Large language models introduce new security challenges, including prompt injection, data exposure, and misuse of model functionality. An AI gateway gives you a defense-in-depth way to mitigate these risks, so you can run generative AI safely in production.
As enterprises scale AI deployments, AI security has moved from a theoretical concern to an urgent one. AI-native systems now connect to sensitive internal APIs, take real-world actions, and interact with regulated data. The OWASP Top 10 for LLM Applications gives you a framework for understanding these risks.
This post covers five of the most common OWASP risks seen in the field, and how an AI gateway offers a security enforcement layer that governs outbound LLM traffic with precision, accountability, and policy-based controls.
The 10 OWASP LLM Risks
For context, here’s the full list:
- Prompt Injection
- Sensitive Information Disclosure
- Supply Chain Vulnerabilities
- Training Data Poisoning
- Improper Output Handling
- Excessive Agency
- System Prompt Leakage
- Embedding/Vector Weaknesses
- Misinformation
- Unbounded Consumption
Here’s a closer look at the five that an AI gateway addresses most directly:
1. Unbounded Consumption
The Risk: AI workloads are unpredictable. A single prompt can fan out into recursive requests or trigger heavy API calls. OWASP calls this “denial of wallet,” where excessive consumption creates real business risk through ballooning costs or resource exhaustion.
Real World: In real deployments, LLM agents can loop endlessly across APIs like OpenAI or Google Maps, racking up thousands of dollars in usage or straining infrastructure within minutes.
How an AI Gateway Helps: A client-side limiting flow enforces per-request token quotas, burst rate limits, and fair queuing. You can allocate usage budgets per agent or application tier and monitor them in real time, aligning consumption with cost accountability.
2. Excessive Agency
The Risk: AI agents often operate autonomously, selecting tools and executing real API calls. Many platforms don’t sufficiently control which tools an agent can access or under what conditions.
Real World: A widely reported example from Simon Willison showed an AI agent using GitHub’s MCP plugin exploited to access private repositories through a tool with overly broad access, a textbook case of excessive agency.
How an AI Gateway Helps: Endpoint access control and MCP access policies let you define precise access rules by tool, method, user, or request context. Tools can be explicitly gated, scoped per customer, and dynamically enabled or disabled, so no agent calls sensitive internal APIs without explicit authorization.
3. Prompt Injection
The Risk: LLMs are vulnerable to adversarial inputs, crafted text that hijacks the model’s behavior. This can override system prompts, exfiltrate data, or force unintended API calls. OWASP ranks this risk at the top of its list.
Real World: Users have embedded hidden instructions in natural-looking queries to jailbreak models or manipulate agent behavior in complex chains.
How an AI Gateway Helps: A data sanitation flow inspects all outbound traffic, filtering payloads for known injection patterns and suspicious structures. Combined with a transform flow, you can rewrite or reject prompts based on compliance, source, or structure, enforcing semantic boundaries before a prompt ever reaches the model.
4. Sensitive Information Disclosure
The Risk: LLMs can inadvertently leak private or regulated data, either through prompts that contain personally identifiable information (PII) or through completions that echo sensitive material.
Real World: Some companies have discovered their prompts included customer account information, secrets, and even bearer tokens, all sent to third-party LLM providers without redaction.
How an AI Gateway Helps: A data sanitation flow and header filtering policies strip secrets, redact PII, and log outbound payloads for auditing. You can apply pattern-based scrubbing to both request and response data, so sensitive fields never transit to external LLMs unless explicitly whitelisted.
5. Improper Output Handling
The Risk: LLM completions can include unsafe commands, untrusted code, or malformed responses. When applications trust those outputs blindly, for example passing them straight to other tools, unexpected behavior or data corruption can follow.
How an AI Gateway Helps: A transform flow intercepts and validates LLM outputs before any downstream system acts on them. Responses can be normalized, sanitized, or blocked entirely based on business rules, giving you last-mile enforcement at the point of response delivery.
Why This Matters: The Egress Layer Is the Security Layer
Most organizations focus on model safety: prompt engineering, evals, fine-tuning. But the real-world risks OWASP identifies live at the integration points between the LLM and the outside world, not inside the model itself.
That’s why egress control matters: governing the requests your systems send to models, the APIs your agents can call, and the payloads that flow through both.
As agentic security frameworks mature, every organization needs:
- Consumption controls to manage cost and scale
- Access controls to prevent agent misuse
- Data policies to protect information boundaries
- Traffic observability to detect and trace issues across LLM pipelines
This AI gateway capability comes from Lunar.dev, now part of Boomi following Boomi’s acquisition of Lunar.dev. Learn more about how Boomi secures MCP and AI agent traffic in production.