Why securing LLM and agent traffic needs a different control point than the one you already have
Ship an agent that can call tools and you've shipped a new attack surface along with it. Not a hypothetical one. The moment a model can query a database, hit an internal API, or send an email on its own, a bad prompt stops being an annoying output and starts being an action. That's the part most teams underestimate when they assume their existing API gateway or WAF already has this covered.
It doesn't, and it was never designed to.
Syntax vs. meaning
A WAF is good at catching things that look wrong: malformed headers, SQL fragments, oversized payloads, known attack signatures. A prompt injection attack doesn't look wrong. It's normal text, in any language, that happens to instruct a model to ignore its system prompt or call a tool it has no business calling. The payload can also be planted somewhere the request itself never shows you, inside a retrieved document, a scraped webpage, or the output of a tool the agent already trusts.
That's the core problem. LLM and agent traffic needs to be inspected for intent, not just structure. A control point built for headers and payload size can't do that job, no matter how well it's tuned.
Agents raise the stakes
With a plain chatbot, a successful attack produces bad text. With an agent, the same attack can trigger a real tool call: a query against production data, an outbound request, a database write. And because agents chain steps together to complete a task, one compromised link in that chain can cascade into several unauthorized actions before anyone notices. Rate limiting alone won't flag this either, since the request volume can look completely ordinary.
NeuralTrust's breakdown of this exact gap goes deeper into where traditional infrastructure falls short and what a purpose-built AI gateway needs to inspect instead, and their agent security page covers the tool-calling threat model in more detail.
What OWASP already told us
The OWASP Top 10 for LLM Applications (2025) is the closest thing this space has to a shared threat model, and a handful of entries map directly onto what a gateway can realistically stop at request time:
- LLM01, Prompt Injection: caught by inline detection before the prompt reaches the model, combining pattern matching for known techniques with semantic classification for new ones.
- LLM02, Sensitive Information Disclosure: handled by PII detection and redaction on both the inbound prompt and the outbound completion.
- LLM06, Excessive Agency: addressed with per-agent, per-tool access scoping, so a manipulated agent can't reach further than the task actually requires.
- LLM07, System Prompt Leakage: caught by inspecting completions for content that resembles the system prompt itself.
- LLM10, Unbounded Consumption: controlled through rate limiting and quotas per user, agent, and endpoint.
Two items on the OWASP list, Supply Chain Risks and Data and Model Poisoning, sit upstream of runtime traffic. A gateway can restrict which models and versions an app is allowed to call, but it's not a substitute for governance earlier in the pipeline.
What actually belongs at the gateway
Strip it down to the controls that matter in production:
- Inline prompt and tool-call inspection, not after-the-fact logging
- PII detection and redaction, both directions
- Access control scoped to the token or agent, down to which tools and data sources it can touch
- Rate limits per user, per agent, per app, to cap both abuse and runaway agentic loops
- A tamper-resistant audit log of every prompt, tool call, and policy decision
- Fail-closed behavior, so a timed-out classifier blocks the request instead of waving it through
That last one is easy to overlook and expensive to get wrong. If your injection classifier times out and the default is to let traffic pass, every outage becomes a silent security hole.
Worth noting this is also where gateways diverge from guardrail libraries bolted onto a single application. Guardrails run inside your own process and need to be wired into every service separately. A gateway sits in front of all of them and applies one policy consistently, which NeuralTrust covers in more detail here.
A simplified policy might look something like this:
policy:
fail_closed: true
rules:
- type: prompt_injection
action: block
- type: pii_detection
scope: [inbound, outbound]
action: redact
- type: agent_tool_access
allowed_tools: [search_docs, read_ticket]
denied_tools: [send_email, execute_code]
- type: rate_limit
scope: per_agent
limit: 100
window: 1m
Declarative, enforced centrally, defaults to blocking. That's the pattern regardless of which product implements it.
The compliance angle isn't optional anymore
IBM's 2025 Cost of a Data Breach Report found that 13% of organizations reported a breach involving an AI model or application, and 97% of those had no AI access controls in place. Of the AI-related incidents, 60% led to compromised data and 31% caused operational disruption. That's not a hypothetical governance gap, it's the current baseline for teams shipping AI without runtime controls.
The EU AI Act adds a legal layer on top. Article 12 requires high-risk systems to support automatic event logging. Article 19 obligates providers to retain those logs. Article 26 requires deployers to keep logs for at least six months and make them available to regulators on request. A gateway that logs every prompt, completion, and policy decision by default satisfies most of this without every downstream team building logging from scratch.
Enforcement and assessment are different jobs
It's worth keeping two layers separate in your head. One layer enforces policy inline, on every request, in real time. The other assesses risk: which tools should an agent never be allowed to touch, where are the gaps in current permissions, what does adversarial testing against the agent's workflows actually reveal. NeuralTrust splits these into TrustGate and TrustGuard for exactly this reason, one enforces, the other informs what gets enforced. Whatever stack you're running, both jobs need to exist somewhere, and conflating them tends to leave gaps in both.
If you want a wider view of the agent security space beyond gateway enforcement, agentsecurity.com is a reasonable starting point for mapping the broader landscape.
The practical takeaway
If your LLM or agent traffic only passes through a conventional WAF or API gateway today, you're covered for the attacks those tools were built for and exposed for the ones that matter most in this space. Prompt injection, tool-call abuse, and PII leakage all live at the semantic layer, and that layer needs its own inline enforcement point, not another rule bolted onto infrastructure that was never meant to read meaning in the first place.
