AI application security
Assess your AI product or agent: where it can be prompt-injected, whether agent permissions are too broad, whether MCP connections are safe, and how to harden it.
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Your AI product is smart, but is it safe?
AI application security is a new field most people haven't figured out
You're building an AI product: you've wired in a large model, built an AI assistant, or set up an agent that does work on its own. It's powerful, but it brings a whole new set of security risks, and traditional security tools are mostly blind to them.
You may be facing these:
- Could your AI be "talked into" doing something it shouldn't with a single message (prompt injection)
- You gave your agent the ability to call tools and access data, but are its permissions too broad
- You connected some MCP tools so the agent can operate external systems, but are those connections safe
- The conversations between users and the AI hold sensitive data — could it leak out
- Could the AI's generated content be manipulated into outputting what it shouldn't
- AI security is new, you want to assess it, but you don't know how or what to look at
AI security risks are growing fast, with prompt injection, agent over-permissioning, and MCP flaws appearing constantly. The problem isn't that your AI isn't smart enough, it's that its smartness brings a new attack surface you haven't had time to defend.
As an AI agent itself, Mooth understands these risks best and is best suited to assess them for you.
Three steps to see your AI application's risks
Tell Mooth about your AI application
What the product is, which models it uses, what abilities and permissions the agent has, which tools and MCP it connects. Upload related docs or code too.
Mooth runs an AI-specific security assessment
For risks specific to AI systems, it checks where it can be prompt-injected, agent permission boundaries, MCP connection safety, data-leak paths, and whether the guardrails actually hold.
Get a targeted assessment and hardening steps
It tells you which AI-specific risks the system has, how they'd be exploited, and how to harden against them.
If you're building an AI product but have never assessed its security, run it free once and see the new risks your AI carries.
What Mooth focuses on
Targets the risks specific to AI systems, rather than applying a traditional security checklist:
| Risk area | Typical issue |
|---|---|
| Prompt injection | Instructions hidden in user input or external content, manipulating the AI into acting for an attacker |
| LLM application risk | Against OWASP LLM Top 10: data leaks, insecure output handling, and more |
| Agent permissions & escalation | The agent's callable tools and accessible data are too broad, so one injection can cause major damage |
| Tool & MCP connections | The safety and trustworthiness of the tools and MCP services it connects |
| AI supply chain | Whether the models and plugins it uses come from trustworthy sources |
| Guardrail effectiveness | Whether the content filtering and safety limits you built actually hold or can be bypassed |
Prompt injection and agent escalation are what Mooth especially watches: when an agent can access sensitive data, touch external content, and act outward all at once, a single prompt injection can cause real harm.
What an assessment looks like
Critical — agent permissions too broad, one prompt injection can cause real damage
Risk: your agent can read user data, call internal APIs, and even perform deletions, while the content it processes includes user input and scraped web pages.
How it would be exploited: an attacker need only hide an instruction in a web page or message to "command" your agent to delete data or send out information, and the agent thinks it's a normal task. This attack doesn't need to break your system, just a message.
How to harden: ① tighten agent permissions, grant by least-necessary; ② treat external content as untrusted input; ③ require human confirmation or extra checks for high-risk operations like deletion and sending.
Every item spells out the risk, how it would be exploited, and how to harden against it. For the new risks of AI, you get protection you can land directly.
Why Mooth especially understands AI security
It's an AI agent itself and understands these risks best. Prompt injection, agent escalation, MCP risk — Mooth didn't learn these from a textbook; they're what it knows most intimately in this field.
It looks at the AI-specific attack surface, not a traditional checklist. Traditional security tools watch the classic vulnerabilities and can't see prompt injection or agent permissions. Mooth assesses specifically for AI systems.
It tells you how the risk would really happen. Not an abstract "there's prompt-injection risk," but exactly how an attacker would use a message to manipulate your AI, so you know how to defend.
It keeps up with the latest in AI security. This field moves fast, with new attacks appearing constantly. Mooth's knowledge updates in real time, an expert in this field that doesn't go stale.
It's for you even without a security background. Even as an AI developer rather than a security expert, you get a professional assessment of AI risks and a hardening plan.
Is your information safe
You'll provide product and system information for this, so:
- It only analyzes what you provide and won't reach into unrelated systems.
- Nothing enters model training — your information is used only for this assessment or a context you authorize.
- Deletable and revocable — you can delete the conversation any time and revoke any data-source access.
Assess your AI application now
No need to become an AI security expert first, no fixed format to prepare. Tell Mooth about your AI product, and within minutes you get a professional assessment of AI-specific risks and hardening steps.
Better to see the new attack surface now than to discover it after your AI is manipulated with a single message.