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Anti-spam & phishing

Stop spam before it reaches the mailbox

Stalwart's spam and phishing filter is part of the server, not an extra daemon to install and tune. It runs at SMTP time, so unwanted mail is stopped before it reaches a mailbox; it learns from user feedback; and it combines a statistical classifier, public blocklists, phishing heuristics, sender reputation and AI analysis into a single score.

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  • Self-learning
  • AI-powered
admin.stalw.art / Dashboard / Security
Stalwart security dashboard with fail-to-ban categories (auth, blocked, brute, loiter) and inbound report warnings
Statistical classifier

Spam filtering that learns from your users.

The classifier learns continuously from what your users do. Every time a recipient marks a message as spam, marks it as ham, or moves it out of the junk folder, the filter updates the score for the patterns in that message. Senders already in a recipient's address book are pre-classified as known correspondents, which removes a common class of false positive without operator intervention.

Read the classifier docs

  • FTRL-Proximal linear classifier.
  • Standard feature hashing or cuckoo feature hashing for large deployments.
  • Continuous online training from user feedback.
  • Address-book signal: senders in a recipient's contacts are weighted toward ham.
  • Encryption-at-rest privacy setting for training data.
LLM-driven analysis

AI for the messages that need a second look.

Some phishing and social-engineering messages slip through any classifier that only counts patterns. The AI classifier sends only the messages the rest of the filter is unsure about to a language model, which keeps cost and latency down. The model receives facts Stalwart has already verified, not raw headers a sender could forge, and returns a structured verdict.

Read the AI classifier docsRead the LLM Sieve docs

  • Only inconclusive messages reach the model.
  • Model tiers with escalation, plus a shadow mode for evaluating a model on real traffic.
  • Training of the statistical classifier from AI verdicts.
  • OpenAI, Azure, Anthropic, Google Gemini, Ollama or any OpenAI-compatible server (vLLM, llama.cpp).
  • Sieve functions for custom AI analysis.
DNSBL and Pyzor

Boosted spam detection with collaborative filtering.

Public blocklists and collaborative filtering catch a large slice of automated spam before anything else has to look at the message. Stalwart queries DNS-based blocklists for the connecting IP, and for the domains and URLs lifted from the message body, so even subtle threats are detected. Pyzor adds collaborative digest-based filtering against a global network of contributors, so spam patterns identified anywhere quickly inform your filter.

Read DNSBL

  • IP, domain, URL and content-hash blocklists.
  • Pyzor digest-based collaborative filtering.
  • Tunable per-source thresholds and weights.
DMARC, SPF, DKIM, ARC

Stop forged senders before they reach the mailbox.

Most phishing forges the From address. Stalwart runs DMARC alignment, SPF policy evaluation, DKIM signature checks and ARC chain verification on every inbound message, and feeds the result into the same score as the rest of the filter, so a message pretending to come from your bank, your CEO or a customer rarely lands in an inbox.

Read DMARC analysis

admin.stalw.art / Reports / Inbox / DMARC
Stalwart inbound DMARC reports list with sender, subject and received timestamp columns
  • DMARC alignment checking and reporting.
  • SPF, DKIM, DKIM2 and ARC verification on inbound mail.
  • Tag-based scoring shared with the classifier and blocklists.
Phishing protection

Phishing detection, beyond keywords.

Phishing rarely relies on the words in the body; it relies on the structure around them. Stalwart spots the techniques attackers actually use, including visually-confusable Unicode characters in URLs, mismatches between the display name and the From address, and sender domains that imitate the recipient's own domain. Trusted-reply tracking recognises genuine reply threads to a message your user previously sent, so a real reply is not treated with suspicion.

  • Homograph URL detection.
  • Sender spoofing and lookalike-domain detection.
  • Trusted-reply tracking on outgoing messages.
Sender reputation

Reputation built from your own traffic.

A sender's reputation should reflect how they have behaved in your traffic, not just what a public list says. Stalwart tracks reputation across four axes at once: the source IP, the network (ASN) it belongs to, the sending domain, and the individual sender address. Each axis contributes a learned score that updates with every message processed, so spam scoring becomes more accurate the longer the deployment runs.

Read ASN and GeoIP

  • Per-IP, per-ASN, per-domain and per-email reputation scoring.
  • ASN and GeoIP enrichment for headers and reporting.
  • Greylisting for unknown senders.
  • Spam traps that auto-train the classifier on what they catch.
Auto-banning

Brute-force and probing attacks blocked automatically.

Stalwart watches for abusive behaviour and bans source IPs on its own, so an operator does not have to install and tune a separate fail2ban-style tool alongside the server. Four independent categories of abuse are tracked, with a configurable threshold for each. Authentication tracking keys on both the source and the login name, so a distributed brute-force attempt against a single account is caught even when the attacker rotates IPs. Every ban records its reason and expiry; manual entries from the web admin or CLI live in the same table.

Read auto-banning

  • Authentication failures.
  • SMTP relay and recipient-probing abuse.
  • Loitering connections.
  • Port and exploit-URL scanning.
Upstream filters

Bring your existing Rspamd, SpamAssassin or ClamAV.

If your team already runs a content-filtering stack and wants to keep it, the built-in filter sits next to it instead of replacing it. Milter handles Rspamd, SpamAssassin and ClamAV without changes; MTA Hooks expose the same lifecycle over HTTP and JSON for filters that prefer a web protocol.

  • Milter for Rspamd, SpamAssassin, ClamAV and any sendmail-compatible filter.
  • MTA Hooks for HTTP + JSON filters.
  • Per-stage invocation across every SMTP phase.