What causes gibberish custom tags being added to contacts?

Key findings

• Bot activity: The most common cause of gibberish custom tags is automated bot activity, often originating from compromised or poorly secured signup forms and APIs. These bots fill out fields with random strings of characters, sometimes including hex values or keyboard smashing patterns.
• Data corruption vs. malicious input: While data corruption or encoding issues can cause garbled text, gibberish with specific patterns (like mixed alphanumeric and special characters) is more indicative of deliberate, automated input rather than random system errors.
• Impact on deliverability: Junk data in contact lists can negatively affect email deliverability by increasing bounce rates, attracting spam trap hits, and leading to higher unsubscribe or spam complaint rates if messages are sent to these invalid entries.
• Source identification: Identifying the source of these bogus sign-ups or data entries is critical for mitigation. This often involves reviewing form submissions, API logs, and data import processes.

Key considerations

• Form validation: Implement robust form validation, including CAPTCHA or similar bot-prevention technologies, on all public-facing forms to prevent automated submissions. Mailchimp, for instance, employs tools like reCAPTCHA to combat spambots.
• Data attribution: Improve tracking of where and how contacts are added to your database. Better attribution can help pinpoint the specific source of the gibberish data.
• Regular cleaning: Regularly audit your contact lists for suspicious entries. Identifying and removing these can help prevent issues that lead to your emails going to spam.
• Input character sets: Ensure that your systems correctly handle and validate character sets, preventing odd symbols or abnormal characters from being inserted into fields intended for readable text.

Key opinions

• Bot-driven data: Many marketers quickly identify gibberish entries as the work of bots due to the non-human pattern of characters, often resembling keyboard smashing or hexadecimal values rather than accidental human input.
• Disruptive input: The nature of the gibberish, which can include special characters like ampersands and capitalized letters in unusual sequences, points away from simple human error or impatience.
• Lack of internal responsibility: A common observation is that no internal team member can account for these entries, reinforcing the idea of external, automated interference.

Key considerations

• Review data sources: It's essential to investigate all avenues through which contacts are added, including web forms, APIs, and manual imports, to identify potential vulnerabilities.
• Automated suppression: Marketers should consider implementing automated rules to suppress or quarantine contacts with patterns indicative of gibberish, protecting list hygiene and preventing spam trap engagement.
• User experience vs. security: There's a balance between making forms easy to use and securing them against bots. Solutions like addressing gibberish contact names often involve stricter validation.
• Proactive list management: Proactively managing lists to remove such anomalies is crucial. Understanding different types of spam traps helps in this process.

Key opinions

• Improve data attribution: Experts stress the importance of knowing exactly where and how all data is acquired. This allows for pinpointing unknown sources contributing gibberish.
• Automated suppression: Once a problematic source is identified, automatically suppressing or rejecting data from it until the root cause is resolved is a best practice.
• Proactive ownership: Taking ownership of data quality and integrity, even if initial systems were set up by others, is crucial for long-term deliverability and database health.
• Reputation risk: Allowing invalid data into contact lists poses a significant risk to domain reputation, potentially leading to blocklisting or increased spam folder placement.

Key considerations

• Regular audits: Conduct regular audits of new contact entries and existing data for anomalies. This helps catch bot activity quickly before it escalates.
• Source identification: Focus on improving the attribution of all data acquisition points. Knowing the source is the first step to mitigating the problem, which can prevent email addresses from ending up on a blacklist.
• Prevention tools: Deploy bot detection and prevention tools (e.g., reCAPTCHA, honeypots) on all data intake forms to filter out automated, gibberish submissions. Preventing email abuse starts at the entry point.
• API security: If data is added via APIs, ensure they are secured against unauthorized or malicious calls that could inject bad data into your system.

Key findings

• Character encoding: Incorrect or mismatched character encoding between systems can lead to text appearing as gibberish, though malicious input often creates patterns beyond simple encoding errors.
• Input validation: Documentation for web forms and APIs stresses the importance of strong input validation to reject data that does not conform to expected formats, preventing random or malformed entries.
• Bot mitigation: Security best practices in documentation often detail methods to combat automated bot submissions, such as CAPTCHA, honeypots, and rate limiting.
• Data integrity: Core principles emphasize maintaining data integrity by ensuring that all incoming data is clean, relevant, and properly formatted for its intended use.

Key considerations

• Content removal processes: Google's documentation on requesting content removal highlights the need for mechanisms to remove unwanted data, including gibberish, from public-facing indices or databases.
• Encoding consistency: Ensure consistent character encoding (e.g., UTF-8) across all data input, processing, and storage layers to prevent legitimate data from appearing as invalid characters.
• Automated text generation: Research into gibberish code to manipulate AI models demonstrates how crafted, non-human readable text can be generated and used for specific purposes, analogous to bot behavior.
• URL character handling: Similar issues can arise with random characters added to URLs in automated processes, underscoring the need for careful character and data handling across all systems.