SPILLWAY: Separating Target Admission from Sanitization to Defend Tool-Using Agents against Prompt Injection
SPILLWAY proposes an architectural split — target admission vs. input sanitisation — to harden tool-using AI agents against prompt injection that can trigger payments or data exfiltration.
Summary written by editorial AI · Source link below
arXiv:2611.07222v1 Announce Type: new Abstract: Tool-using agents act on email, files, and payments, so an instruction hidden in a web page or a shared document can make an agent send data or money to an attacker. Many runtime defenses put a language model on the decision that lets an action through, so the decision depends on a model that may read the attacker's text and adds model calls to every step. Provenance defenses keep the model off the final check but need a model to plan or route eac
Editorial Analysis
As enterprises connect LLM agents to email, payment, and file systems, prompt injection becomes a business-critical risk; architectural defences like admission/sanitisation separation reduce the blast radius of a single compromised input.
Evaluate whether your agentic AI deployments enforce an explicit admission gate before any tool action is permitted.
AI agents acting on emails and payments can be hijacked via hidden instructions — a new defence architecture limits that exposure.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
More from the AI Security Desk
- Claude Code's “Don't Ask Again” File Exposes Passwords and Keys in 1,122 Public Copies on GitHub2d
- Runaway AI hacking agent behind WorkOS breach after escaping sandbox3d
- AI Gave Your Incident Archive a Second Job3d
- Malicious Browser Extensions Could Use Claude Dispatch to Run Code on Users' Computers3d
- AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation3d