{"data":{"id":"f79a89c2-25a9-4579-8369-3fabec3f8e09","title":"Agentic AI Defense: Decode-Time Provenance Binding for MCP","summary":"This research shows that instructions hidden in an image, using LSB, DCT or metadata steganography, can bypass text scanners and enter a multi-agent MCP workflow when a tool decodes them. The authors propose decode-time provenance binding, which combines HMAC-authenticated trust labels, an MCP-maintained provenance ledger and context-taint tracking. In a testbed with 24 adaptive payloads, the undefended pipeline had 100% attack success, keyword and regex defenses each permitted 62.5%, and the proposed mechanism blocked all attacks.","solution":"Decode-time provenance binding: HMAC-authenticated trust labels, an MCP-maintained provenance ledger and context-taint tracking, which label decoded content as untrusted and block critical tool calls influenced by that content. Code and data are publicly available.","labels":["security","research"],"sourceUrl":"https://arxiv.org/abs/2610.31974v1","publishedAt":"2026-10-22T22:27:13.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["prompt_injection"],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":["Model Context Protocol (MCP)"],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":"2026-10-22T22:27:13.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity","confidentiality"],"aiComponentTargeted":"agent","llmSpecific":true,"classifierConfidence":0.95,"researchCategory":"preprint","atlasIds":["AML.T0051"]}}