https://runwall.vercel.app/assets/AiAgentSecurity-CLqzG3Tb.js

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They don't just generate text; they take actions with real consequences. This makes them high-value targets for adversarial attacks: prompt injection that hijacks agent behavior, tool poisoning that exfiltrates data through malicious tool definitions, data exfiltration across multi-step workflows, and runaway loops that drain resources.","Securing AI agents requires a new category of defense — execution governance — that evaluates every agent action in context before it executes. Runwall provides this as a zero-trust governance layer for MCP-based agents: every tool call is verified against identity, evaluated against policy, scored for risk, checked for data taint, and logged immutably. No action is trusted by default."],sections:[{heading:"Prompt Injection Defense",body:"Prompt injection is the most discussed threat to AI agents: adversarial instructions hidden in data the agent processes (web pages, files, messages) that attempt to hijack the agent's behavior. Runwall defends against prompt injection through session-level taint tracking — when an agent ingests untrusted input, the session is flagged and downstream destructive actions are restricted, even if the injected content is paraphrased or reformulated by the agent."},{heading:"Tool Poisoning Prevention",body:"Tool poisoning exploits the trust between agents and their tools: a malicious tool definition can trick the agent into including sensitive data in parameters, or a tool can perform operations beyond what its name suggests. Runwall's Tool & MCP Registry requires explicit tool registration and trust verification. Unregistered tools are blocked, and registered tools can have parameter-level validation rules."},{heading:"Data Exfiltration Control",body:"AI agents naturally move data between tools as part of their workflow. Taint tracking ensures that when sensitive data enters an agent's session, all downstream write operations are governed — preventing the agent from forwarding, summarizing, or redistributing sensitive information to unauthorized destinations."},{heading:"Runaway Loop Protection",body:"Autonomous agents can enter infinite loops through logic errors or adversarial prompting, executing thousands of tool calls and draining resources. Runwall enforces per-agent, per-tool rate limits and budget quotas that hard-block further calls when limits are exceeded."}],faqs:[{question:"How do I secure my AI agent?",answer:"Use an execution governance layer like Runwall that sits between your agent and its tools. Every tool call should be verified against policy, risk-scored, checked for data taint, and logged. Key defenses include taint tracking for prompt injection, a verified tool registry for tool poisoning, rate limits for runaway loops, and approval workflows for high-risk actions."},{question:"What is the biggest security risk with AI agents?",answer:"Prompt injection is the most broadly applicable threat — adversarial instructions in data the agent processes can hijack its behavior. But the full threat model also includes tool poisoning, data exfiltration through multi-step workflows, and runaway loops. Effective security requires defense against all four vectors."},{question:"Can I use Runwall to secure agents I didn't build?",answer:"Yes. Runwall operates as a transparent MCP proxy. Any MCP-compatible agent — Claude Code, Codex, Cursor, Cline, or custom agents — can be governed by Runwall without code modifications. You simply point the agent's MCP configuration to the Runwall endpoint."},{question:"Is zero-trust necessary for AI agents?",answer:"Yes. AI agents are non-deterministic — the same input can produce different action sequences. This means you cannot pre-certify an agent as safe; you must evaluate each action at runtime. Zero-trust execution governance (verifying every action, trusting none by default) is the appropriate security model for autonomous AI systems."}],relatedLinks:[{label:"Threat Model",to:"/security/threat-model"},{label:"Taint Tracking Documentation",to:"/docs/taint"},{label:"Agent Security Research",to:"/research/agent-security"},{label:"AI Agent Firewall",to:"/ai-agent-firewall"},{label:"Coding Agents Use Case",to:"/use-cases/coding-agents"},{label:"Getting Started",to:"/docs/getting-started"}]};function u(){return e.jsx(t,{data:a})}

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