AI Agent Security Lab

Security reviews for AI agents before they reach production

Agent Security Audit focuses on the operational risks teams hit after the demo: prompt injection, tool permissions, data exfiltration, RAG trust, audit logs, approval gates, and launch blockers.

Tool boundaryRead, write, send, delete, approve Attack surfacePrompt injection and tool-chain risk Evidence trailTraces, logs, rollback, review Launch gateBlockers, approvals, escalation

AI Agent Security

Prompt injection, MCP, data leakage, tool permissions, and production incidents.

Open security topic

Readiness Self-Assessment

A fast first-pass check for teams preparing an agent for real users.

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Sample Audit Report

See the kind of evidence, findings, and roadmap a formal review should produce.

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AI Agent Readiness, Security, and Tooling Guides

Agent Security Audit helps teams make AI agents, RAG systems, and tool-using LLM applications safer before they reach real users. The focus is practical: readiness checks, prompt injection testing, framework selection, guardrail review, observability, cost control, and production launch risk.

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Core resource hubs

Foundational guides

Common problems we cover

  • Security: prompt injection, data leakage, unsafe tool permissions, secrets handling, and sandbox boundaries.
  • Reliability: task success, regression testing, RAG grounding, citation quality, fallback behavior, and incident response.
  • Operations: trace review, cost control, latency, human approval gates, audit logs, and release readiness.
  • Tool choice: framework selection, vendor comparison, open-source tradeoffs, and when an agent is the wrong tool.

Recommended paths

Recent field notes

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Contact

For audit requests, partnership notes, or a question about an AI agent launch, email support@ibbs.ai.

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