Why are automated scripts and crawlers opening my emails, and how can I identify and exclude them from tracking?
Michael Ko
Co-founder & CEO, Suped
Published 21 Apr 2025
Updated 21 May 2026
7 min read
Summarize with
Automated scripts and crawlers open emails because security systems fetch images, rewrite links, scan URLs, and inspect message content before or shortly after the recipient sees the message. In education, government, healthcare, and large B2B environments, this often comes from mail gateways, student safety tools, sandboxing systems, link scanners, image proxies, and cloud-hosted security services.
The user agents python-requests and AHC/2.1 are strong signs of non-human traffic. If those opens also come from AWS, Azure, Google Cloud, or other hosting networks, I treat them as security or automation events first, not subscriber engagement. The right fix is not to block those requests at the network layer. It is to identify them, label them, and exclude them from open and click reporting while still allowing the security system to inspect the email.
What is actually opening the email
Most email open tracking works by placing a tiny remote image in the email. When that image loads, the sender records an open. That sounds simple, but the modern inbox does not behave like a single human clicking one message in one mail client. Security layers sit between the sender and the recipient, and those layers fetch remote content for their own checks.
- Security scanning: A gateway fetches images and links so it can inspect the email before delivery or before the recipient clicks.
- Link rewriting: A filter replaces URLs with protected links, then checks the original destination in the background.
- Image proxying: A mailbox provider loads images through a proxy, which hides the recipient IP and can change open timing.
- Content safety: Schools and colleges inspect student email to enforce acceptable use, safety, and compliance policies.
- Automation tooling: Internal scripts, test harnesses, and crawlers fetch messages or landing pages using libraries rather than browsers.
Do not block these fetches just to protect reporting numbers. If a security tool cannot fetch the pixel, parse the body, or scan the destination, the email can be delayed, quarantined, or treated with more suspicion. Exclude the traffic from analytics instead.
The educational institute pattern is especially important. A school domain can route student mail through a safety product hosted on AWS. Your logs show an AWS IP and a script-like user agent, but the underlying cause is still the institution's filtering stack. That traffic is not the student reading the message.
How to identify automated opens
I start by treating each open as an event with evidence, not a truth label. A single signal can be wrong. Several signals together give you a reliable exclusion rule.
|
|
|
|---|---|---|
User agent | Script client | Library clients are rarely normal mail app opens. |
Host network | AWS | Cloud IPs often belong to hosted scanners. |
Timing | 0-30 sec | Fast opens after delivery often come from pre-delivery checks. |
Pattern | Many recipients | One IP opening many accounts points to automation. |
Link behavior | All links | Full-page crawling is a scanner pattern. |
Signals that separate human opens from scanner opens.
The strongest signs are script user agents, hosting provider ASN, very fast timing, repeated IPs across unrelated recipients, and open events that happen without any later human activity. I also watch for campaigns where opens spike but clicks, replies, conversions, and on-site behavior do not move with them.
Five signals used to classify an email open event.
The exclusion rules I would use
I would not exclude every AWS open forever with no review, but I would heavily down-rank or exclude cloud-hosted opens when they match scanner behavior. Some real people use VPNs or corporate systems that terminate in cloud networks, but normal subscribers do not usually open email through raw cloud compute with script user agents.
Poor filtering
- Network block: Blocking cloud networks from loading pixels can interfere with content checks.
- Single signal: Filtering by user agent alone misses scanners that spoof browsers.
- Hard deletion: Removing events destroys the audit trail needed to explain reporting changes.
Better filtering
- Label first: Store scanner, proxy, suspicious, and human labels beside the raw event.
- Score signals: Combine user agent, ASN, timing, volume, and click path before exclusion.
- Report both: Keep raw opens and filtered opens so teams can reconcile campaign data.
Example scanner classification logicjavascript
const scannerUserAgents = [ /python-requests/i, /AHC\/2\.1/i, /curl/i, /wget/i, /httpclient/i ]; function classifyOpen(event) { let score = 0; if (scannerUserAgents.some((rule) => rule.test(event.userAgent))) score += 4; if (event.asnType === "hosting") score += 3; if (event.secondsAfterDelivery <= 30) score += 2; if (event.sameIpRecipientCount >= 10) score += 2; if (event.clickedEveryTrackedLink === true) score += 3; if (score >= 6) return "exclude_from_engagement"; if (score >= 3) return "suspicious_review"; return "count_as_engagement"; }
This kind of scoring is easier to defend than a broad deny rule. If a third party questions invalid traffic, you can show the exact conditions used to classify an event. You can also tune the threshold without changing the raw event history.
For a one-off investigation, send a controlled message to a test mailbox and inspect the headers, authentication result, tracking pixel request, and link fetches with an email tester. That gives you a clean baseline before you compare campaign traffic from institutional domains.
Email tester
Send a real email to this address. Suped opens the report when the test is ready.
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How cloud IP ranges fit into the decision
Cloud IP ranges are useful for classification, but they are not perfect identity. AWS, Azure, Google Cloud, and similar networks host security gateways, crawlers, proxies, QA systems, and personal VPNs. Because of that, I use cloud ownership as a strong signal, not the only rule.
Example event fields to storejson
{ "event_type": "open", "recipient_domain": "school.example", "ip": "203.0.113.42", "asn": "AS16509", "asn_name": "Amazon.com, Inc.", "user_agent": "AHC/2.1", "seconds_after_delivery": 8, "scanner_score": 9, "engagement_label": "exclude_from_engagement" }
Keep the raw IP, resolved ASN, hosting provider flag, recipient domain, and normalized user agent. Then build reporting views that include or exclude event labels. Marketing reports can use filtered engagement, while deliverability and compliance reviews can inspect all events.
Suped's product is useful here when authentication and deliverability context matter around the same campaign. Suped brings DMARC, SPF, DKIM monitoring, blocklist monitoring, and deliverability checks into one place, so scanner-heavy campaigns can be reviewed beside domain health instead of in isolation.
If the campaign also has delivery complaints, authentication failures, or domain reputation questions, validate the sending domain with a domain health check and monitor policy results through DMARC monitoring. Bot opens are an analytics problem, but poor authentication can make filtering systems more aggressive.
Suped DMARC dashboard showing email volume, authentication health, and source breakdown
A practical workflow for excluding scanner traffic
The cleanest workflow is to preserve every event, tag the events that look automated, then exclude tagged events only in engagement metrics. This keeps security systems working and gives your team a repeatable explanation for why reported opens changed.
Open event classification
A typical scanner-heavy campaign separates raw opens into human, suspicious, and excluded events.
Human
Suspicious
Excluded
- Capture fields: Store IP, ASN, user agent, recipient domain, timestamp, campaign ID, message ID, and event type.
- Normalize clients: Map raw user agents into browser, mail client, proxy, script, and unknown groups.
- Score events: Assign points for script clients, hosting networks, fast timing, high fan-out, and all-link fetches.
- Apply labels: Use labels such as human, suspicious, scanner, proxy, and test traffic.
- Split reporting: Show raw opens for audit, filtered opens for engagement, and excluded opens for data quality review.
Flowchart for filtering automated email opens from reports.
What not to remove
Open tracking has always had measurement limits, and privacy protections have made it less reliable as a direct measure of human attention. Still, I do not throw away open data entirely. I use it as a directional signal after scanner filtering, then pair it with clicks, replies, conversions, unsubscribe rates, spam complaints, and authenticated delivery data.
Never exclude a subscriber from future campaigns only because their first open came through a scanner. Exclude the event from engagement metrics, not the person from your list. A real recipient can still read later, click later, or convert through a different device.
Also avoid using scanner opens as proof that your subject line worked. If a school filter opens every message in seconds, that is a delivery and safety workflow, not audience intent. For deeper click-specific handling, the same logic applies to bot user agents and artificial opens.
Views from the trenches
Best practices
Label cloud-hosted script opens before filtering, so reporting stays explainable later.
Keep raw events for audit work, then build filtered engagement reports for decisions.
Review institutional domains separately because school filters often scan at scale.
Common pitfalls
Blocking scanner IPs at the edge can cause filters to distrust or delay the email.
Treating every open as human engagement inflates reports and weakens attribution.
Relying only on user agent rules misses scanners that send normal browser strings.
Expert tips
Use ASN, timing, fan-out, and client type together instead of one brittle rule.
Track excluded traffic as its own metric so partners can see invalid activity.
Recheck rules after provider changes because security vendors alter fetch behavior.
Marketer from Email Geeks says script-like clients are usually automation used to complete system tasks, not ordinary inbox activity.
2021-06-30 - Email Geeks
Marketer from Email Geeks says AHC/2.1 traffic tied to AWS is a strong sign of a hosted safety filter rather than a student open.
2021-06-30 - Email Geeks
The answer in practice
Automated opens happen because security systems and scripts inspect email content. With python-requests, AHC/2.1, repeated AWS IPs, and education-domain recipients, the most likely explanation is a hosted security or student safety filter. Count those events as machine activity unless later signals prove human engagement.
The practical path is straightforward: keep raw logs, enrich each event with user agent and network ownership, score scanner signals, label events, and exclude high-confidence scanner activity from engagement reporting. Do not block the scanner from fetching content unless you are prepared for delivery and approval side effects.
When scanner traffic appears beside authentication or reputation problems, Suped's product helps connect the dots. Suped monitors DMARC, SPF, DKIM, blocklist (blacklist) status, and deliverability signals in one workflow, with automated issue detection and clear steps to fix. That keeps the analytics question separate from the domain health question while still letting teams investigate both.
