CapSeal: Capability-Sealed Secret Mediation for Secure Agent Execution
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Abstract (arXiv)
Modern AI agents routinely depend on secrets such as API keys and SSH credentials, yet the dominant deployment model still exposes those secrets directly to the agent process through environment variables, local files, or forwarding sockets. This design fails against prompt injection, tool misuse, and model-controlled exfiltration because the agent can both use and reveal the same bearer credential. We present CapSeal, a capability-sealed secret mediation architecture that replaces direct secret access with constrained invocations through a local trusted broker. CapSeal combines capability issuance, schema-constrained HTTP execution, broker-executed SSH actions, anti-replay session binding, policy evaluation, and tamper-evident audit trails. We describe a Rust prototype integrated with an MCP-facing adapter, formulate conditional security goals for non-disclosure, constrained use, replay resistance, and auditability, and define an evaluation plan spanning prompt injection, tool misuse, and SSH abuse. The resulting system reframes secret handling for agentic systems from handing the model a key to granting the model a narrowly scoped, non-exportable action capability.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 36 references that are in the map. First posted on arXiv 18 Apr 2026.
Builds on
14 of 36 references.
References · 14
- 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
- 2024AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Basescited by 402
- 2024Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Securitycited by 257
- 2025Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directionscited by 257
- 2025Defeating Prompt Injections by Designcited by 256
- 2025Prompt Injection Attack to Tool Selection in LLM Agentscited by 145
- 2024Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systemscited by 126
- 2024BadAgent: Inserting and Activating Backdoor Attacks in LLM Agentscited by 106
- 2024The Task Shield: Enforcing Task Alignment to Defend Against Indirect Prompt Injection in LLM Agentscited by 89
- 2025MELON: Provable Defense Against Indirect Prompt Injection Attacks in AI Agentscited by 67
- 2025IPIGuard: A Novel Tool Dependency Graph-Based Defense Against Indirect Prompt Injection in LLM Agentscited by 46
- 2026ClawKeeper: Comprehensive Safety Protection for OpenClaw Agents Through Skills, Plugins, and Watcherscited by 18
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Citing papers per year: 2026: 1.
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- 2026When Agents Handle Secrets: A Survey of Confidential Computing for Agentic AI
- 2026AgentKernel: The Trust-Native Agentic Operating System
- 2025AgentBound: Securing Execution Boundaries of AI Agents
- 2026CAPMAS: Capability-Based Delegation of Privileges in Multi-Agent Systems
- 2026Authenticated Workflows: A Systems Approach to Protecting Agentic AI
- 2026AgenticCyOps: Securing Multi-Agentic AI Integration in Enterprise Cyber Operations
- 2026A Policy Algebra for Trust-Preserving Agentic AI Execution
- 2026Verifiable Agentic Infrastructure: Proof-Derived Authorization for Sovereign AI Systems