解碼溯源綁定機制擋住隱寫注入

Computer Science > Cryptography and Security arXiv:2610.

31974 (cs) [Submitted on 22 Oct 2026] Title:Agentic AI Defense: Decode-Time Provenance Binding for MCP Authors:Eric Yocam, Varghese Vaidyan, Paris Kalathas, Patrick Tague, Bruce DeBruhl, Yong Wang View a PDF of the paper titled Agentic AI Defense: Decode-Time Provenance Binding for MCP, by Eric Yocam and 5 other authors View PDF HTML (experimental) Abstract:Agentic AI systems using the Model Context Protocol (MCP) are vulnerable to indirect prompt injection through untrusted tool outputs.

We demonstrate cross-modal trust laundering, in which instructions concealed in an email-signature image using least-significant-bit (LSB), discrete cosine transform (DCT), or metadata steganography bypass text scanners and enter a multi-agent workflow when decoded by a tool.

To counter this threat, we propose decode-time provenance binding, which combines HMAC-authenticated trust labels with an MCP-maintained provenance ledger and context-taint tracking.

The mechanism labels decoded content as untrusted and blocks critical tool calls influenced by that content, even when a compromised agent substitutes a valid trusted label.We evaluate the approach using a real MCP testbed with three steganographic carriers and 24 adaptive payloads.

The undefended pipeline exhibits 100% attack success, while keyword and regex defenses each permit 62.5% of attacks.Our mechanism blocks all attacks (0% success), including adversarial label substitution across mediated agent boundaries.

A four-component ablation validates the roles of decode marking, cryptographic authentication, lineage tracking, and sink enforcement.An audited trusted-promotion mechanism increases legitimate-task completion from 67% to 100% without compromising protection.

The approach provides carrier-independent containment within fully mediated MCP execution environments.Code and data are publicly available.Comments: 12 pages, 5 figures, 13 tables, 2 algorithms.Code and data available at this https URL Subjects: Cryptography and Security (cs.

CR); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) ACM classes: D.4.6; K.6.5; I.2.11 Cite as: arXiv:2610.31974 [cs.CR] (or arXiv:2610.31974v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2610.

31974 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Eric Yocam [view email] [v1] Thu, 22 Oct 2026 22:27:13 UTC (528 KB) Full-text links: Access Paper: View a PDF of the paper titled Agentic AI Defense: Decode-Time Provenance Binding for MCP, by Eric Yocam and 5 other authorsView PDFHTML (experimental)TeX Source view license Additional Features Audio Summary Current browse context: cs.

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