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AI Security

AI Security Assessments

Securing AI systems from model to agent. Agent47 Labs evaluates the security of AI-enabled applications, large language models, retrieval systems, tool integrations, and agentic workflows — testing the trust boundaries between prompts, models, data, tools, infrastructure, and humans. Assessments combine human-led offensive testing with evidence-backed validation of weaknesses such as prompt manipulation, data exposure, insecure tool use, retrieval trust, and agent authorization.

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What we test

AI & LLM Application Security

Assess applications that integrate large language models or other AI capabilities for weaknesses in how prompts, context, model output, application logic, and sensitive data are handled — and how those weaknesses affect the wider application's trust boundaries.
  • Prompt injection & context manipulation
  • Guardrail & output-handling bypass
  • Sensitive-data exposure
  • Authorization around AI functionality

RAG, Data & Tool Security

Assess how AI applications retrieve information, access data, and invoke external tools or services — including retrieval trust, sensitive-data exposure, and the boundaries between the model and the external systems it reaches, of which MCP is one integration pattern.
  • RAG / retrieval trust
  • Poisoned or malicious context
  • Tool-use & plugin security
  • MCP integration security

Agentic AI Security

Assess systems that can plan, invoke tools, take actions, coordinate workflows, or operate with varying levels of autonomy — examining privilege boundaries, infrastructure access, and where human approval is required. For deeper methodology, explore our Agentic AI Security deep dive.
  • Agent authorization boundaries
  • Tool permissions & action chains
  • Agent-to-agent workflows
  • Human approval & escalation controls

What we validate

  • Prompt & Context Boundaries

    Evaluate how instructions, system context, user input, and retrieved content can influence model behavior and application decisions.

  • Retrieval & Data Exposure

    Assess whether retrieval systems, application context, and AI workflows expose or misuse sensitive information.

  • Tools, Integrations & Authorization

    Validate how AI systems invoke tools, plugins, MCP servers, APIs, and other integrations — and whether authorization boundaries are actually enforced.

  • Agent & Human Control Boundaries

    Assess agent permissions, workflow escalation, and the controls that determine when human approval is required before higher-impact actions occur.

Ready to test your AI system?

Assess the security of AI applications, LLMs, retrieval systems, tool integrations, and agentic workflows through human-led offensive testing.

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