Quint vs Lakera: When to Use Each for AI Agent Security

I get this question in almost every first call. "We've already got Lakera — do we still need you?" Short answer: usually yes, but not for the reason you'd think. They're not competing products. They live on different floors of the same building.

Lakera sits in the prompt path. Every time your app sends text to a model or gets text back, Lakera can read it. That's a useful vantage point for a specific class of problem.

We sit on the endpoint. When the model says "call read_file" and the agent actually does the reading, we're watching at the OS. That's a useful vantage point for a different class of problem.

If you stop reading now: ship an LLM product yourself, Lakera is probably your tool. Have a fleet of developers running Claude Code or Cursor, we're probably yours. Have both, you probably want both. No, really — most of our bigger design partners also run Lakera. That's fine. It's not a land grab.

What Lakera is genuinely good at

Prompt injection detection is hard. Harder than it looks from the outside. Lakera Guard has been at it longer than almost anyone and it shows — their classifier catches things, including some of the trickier indirect injection shapes where the payload is baked into a PDF or a tool response.

Gandalf, their prompt-injection puzzle, is also just a great piece of developer education. More engineers learned what prompt injection is from playing Gandalf than from reading OWASP. That kind of thing matters.

Their red-teaming product lets you probe your own LLM app before shipping. That's a net good. Offensive testing before production always is.

Where the prompt layer ends

Here's the part that gets compressed in sales decks. Lakera reads the text between your app and the LLM. That means there's a structural set of things it can't see — not because they haven't gotten to them, but because the attack doesn't pass through the API it's reading.

Once the model says "use this tool," the action moves off the prompt layer. The agent's runtime opens a socket, touches a file, spawns a process. None of that shows up in the LLM API call. So things like:

• A sequence of legitimate-looking actions that, together, are an exfiltration. Each file read, each network call, is boring on its own. The signal is in the order and the combination. You can only see that if you're watching at the OS.
• MCP tool poisoning. The malicious instructions live in tool metadata, not in anything the user typed. The agent reads them and acts on them as if they came from the system prompt. A prompt filter watching user input doesn't get a look.
• Shadow agents. If a dev installed Claude Code on a Tuesday and nobody told security, Lakera isn't proxying its traffic. It can't monitor what it doesn't know exists.
• Proxy-vs-kernel divergence. The agent tells its tool layer "editing a config file," and actually writes to /etc/sudoers. That gap — between what the agent says it's doing and what the OS records it doing — is probably the single highest-signal thing you can watch for. And you need both observations, at once, to spot it.

This isn't a list of bugs. It's just the shape of a prompt-layer tool. A firewall isn't bad at detecting insider threats — it's the wrong instrument. Same idea.

What we're doing on the endpoint

We install an agent on the developer's laptop (macOS and Linux today). From there we can see every process, every file access, every network call that any AI agent makes. Claude Code, Cursor, Copilot, Windsurf, custom MCP stuff — if it runs on the machine, we see it. No per-tool configuration.

Then we do two things that matter:

We build a baseline. Per agent, per user, per session. The Claude Code session on a backend engineer's machine looks nothing like the Cursor session on a frontend dev's machine, and both look nothing like whatever CI is doing. We model each of those separately. When something drifts, we score it.

We cross-check the two layers. We read the agent's declared intent (the tool calls, the proxy traffic) and we read what the OS actually did. When they disagree, we flag. Most successful attacks against agents show up here first. More on how that works in Behavioral Security for AI Agents.

Oh, and we keep an immutable log. When someone asks "what did the agent do between 2:14 and 2:17," you don't hunt through chat transcripts. You get OS-level receipts with timestamps.

The table

I keep the table because people ask for it. Don't read too much into any single row.

| | Lakera | Quint | |---|---|---| | Where it sits | In front of the LLM API | On the endpoint | | What it reads | Prompt text in and out | Every syscall, every network call, every tool execution | | Main signal | Content analysis of prompts | Behavioral sequence scoring | | Prompt injection | Catches the attempt | Catches the consequences | | MCP tool poisoning | Blind (payload is in tool metadata) | Yes | | Shadow agents | Blind (not proxied) | Discovers them | | Audit trail | LLM API logs | OS action log with process context | | Deployment | SDK or proxy | Endpoint agent | | Requires code changes | Yes | No |

Which one do you actually want?

If you're building an LLM-powered product — a chatbot, a summarization feature, a customer-facing agent — and the thing you lose sleep over is "what if a user prompt-injects us into saying something awful," that's Lakera's home turf. Filter the inputs, filter the outputs, sleep better.

If you're a security team at a company where developers have started running AI coding agents, and the thing keeping you up is "what is that thing actually doing at 3 AM on my engineer's laptop," that's what we built. The threat model gets into why.

A lot of enterprises genuinely have both problems. The overlap between what the two products see is small, so running both isn't redundant — it's just two different cameras pointing at two different rooms.

FAQ

Does Quint replace Lakera?

No. Different layers. We don't do prompt filtering; they don't do OS-level behavioral monitoring. The honest framing is: pick based on your actual risk, not on "which one wins."

Does Lakera see what an MCP tool does?

Not really. Lakera sees the LLM's decision to call the tool — that decision shows up in the API response. It doesn't see what the tool actually did once the agent runtime executed it. The execution and all its side effects (files read, sockets opened, processes started) happen off the prompt path.

If a prompt injection succeeds and the agent starts exfiltrating, who catches it?

Depends on how the exfil happens. If the payload ends up in the model's output text, Lakera can catch it. If the agent starts opening network connections and writing to disk instead, we catch it. Both together is the best posture.

If you want to see what runtime behavioral monitoring looks like on your own fleet, book a demo.