
Postfleet
Inbound email for AI agents, screened for prompt injection
Details
- Follow on
- @postfleetai
- Categories
- AIDeveloper ToolsSecurity
- Target Audience
- AI DevelopersAI EngineersSoftware Developers
- Pricing
- Freemium from $20
- Platforms
- API
- Featured in
- Best SecurityBest Security Scanning Tools
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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)
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
@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.
Smart approach to securing agent inputs. The multi-layer screening process eliminates silent vulnerabilities that other solutions miss. Impressed with the execution focus.
@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.
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.