly challenging in workflows spanning many codebases and...
As agentic AI systems grow more complex and interconnected, preventing data leakage and misuse becomes critical to maintaining security and trust. This approach addresses the challenge of enforcing security controls across diverse codebases and agents before deployment.
Security-conscious readers may want to review the source and watch for practical exposure or mitigation details.
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VQV surfaced this signal because it is recent, relevant to AI Security, connected to arXiv.
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