Quint vs Lakera: When to Use Each for AI Agent Security
If you're Googling "Quint vs Lakera," you're probably evaluating AI security tools and wondering which one you need. The honest answer: they solve different problems, and one does not replace the other. Lakera sits in front of your LLM and inspects what goes in and what comes out. Quint sits on the endpoint and watches what the agent actually does after the LLM responds. One is an input/output filter. The other is a behavioral monitor. Both exist because AI agents introduced a new attack surface, and no single tool covers all of it.
What Lakera does well
Lakera deserves real credit for a few things.
Their prompt injection detection (Lakera Guard) is one of the more mature products in the category. Prompt injection is a hard problem, and they've invested serious research into it. Their classifier catches a meaningful percentage of injection attempts, including indirect injections embedded in documents and tool responses. That's not trivial.
Gandalf, their interactive prompt injection challenge, has been played by over a million people. It did more to raise awareness of prompt injection as an attack class than any whitepaper or conference talk. That kind of developer education has real value for the whole ecosystem.
Their red teaming product (Lakera Red) lets you probe your own LLM applications for vulnerabilities before shipping. Offensive testing before production is always a good idea.
And their research team publishes consistently. The work they've done on characterizing prompt injection variants and building taxonomies is referenced across the industry.
What Lakera is not designed to do
This is scope, not criticism. Lakera Guard inspects the text going into and coming out of an LLM API call. That's the layer it operates at. Which means there's a set of things it structurally cannot see:
What the agent does after the LLM call. When Claude Code reads a file, opens a network socket, spawns a subprocess, or writes to disk, those actions happen at the OS level. They don't pass through the LLM API. Lakera doesn't see them because they don't occur at the layer Lakera monitors.
Sequences of legitimate actions that add up to an attack. A file read is fine. A file read followed by a socket open to an external IP followed by a write to ~/.ssh/authorized_keys is an exfiltration in progress. But each individual action, examined in isolation at the prompt layer, looks clean. The signal is in the sequence, and sequence analysis requires maintaining state across actions at the OS level.
MCP tool poisoning. When a malicious MCP server embeds hidden instructions in a tool description, the agent executes those instructions as if they came from the system prompt. The poisoned instructions never appear as user input. They're in the tool metadata, which sits in the system trust tier. A prompt filter scanning user inputs and model outputs won't flag instructions the agent treats as its own. (We wrote about this class of attack in detail in MCP Tool Poisoning.)
Shadow agents and unmanaged AI tools. If a developer installs a new AI coding assistant and starts running it on their machine, Lakera has no way to know. It only sees traffic to LLM APIs it's been configured to proxy. Agents that bypass the proxy, or that the org doesn't know about, are invisible.
Proxy/kernel divergence. The agent says it's "editing a config file." The OS says it's writing to /etc/sudoers. When the intent layer and the action layer disagree, that gap is the most reliable signal you have. But you can only detect it if you're watching both layers simultaneously. Lakera watches one.
None of this is a flaw in Lakera. It's the boundary of what a prompt-layer tool can do. You wouldn't criticize a firewall for not detecting insider threats. Same idea.
What Quint does differently
Quint operates at the OS and network layer. We intercept every action the agent takes, not just its LLM API calls, but its file reads, shell commands, network connections, process spawns, and tool invocations.
Behavioral baselines. We build a per-agent, per-user, per-session model of what normal looks like. Claude Code on a backend engineer's laptop has a different baseline than Cursor on a frontend developer's machine. When an agent deviates from its baseline, we score the deviation in real time.
Proxy/kernel divergence detection. We watch both what the agent says it's doing (the proxy layer, which includes tool calls and LLM traffic) and what it actually does (the OS layer, which includes syscalls and network activity). When those two disagree, we flag it. This catches the class of attacks where every individual action looks legitimate but the sequence is a breach in progress. (More on this architecture in Behavioral Security for AI Agents.)
Immutable audit trail. Every action, every score, every deviation is recorded. When your security team or a regulator asks "what did the agent do between 2:14 and 2:17 PM on Tuesday," you can answer with OS-level receipts, not chat logs.
No code changes required. Quint deploys at the endpoint level. You don't need to modify your agents, your prompts, or your API calls. If the agent runs on the machine, we see it.
Comparison table
| Capability | Lakera | Quint | |---|---|---| | Operating layer | LLM API (prompt input/output) | OS + network + proxy | | Primary detection signal | Prompt content analysis | Behavioral sequence scoring | | Audit trail | LLM API call logs | Full OS-level action log | | Detects MCP tool poisoning | No (instructions sit in system tier) | Yes (observes actual tool execution) | | Detects shadow/unmanaged agents | No (only sees configured API traffic) | Yes (sees all agent processes on endpoint) | | Detects prompt injection | Yes (core capability) | Detects the effects of prompt injection | | Detects supply chain attacks on agents | Partial (if payload appears in prompt text) | Yes (observes anomalous behavior regardless of entry point) | | Deployment model | API proxy / SDK integration | Endpoint agent (macOS, Linux) | | Requires code changes | Yes (integrate SDK or route traffic through proxy) | No | | Works with any agent | Works with any LLM API it proxies | Works with any agent running on the endpoint |
When to use Lakera
You're building your own LLM application and you want input/output filtering at the API layer. You have a product that takes user prompts, sends them to a model, and returns responses. You want to catch prompt injection attempts before they reach the model. You want to filter sensitive data out of model outputs before they reach the user.
This is Lakera's sweet spot. They've built a focused product for this exact workflow, and it does the job.
If your primary concern is "what text is flowing through my LLM API calls," Lakera is a strong choice.
When to use Quint
You have developers running AI agents on their machines. Claude Code, Cursor, Copilot, Windsurf, or custom agents built on MCP. You need to know what those agents actually do: what files they read, what commands they run, what network connections they open. You need to detect when an agent's behavior deviates from its baseline, whether that deviation came from a prompt injection, a poisoned tool, or a hallucinated action. You need an audit trail that a security team or regulator can review.
If your primary concern is "what are these agents doing on our endpoints," that's what Quint is built for. (We break down the full threat model in The AI Agent Threat Model.)
When to use both
You're an enterprise running both custom LLM applications (chatbots, internal tools, customer-facing products) and a fleet of AI coding agents on developer machines. Your LLM apps need prompt filtering at the API layer. Your agent fleet needs behavioral monitoring at the OS layer. These are two different attack surfaces, and defense in depth means covering both.
Lakera filters what goes into and comes out of your models. Quint monitors what your agents do with those model responses. There's no overlap in what they see, which means there's no redundancy in running both.
FAQ
Does Quint replace Lakera?
No. They operate at different layers. Quint doesn't inspect prompt content for injection patterns. Lakera doesn't monitor OS-level agent behavior. If you need both capabilities, you need both tools.
Does Lakera see MCP tool calls?
Lakera sees LLM API traffic. MCP tool calls are executed by the agent locally (or via an MCP server), not through the LLM API. The model decides to call a tool, and the agent runtime executes it. The execution and its side effects (file writes, network calls, process spawns) happen outside the LLM API path. Lakera would see the model's decision to call the tool in the API response, but not what the tool actually did.
Which one catches prompt-injection-driven exfiltration?
Depends on where you're looking. Lakera can catch the injection attempt in the prompt or detect sensitive data in the model's output. But if the injection succeeds and the agent starts exfiltrating data through file operations or network calls (not through the model's text output), Lakera won't see the exfiltration itself. Quint will, because the file access and network connection happen at the OS level, where Quint is watching. The strongest posture is both: Lakera to catch the injection attempt, Quint to catch the exfiltration if the injection gets through.
Quint monitors what AI agents actually do. If you want to see it on your own fleet, request a demo.