Frameworks help when they turn into controls. Otherwise they become slides that nobody uses.
The OWASP LLM Top 10 gives teams a shared language for GenAI risk. This post translates the key items into practical actions for enterprise AI agents and RAG systems.
Why this matters more for agents and RAG
Agents can call tools. RAG systems pull in content. Both expand your attack surface.
- Agents: tool calls can change systems.
- RAG: retrieved text becomes part of the model’s context.
That combination creates predictable failure modes: injection, tool misuse, data leakage, and weak traceability.
The risks that hit enterprise teams hardest
You don’t need to memorize ten items to get value. You need to focus on the risks that show up in real deployments.
1) Prompt injection and indirect prompt injection
Attackers embed instructions in content the system reads: tickets, docs, emails, web pages. The model treats it as guidance and may follow it.
Practical controls:
- Treat retrieved content as untrusted input.
- Constrain tools with strict schemas and allowlists.
- Put a policy gate before any tool call.
- Red-team with injected content.
Related: Prompt Injection Prevention for AI Agents
2) Insecure output handling
This shows up when agent outputs get executed, stored, or forwarded without validation. For example: a model output becomes an email, a SQL query, or a system update.
Practical controls:
- Validate outputs before execution.
- Use structured outputs, not free text, for high-impact actions.
- Sanitize and filter sensitive data in outputs.
3) Sensitive information disclosure
Enterprises often leak data through “helpful” outputs. The model may reveal restricted content from retrieved documents or tool results.
Practical controls:
- Enforce identity-based retrieval permissions.
- Mask sensitive fields in tool results.
- Use least privilege and scope down data domains.
4) Tool and plugin supply chain risk
Connectors, plugins, and tool servers act like privileged integrations. If you add tools without governance, you create a fast path for abuse.
Practical controls:
- Allowlist tools and pin versions.
- Require security review before enabling new tools.
- Log every tool call and alert on anomalies.
Related: Tool and Connector Security for Agentic AI
Owner map (who should do what)
- Security: threat model, policy gates, approvals, incident response
- Platform: identity, tool routing, logging, environments
- App teams: workflow scope, output validation, user experience
- Data teams: retrieval permissions, data classification, masking
Week-one implementation plan
- Pick one workflow that matters and scope it tightly.
- List the tools it needs. Kill everything else.
- Add approval gates for high-impact actions.
- Add logging for the full tool chain.
- Run a red-team session using injected content.
Read next: Agentic AI Security Checklist for Enterprise Workflows
