Lakera
Lakera might tell your agent "don't do that." Mailgator makes sure it can't.
Detection and enforcement are different guarantees — and for email access control, the difference matters more than you'd think.
| Mailgator | Lakera | |
|---|---|---|
| Security layer | Email protocol (IMAP/SMTP) | AI model / prompt |
| Enforcement | Hard block at the proxy — agent can't bypass | Detects and flags — can be fooled |
| Email-specific controls | Folder, sender, operation-level rules | No email-specific product |
| Prompt injection protection | No (different layer) | Yes, core product |
| Scope | Email only | All AI tool interactions |
| Data location | Your infrastructure | Lakera cloud (API calls) |
| Pricing | From €3/mo | Enterprise pricing |
Lakera's Gandalf product and Guard API are specifically built to detect prompt injection attacks. If your AI agent processes untrusted input and you need to catch manipulation attempts before they reach the model, Lakera does this well. It's a real problem Mailgator doesn't address at all.
Lakera works across all AI tool interactions, not just email. If you need to secure an agent that uses email, databases, APIs, and file systems all at once, Lakera provides a single security layer at the model level — one integration covers everything.
Lakera detects bad intent and flags it. Mailgator blocks the operation at the protocol level. If your TOML rules say "deny access to HR folder," the agent physically cannot read those emails — there's nothing to detect or flag because the proxy won't pass the request through. Detection has false negatives. Protocol-level blocking doesn't.
Lakera is general-purpose AI security. It doesn't know what an IMAP folder is or what SMTP operations mean. Mailgator understands email at the protocol level: folders, senders, recipients, read vs. send vs. delete vs. move. The rules map directly to what your agent is actually trying to do.
Lakera's Guard runs as a cloud API — every prompt gets sent to their servers for evaluation. Mailgator runs entirely on your infrastructure. Your email data and agent interactions stay local. No latency added per operation, no external API dependency in your critical path.
Mailgator rules are a TOML file you can read in 30 seconds. You know exactly what's allowed and what's blocked — no ambiguity. Lakera's detection is model-based, which means some degree of opacity in how decisions are made. You can't easily predict or audit which edge cases it will miss.
Use Lakera if your primary concern is prompt injection — you need to protect the AI model itself from being manipulated by untrusted input. Lakera works at the input/output layer of the model, catching attacks before they reach your LLM. If your agent processes emails from unknown senders and those emails might contain adversarial instructions, Lakera addresses that threat.
Use Mailgator if your concern is "what can this agent actually do with email." The best setup is often both: Lakera to protect the model from manipulation, Mailgator to ensure the agent can only touch the emails it's supposed to. Belt and suspenders — Lakera secures the model, Mailgator secures the mailbox.
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