Postfleet

Postfleet

Inbound email for AI agents, screened for prompt injection

scottfoster
@scottfoster
Published on Aug 4, 2026
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$600 MRR
11 PeerPush
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@postfleetai
Pricing
Freemium from $20
Platforms
API

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How AI and people discover Postfleet on PeerPush

Last AI readLast AI readHow long ago an AI crawler last read this listing.
24 hours ago
  • ChatGPT
  • Claude

6 AI crawlers since launch

Time since an AI crawler last read this listing. 6 AI crawlers read it since launch.
Live AI conversations all-timeLive AI fetchesReads that happened live while an AI assistant was answering a real user question, not routine background crawling.
19

Fetched by AI answering users

Fetched by ChatGPT and Claude while answering real user questions.

About Postfleet

Postfleet is inbound email infrastructure for AI agents, built on the assumption that any message can be an attack. Every message runs the same pipeline before your agent sees it: SPF/DKIM/DMARC checks, HTML-to-text cleaning, removal of hidden elements, HTML comments and zero-width characters, malware and spam screening, then a prompt-injection scan. What comes out is typed JSON matching a schema you define. It fails closed. Malware is quarantined, spam is suppressed, and a message flagged for injection risk never reaches extraction — your agent gets an explicit status (skipped_injection_risk) instead of quietly poisoned context. The pre-sanitization body is never sent to agent-facing tools. Connect over MCP with 14 tools (Claude, Claude Code, Cursor, Windsurf, Cline, VS Code) or use the REST API. Sends are idempotent — retry with the same client_id and the email goes out at most once — with optional human approval before anything irreversible leaves. We publish the red-team scoreboard, including what got through and one false flag we caught ourselves: 99.1% of 113 attacks caught, most written by independent models and a public benchmark rather than by us. Screened against known patterns, never claimed immune. Free: 100 messages, 3 mailboxes. Sanitization and injection screening are never metered.

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Comments (3)

omribenshoham
@omribenshoham

Email filtering for AI agents is a real problem that most overlook. The security-first approach here matters - catching prompt injections before they reach your agent prevents silent failures and keeps system behavior predictable. Good exec

scottfoster
@scottfoster

@omribenshoham Filtering undersells it - a filter is binary. This is a typed contract, and failure directions are asymmetric: a spam-scan error fails open, an injection-scan error fails closed. Extraction never runs.

galdayan1895
@galdayan1895

Smart approach to securing agent inputs. The multi-layer screening process eliminates silent vulnerabilities that other solutions miss. Impressed with the execution focus.

scottfoster
@scottfoster

@galdayan1895 Thanks! Small correction: we don't eliminate anything, 99.1% of 113 attacks caught last run, and we publish what got through. What we do kill failure: flagged mail returns skipped_injection_risk, not quiet poison.

scottfoster
@scottfoster

An inbox is the one input your agent can't vet. We screen every message for prompt injection before the model sees it, and publish the attacks that got past us. Live demo, no signup.